A new worm that spreads via USB sticks is infecting computers in Ukraine and beyond.
The group—known by many names, including Gamaredon, Primitive Bear, ACTINIUM, Armageddon, and Shuckworm—has been active since at least 2014 and has been attributed to Russia’s Federal Security Service by the Security Service of Ukraine. Most Kremlin-backed groups take pains to fly under the radar; Gamaredon doesn’t care to. Its espionage-motivated campaigns targeting large numbers of Ukrainian organizations are easy to detect and tie back to the Russian government. The campaigns typically revolve around malware that aims to obtain as much information from targets as possible.
One of those tools is a computer worm designed to spread from computer to computer through USB drives. Tracked by researchers from Check Point Research as LitterDrifter, the malware is written in the Visual Basic Scripting language. LitterDrifter serves two purposes: to promiscuously spread from USB drive to USB drive and to permanently infect the devices that connect to such drives with malware that permanently communicates with Gamaredon-operated command-and-control servers.
Online voting is insecure, period. This doesn’t stop organizations and governments from using it. (And for low-stakes elections, it’s probably fine.) Switzerland—not low stakes—uses online voting for national elections. Andrew Appel explains why it’s a bad idea:
Last year, I published a 5-part series about Switzerland’s e-voting system. Like any internet voting system, it has inherent security vulnerabilities: if there are malicious insiders, they can corrupt the vote count; and if thousands of voters’ computers are hacked by malware, the malware can change votes as they are transmitted. Switzerland “solves” the problem of malicious insiders in their printing office by officially declaring that they won’t consider that threat model in their cybersecurity assessment.
The Swiss Post e-voting system aims to protect your vote against vote manipulation and interference. The goal is to achieve this even if your own computer is infected by undetected malware that manipulates a user vote. This protection is implemented by special return codes (Prüfcode), printed on the sheet of paper you receive by physical mail. Your computer doesn’t know these codes, so even if it’s infected by malware, it can’t successfully cheat you as long as, you follow the protocol.
Unfortunately, the protocol isn’t explained to you on the piece of paper you get by mail. It’s only explained to you online, when you visit the e-voting website. And of course, that’s part of the problem! If your computer is infected by malware, then it can already present to you a bogus website that instructs you to follow a different protocol, one that is cheatable. To demonstrate this, I built a proof-of-concept demonstration.
Appel again:
Kuster’s fake protocol is not exactly what I imagined; it’s better. He explains it all in his blog post. Basically, in his malware-manipulated website, instead of displaying the verification codes for the voter to compare with what’s on the paper, the website asks the voter to enter the verification codes into a web form. Since the website doesn’t know what’s on the paper, that web-form entry is just for show. Of course, Kuster did not employ a botnet virus to distribute his malware to real voters! He keeps it contained on his own system and demonstrates it in a video.
Again, the solution is paper. (Here I am saying that in 2004.) And, no, blockchain does not help—it makes security worse.
On October 13, 2023, Cloudflare’s Cloudforce One Threat Operations Team became aware of a website hosting a Google Android Application (APK) impersonating the legitimate RedAlert – Rocket Alerts application (https://play.google.com/store/apps/details?id=com.red.alert&hl=en&pli=1). More than 5,000 rockets have been launched into Israel since the attacks from Hamas began on October 7th 2023. RedAlert – Rocket Alerts developed by Elad Nava allows individuals to receive timely and precise alerts about incoming airstrikes. Many people living in Israel rely on these alerts to seek safety – a service which has become increasingly important given the newest escalations in the region.
Applications alerting of incoming airstrikes have become targets as only days ago, Pro-Palestinian hacktivist group AnonGhost exploited a vulnerability in another application, “Red Alert: Israel” by Kobi Snir. (https://cybernews.com/cyber-war/israel-redalert-breached-anonghost-hamas/) Their exploit allowed them to intercept requests, expose servers and APIs, and send fake alerts to some app users, including a message that a “nuclear bomb is coming”. AnonGhost also claimed they attacked other rocket alert applications, including RedAlert by Elad Nava. As of October 11, 2023, the RedAlert app was reportedly functioning normally.
In the last two days, a new malicious website (hxxps://redalerts[.]me) has advertised the download of well-known open source application RedAlert by Elad Nava (https://github.com/eladnava/redalert-android). Domain impersonation continues to be a popular vector for attackers, as the legitimate website for the application (hxxps://redalert[.]me ) differs from the malicious website by only one letter. Further, threat actors continue to exploit open source code and deploy modified, malicious versions to unsuspecting users.
The malicious website hosted links to both the iOS and the Android version of the RedAlert app. But while the link to the Apple App Store referred to the legitimate version of the RedAlert app by Elad Nava, the link supposedly referring to the Android version hosted on the Play Store directly downloads a malicious APK file. This attack demonstrates the danger of sideloading applications directly from the Internet as opposed to installing applications from the approved app store.
The malicious RedAlert version imitates the legitimate rocket alert application but simultaneously collects sensitive user data. Additional permissions requested by the malicious app include access to contacts, call logs, SMS, account information, as well as an overview of all installed apps.
The website hosting the malicious file was created on October 12, 2023 and has since been taken offline. Only users who installed the Android version of the app from this specific website are impacted and urgently advised to delete the app. Users can determine if they installed the malicious version by reviewing the permissions granted to the RedAlert app. If users are unsure whether they installed the malicious version, they can delete the RedAlert applications and reinstall the legitimate version directly in the Play Store.
Screenshot of the attacker site https://redalerts[.]me
Malicious Android Package Kit (APK) Analysis
The malicious Android Package Kit (APK) file is installed by a user when they click the Google Play button on the fake RedAlert site. Once clicked, the user downloads the app directly from the fake site at hxxps://redalerts[.]me/app.apk. The SHA-256 hash of the APK is 5087a896360f5d99fbf4eb859c824d19eb6fa358387bf6c2c5e836f7927921c5.
Capabilities
A quick analysis of the AndroidManifest.xml file shows several differences compared to the legitimate, open source RedAlert application. Most notable are the additional permissions needed to collect information on the victim. The permissions added are listed below:
android.permission.GET_ACCOUNTS
android.permission.QUERY_ALL_PACKAGES
android.permission.READ_CALL_LOG
android.permission.READ_CONTACTS
android.permission.READ_PHONE_NUMBERS
android.permission.READ_PHONE_STATE
android.permission.READ_PRIVILEGED_PHONE_STATE
android.permission.READ_SMS
The application is designed to look and act like RedAlert. However, upon opening the app, a malicious service is started in the background. The startService() call is the only change to the onCreate() method, and this begins the sequence of malicious activity, which the actor has placed in a package called com.company.allinclusive.AI
The attacker starts their malicious code within the legitimate RedAlert code com.red.alert.activities: Main.java
The service is run to gather data from victims’ phones and upload it to the actor’s secure server. The data is extensive and includes:
SIM information, including IMEI and IMSI numbers, network type, country, voicemail number, PIN status, and more
Full Contact list
All SMS messages, including content and metadata for all statuses (e.g. received, outgoing, sent, etc.)
A list of accounts associated with the device
All phone calls and conversation details for including incoming, outgoing, missed, rejected, and blocked calls
Logged-in email and app accounts
List of installed applications
The actor’s code for gathering this information is illustrated below.
com.company.allinclusive.AI: AIMain.java contains the data the attacker will capture form the target
Stolen data is uploaded to an HTTP server at a hardcoded IP address. The actor has a Tools class which details the IP address where the data is to be uploaded:
com.company.allinclusive.AI: Tools.java stores the attackers command and control for the malware
Although HTTP and port 80 are specified, the actor appears to have the ability to use HTTPS and port 443 if a certificate is found bundled within the application package:
com.company.allinclusive.AI: UploadFileAsync.java
Data is uploaded through a Connector class, written by the actor. The Connector is responsible for encrypting the stolen data and uploading it to the HTTP server. In this sample, files are encrypted with AES in CBC mode with PKCS5 Padding. The keys are randomly generated and appended to the packaged data, however the keys are encrypted with RSA using a public key bundled in the malicious app. Because of this, anybody who is able to intercept the stolen data will be unable to decrypt it without the actor’s private key.
The encrypted files have names that look like <ID>_<DATE>.final, which contain:
<ID>_<DATE>.enc (encrypted data)
<ID>_<DATE>.param (AES encryption parameters, e.g. key and IV)
<ID>_<DATE>.eparam (RSA parameters, e.g. public key)
Anti-Analysis Runtime Capabilities
To avoid detection the actor included anti-analysis capabilities which can run at the time the app is started. The methods for anti-analysis that the attacker has included were anti-debugging, anti-emulation, and anti-test operations
Anti-Debugging
The application makes a simple call using the builtin android.os.Debug package to see if the application is being debugged.
The application attempts to locate certain files and identifiers to determine whether it is being run in an emulated environment. A snippet of these indicators are shown below:
com.company.allinclusive.AI.anti.emulator: FindEmulator.java checks for common emulators
Anti-Test
The application has utilities to identify whether a test user (“monkey”) is using the application:
These methodologies are all rudimentary checks for whether the application is under runtime analysis. It does not, however, protect the malicious code against static analysis.
How To Detect This Malware On Your Device
If you have installed RedAlert on your device, the extraneous permissions added by the actor can be used to determine whether you have been compromised. The following permissions appearing on the RedAlert app (whether or not enabled) would indicate compromise:
Call Logs
Contacts
Phone
SMS
How To Protect Yourself
You can avoid attacks like this by following the guidance below:
Keep your mobile device up to date on the latest software version at all times
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Kaspersky is reporting a zero-click iOS exploit in the wild:
Mobile device backups contain a partial copy of the filesystem, including some of the user data and service databases. The timestamps of the files, folders and the database records allow to roughly reconstruct the events happening to the device. The mvt-ios utility produces a sorted timeline of events into a file called “timeline.csv,” similar to a super-timeline used by conventional digital forensic tools.
Using this timeline, we were able to identify specific artifacts that indicate the compromise. This allowed to move the research forward, and to reconstruct the general infection sequence:
The target iOS device receives a message via the iMessage service, with an attachment containing an exploit.
Without any user interaction, the message triggers a vulnerability that leads to code execution.
The code within the exploit downloads several subsequent stages from the C&C server, that include additional exploits for privilege escalation.
After successful exploitation, a final payload is downloaded from the C&C server, that is a fully-featured APT platform.
The initial message and the exploit in the attachment is deleted
The malicious toolset does not support persistence, most likely due to the limitations of the OS. The timelines of multiple devices indicate that they may be reinfected after rebooting. The oldest traces of infection that we discovered happened in 2019. As of the time of writing in June 2023, the attack is ongoing, and the most recent version of the devices successfully targeted is iOS 15.7.
Reuters is reporting that the FBI “had identified and disabled malware wielded by Russia’s FSB security service against an undisclosed number of American computers, a move they hoped would deal a death blow to one of Russia’s leading cyber spying programs.”
The headline says that the FBI “sabotaged” the malware, which seems to be wrong.
EDITED TO ADD: Maybe “sabotaged” is the right word. The FBI hacked the malware so that it disabled itself.
Despite the bravado of its developers, Snake is among the most sophisticated pieces of malware ever found, the FBI said. The modular design, custom encryption layers, and high-caliber quality of the code base have made it hard if not impossible for antivirus software to detect. As FBI agents continued to monitor Snake, however, they slowly uncovered some surprising weaknesses. For one, there was a critical cryptographic key with a prime length of just 128 bits, making it vulnerable to factoring attacks that expose the secret key. This weak key was used in Diffie-Hellman key exchanges that allowed each infected machine to have a unique key when communicating with another machine.
In the early stages of the war in Ukraine in 2022, PIPEDREAM, a known malware was quietly on the brink of wiping out a handful of critical U.S. electric and liquid natural gas sites. PIPEDREAM is an attack toolkit with unmatched and unprecedented capabilities developed for use against industrial control systems (ICSs).
The malware was built to manipulate the network communication protocols used by programmable logic controllers (PLCs) leveraged by two critical producers of PLCs for ICSs within the critical infrastructure sector, Schneider Electric and OMRON.
This is a technical post covering practical methodology to extract configuration data from recent Qakbot samples. In this blog, I will provide some background on Qakbot, then walk through decode themes in an easy to visualize manner. I will then share a Velociraptor artifact to detect and automate the decode process at scale.
Qak!
Qakbot or QBot, is a modular malware first observed in 2007 that has been historically known as a banking Trojan. Qbot is used to steal credentials, financial, or other endpoint data, and in recent years, regularly a loader for other malware leading to hands on keyboard ransomware.
Typical delivery includes malicious emails as a zipped attachment, LNK, Javascript, Documents, or an embedded executable. The example shown in this post was delivered by an email with an attached pdf file:
An example Qakbot infection chain
Qakbot has some notable defense evasion capabilities including:
Checking for Windows Defender sandbox and terminating on discovery.
Checking for the presence of running anti-virus or analysis tools, then modifying its later stage behavior for evasion.
Dynamic corruption of payload on startup and rewrite on system shutdown.
Due to the commodity nature of delivery, capabilities and end game, it is worth extracting configuration from observed samples to scope impact from a given campaign. Hunting enterprise wide and finding a previously missed machine or discovering an ineffective control can be the difference in preventing a domain wide ransomware event, or a similar really bad day.
Configuration
Qakbot has an RC4 encoded configuration, located inside two resources of the unpacked payload binary. The decryption process has not changed significantly in recent times, but for some minor key changes. It uses a SHA1 of a hard coded key that can typically be extracted as an encoded string in the .data section of the payload binary. This key often remains static across campaigns, which can speed up analysis with the maintainance of a recent key list.
Current samples undergo two rounds of RC4 decryption with validation built in. The validation bytes dropped from the data for the second round.
After the first round:
The first 20 bytes in hex is for validation and is compared with the SHA1 of the remaining decoded data
Bytes [20:40] is the key used for the second round of decoding
The Data to decode is byte [40:] onwards
The same validation process occurs for the second round decoded data: Verification = data[:20] , DecodedData = data[20:]
First round of Qakbot decode and verification
Campaign information is located inside the smaller resource where, after this decoding and verification process, data is clear text.
Decoded campaign information
The larger resource stores Command and Control configuration. This is typically stored in netaddress format with varying separators. A common technique for finding the correct method is searching for common ports and separator patterns in the decoded data.
Easy to spot C2 patterns: port 443
Encoded strings
Qakbot stores blobs of xor encoded strings inside the .data section of its payload binary. The current methodology is to extract blobs of key and data from the referenced key offset which similarly is reused across samples.
Current samples start at offset 0x50, with an xor key, followed by a separator of 0x0000 before encoded data. In recent samples I have observed more than one string blob and these have occurred in the same format after the separator.
Encoded strings .data
Next steps are splitting on separators, decode expected blob pairs and drop any non printable. Results are fairly obvious when decoding is successful as Qakbot produces clean strings. I typically have seen two well defined groups with strings aligning to Qakbot capabilities.
Decoded strings: RC4 key highlighted
Payload
Qakbot samples are typically packed and need execution or manual unpacking to retrieve the payload for analysis. Its very difficult to obtain this payload remotely at scale, in practice the easiest way is to execute the sample in a VM or sandbox that enables extracting the payload with correct PE offsets.
When executing locally Qakbot typically injects its payload into a Windows process, and can be detected with yara targeting the process for an unbacked section with PAGE_EXECUTE_READWRITE protections.
Below is an example of running PE-Sieve / Hollows Hunter tool from Hasherezade. This helpful tool enables detection of several types of process injection, and the dumping of injected sections with appropriately aligned headers. In this case, the injected process is wermgr.exe but it’s worth to note, depending on variant and process footprint, your injected process may vary.
Dumping Qakbot payload using pe-sieve
Doing it at scale
Now I have explained the decode process, time to enable both detection and decode automation in Velociraptor.
I have recently released Windows.Carving.Qakbot which leverages a PE dump capability in Velociraptor 0.6.8 to enable live memory analysis. The goal of the artifact was to automate my decoding workflow for a generic Qakbot parser and save time for a common analysis. I also wanted an easy to update parser to add additional keys or decode nuances when changes are discovered.
Windows.Carving.Qakbot: parameters
This artifact uses Yara to detect an injected Qakbot payload, then attempts to parse the payload configuration and strings. Some of the features in the artifact cover changes observed in the past in the decryption process to allow a simplified extraction workflow:
Automatic PE extraction and offset alignment for memory detections.
StringOffset – the offset of the string xor key and encoded strings is reused regularly.
PE resource type: the RC4 encoded configuration is typically inside 2 resources, I’ve observed BITMAP and RCDATA
Unescaped key string: this field is typically reused over samples.
Type of encoding: single or double, double being the more recent.
Hidden TargetBytes parameter to enable piping payload in for analysis.
Worker threads: for bulk analysis / research use cases.
Windows.Carving.Qakbot: live decode
Research
The Qakbot parser can also be leveraged for research and run bulk analysis. One caveat is the content requires payload files that have been dumped with offsets intact. This typically requires some post collection filtering or PE offset realignment but enables Velociraptor notebook to manipulate post processed data.
Some techniques I have used to bulk collect samples:
Sandbox with PE dumping features: api based collection
Virustotal search: crowdsourced_yara_rule:0083a00b09|win_qakbot_auto AND tag:pedll AND NOT tag:corrupt (note: this will collect some broken payloads)
Bulk collection: IPs seen across multiple campaign names and ports
Some findings from a small data set ~60 samples:
Named campaigns are typically short and not longer than a few samples over a few days.
IP addresses are regularly reused and shared across campaigns
Most prevalent campaigns are BB and obama prefixed
Minor campaigns observed: azd, tok and rds with only one or two observed payload samples each.
Strings analysis can also provide insights to sample behavior over time to assist analysis. A great example is the adding to process name list for anti-analysis checks.
Bulk collection: Strings highlighting anti-analysis check additions over time
Conclusion
During this post I have explained the Qakbot decoding process and introduced an exciting new feature in Velociraptor. PE dumping is a useful capability and enables advanced capability at enterprise scale, not even available in expensive paid tools. For widespread threats like Qakbot, this kind of content can significantly improve response for the blue team, or even provide insights into threats when analyzed in bulk. In the coming months the Velociraptor team will be publishing a series of similar blog posts, offering a sneak peek at some of the types of memory analysis enabled by Velociraptor and incorporated into our training courses.
I also would like to thank some of Rapid7’s great analysts – Jakob Denlinger and James Dunne for bouncing some ideas when writing this post.
The FBI is warning people against using public phone-charging stations, worrying that the combination power-data port can be used to inject malware onto the devices:
Avoid using free charging stations in airports, hotels, or shopping centers. Bad actors have figured out ways to use public USB ports to introduce malware and monitoring software onto devices that access these ports. Carry your own charger and USB cord and use an electrical outlet instead.
How much of a risk is this, really? I am unconvinced, although I do carry a USB condom for charging stations I find suspicious.
Car thieves are injecting malicious software into a car’s network through wires in the headlights (or taillights) that fool the car into believing that the electronic key is nearby.
Researchers at Russian cybersecurity firm Kaspersky today revealed that they identified a small number of cryptocurrency-focused firms as at least some of the victims of the 3CX software supply-chain attack that’s unfolded over the past week. Kaspersky declined to name any of those victim companies, but it notes that they’re based in “western Asia.”
Security firms CrowdStrike and SentinelOne last week pinned the operation on North Korean hackers, who compromised 3CX installer software that’s used by 600,000 organizations worldwide, according to the vendor. Despite the potentially massive breadth of that attack, which SentinelOne dubbed “Smooth Operator,” Kaspersky has now found that the hackers combed through the victims infected with its corrupted software to ultimately target fewer than 10 machines—at least as far as Kaspersky could observe so far—and that they seemed to be focusing on cryptocurrency firms with “surgical precision.”
The New York Times is reporting that a US citizen’s phone was hacked by the Predator spyware.
A U.S. and Greek national who worked on Meta’s security and trust team while based in Greece was placed under a yearlong wiretap by the Greek national intelligence service and hacked with a powerful cyberespionage tool, according to documents obtained by The New York Times and officials with knowledge of the case.
The disclosure is the first known case of an American citizen being targeted in a European Union country by the advanced snooping technology, the use of which has been the subject of a widening scandal in Greece. It demonstrates that the illicit use of spyware is spreading beyond use by authoritarian governments against opposition figures and journalists, and has begun to creep into European democracies, even ensnaring a foreign national working for a major global corporation.
The simultaneous tapping of the target’s phone by the national intelligence service and the way she was hacked indicate that the spy service and whoever implanted the spyware, known as Predator, were working hand in hand.
By now, the news about what happened at Silicon Valley Bank (SVB) leading up to its collapse and takeover by the US Federal Government is well known. The rapid speed with which the collapse took place was surprising to many and the impact on organizations, both large and small, is expected to last a while.
Unfortunately, where everyone sees a tragic situation, threat actors see opportunity. We have seen this time and again – in order to breach trust and trick unsuspecting victims, threat actors overwhelmingly use topical events as lures. These follow the news cycle or known high profile events (The Super Bowl, March Madness, Tax Day, Black Friday sales, COVID-19, and on and on), since there is a greater likelihood of users falling for messages referencing what’s top of mind at any given moment.
The SVB news cycle makes for a similarly compelling topical event that threat actors can take advantage of; and it’s crucial that organizations bolster their awareness campaigns and technical controls to help counter the eventual use of these tactics in upcoming attacks. It’s tragic that even as the FDIC is guaranteeing that SVB customers’ money is safe, bad actors are attempting to steal that very money!
Preemptive action
In anticipation of future phishing attacks taking advantage of the SVB brand, Cloudforce One (Cloudflare’s threat operations and research team) significantly increased our brand monitoring focused on SVB’s digital presence starting March 10, 2023 and launched several additional detection modules to spot SVB-themed phishing campaigns. All of our customers taking advantage of our various phishing protection services automatically get the benefit of these new models.
Here’s an actual example of a real campaign involving SVB that’s happening since the bank was taken over by the FDIC.
KYC phish – DocuSign-themed SVB campaign
A frequent tactic used by threat actors is to mimic ongoing KYC (Know Your Customer) efforts that banks routinely perform to validate details about their clients. This is intended to protect financial institutions against fraud, money laundering and financial crime, amongst other things.
On March 14, 2023, Cloudflare detected a large KYC phishing campaign leveraging the SVB brand in a DocuSign themed template. This campaign targeted Cloudflare and almost all industry verticals. Within the first few hours of the campaign, we detected 79 examples targeting different individuals in multiple organizations. Cloudflare is publishing one specific example of this campaign along with the tactics and observables seen to help customers be aware and vigilant of this activity.
Campaign Details
The phishing attack shown below targeted Matthew Prince, Founder & CEO of Cloudflare on March 14, 2023. It included HTML code that contains an initial link and a complex redirect chain that is four-deep. The chain begins when the user clicks the ‘Review Documents’ link. It takes the user to a trackable analytic link run by Sizmek by Amazon Advertising Server bs[.]serving-sys[.]com. The link then further redirects the user to a Google Firebase Application hosted on the domain na2signing[.]web[.]app. The na2signing[.]web[.]app HTML subsequently redirects the user to a WordPress site which is running yet another redirector at eaglelodgealaska[.]com. After this final redirect, the user is sent to an attacker-controlled docusigning[.]kirklandellis[.]net website.
Campaign Timeline
2023-03-14T12:05:28Z First Observed SVB DoucSign Campaign Launched
2023-03-14T15:25:26Z Last Observed SVB DoucSign Campaign Launched
A look at the HTML file Google Firebase application (na2signing[.]web[.]app)
The included HTML file in the attack sends the user to a WordPress instance that has recursive redirection capability. As of this writing, we are not sure if this specific WordPress installation has been compromised or a plugin was installed to open this redirect location.
<html dir="ltr" class="" lang="en"><head>
<title>Sign in to your account</title>
<script type="text/javascript">
window.onload = function() {
function Redirect (url){
window.location.href = url;
}
var urlParams = new URLSearchParams(window.location.href);
var e = window.location.href;
Redirect("https://eaglelodgealaska[.]com/wp-header.php?url="+e);
}
</script>
Indicators of Compromise
na2signing[.]web[.]app Malicious Google Cloudbase Application.
eaglelodgealaska[.]com Possibly compromised WordPress website or an open redirect.
*[.]kirklandellis[.]net Attacker Controlled Application running on at least docusigning[.]kirklandellis[.]net.
Recommendations
Cloudflare Email Security customers can determine if they have received this campaign in their dashboard with the following search terms:
Customers can also track IOCs related to this campaign through our Threat Indicators API. Any updated IOCs will be continually pushed to the relevant API endpoints.
Ensure that you have appropriate DMARC policy enforcement for inbound messages. Cloudflare recommends [p = quarantine] for any DMARC failures on incoming messages at a minimum. SVB’s DMARC records [v=DMARC1; p=reject; pct=100] explicitly state rejecting any messages that impersonate their brand and are not being sent from SVB’s list of designated and verified senders. Cloudflare Email Security customers will automatically get this enforcement based on SVB’s published DMARC records. For other domains, or to apply broader DMARC based policies on all inbound messages, Cloudflare recommends adhering to ‘Enhanced Sender Verification’ policies across all inbound emails within their Cloudflare Area 1 dashboard.
Cloudflare Gateway customers are automatically protected against these malicious URLs and domains. Customers can check their logs for these specific IOCs to determine if their organization had any traffic to these sites.
Work with your phishing awareness and training providers to deploy SVB-themed phishing simulations for your end users, if they haven’t done so already.
Encourage your end users to be vigilant about any ACH (Automated Clearing House) or SWIFT (Society for Worldwide Interbank Financial Telecommunication) related messages. ACH & SWIFT are systems which financial institutions use for electronic funds transfers between entities. Given its large scale prevalence, ACH & SWIFT phish are frequent tactics leveraged by threat actors to redirect payments to themselves. While we haven’t seen any large scale ACH campaigns utilizing the SVB brand over the past few days, it doesn’t mean they are not being planned or are imminent. Here are a few example subject lines to be aware of, that we have seen in similar payment fraud campaigns:
“We’ve changed our bank details”
“Updated Bank Account Information”
“YOUR URGENT ACTION IS NEEDED –
Important – Bank account details change”
“Important – Bank account details change”
“Financial Institution Change Notice”
Stay vigilant against look-alike or cousin domains that could pop up in your email and web traffic associated with SVB. Cloudflare customers have in-built new domain controls within their email & web traffic which would prevent anomalous activity coming from these new domains from getting through.
Ensure any public facing web applications are always patched to the latest versions and run a modern Web Application Firewall service in front of your applications. The campaign mentioned above took advantage of WordPress, which is frequently used by threat actors for their phishing sites. If you’re using the Cloudflare WAF, you can be automatically protected from third party CVEs before you even know about them. Having an effective WAF is critical to preventing threat actors from taking over your public Web presence and using it as part of a phishing campaign, SVB-themed or otherwise.
Staying ahead
Cloudforce One (Cloudflare’s threat operations team) proactively monitors emerging campaigns in their formative stages and publishes advisories and detection model updates to ensure our customers are protected. While this specific campaign is focused on SVB, the tactics seen are no different to other similar campaigns that our global network sees every day and automatically stops them before it impacts our customers.
Having a blend of strong technical controls across multiple communication channels along with a trained and vigilant workforce that is aware of the dangers posed by digital communications is crucial to stopping these attacks from going through.
Here’s a piece of Chinese malware that infects SonicWall security appliances and survives firmware updates.
On Thursday, security firm Mandiant published a report that said threat actors with a suspected nexus to China were engaged in a campaign to maintain long-term persistence by running malware on unpatched SonicWall SMA appliances. The campaign was notable for the ability of the malware to remain on the devices even after its firmware received new firmware.
“The attackers put significant effort into the stability and persistence of their tooling,” Mandiant researchers Daniel Lee, Stephen Eckels, and Ben Read wrote. “This allows their access to the network to persist through firmware updates and maintain a foothold on the network through the SonicWall Device.”
To achieve this persistence, the malware checks for available firmware upgrades every 10 seconds. When an update becomes available, the malware copies the archived file for backup, unzips it, mounts it, and then copies the entire package of malicious files to it. The malware also adds a backdoor root user to the mounted file. Then, the malware rezips the file so it’s ready for installation.
“The technique is not especially sophisticated, but it does show considerable effort on the part of the attacker to understand the appliance update cycle, then develop and test a method for persistence,” the researchers wrote.
Researchers have discovered malware that “can hijack a computer’s boot process even when Secure Boot and other advanced protections are enabled and running on fully updated versions of Windows.”
Dubbed BlackLotus, the malware is what’s known as a UEFI bootkit. These sophisticated pieces of malware target the UEFI—short for Unified Extensible Firmware Interface—the low-level and complex chain of firmware responsible for booting up virtually every modern computer. As the mechanism that bridges a PC’s device firmware with its operating system, the UEFI is an OS in its own right. It’s located in an SPI-connected flash storage chip soldered onto the computer motherboard, making it difficult to inspect or patch. Previously discovered bootkits such as CosmicStrand, MosaicRegressor, and MoonBounce work by targeting the UEFI firmware stored in the flash storage chip. Others, including BlackLotus, target the software stored in the EFI system partition.
Because the UEFI is the first thing to run when a computer is turned on, it influences the OS, security apps, and all other software that follows. These traits make the UEFI the perfect place to launch malware. When successful, UEFI bootkits disable OS security mechanisms and ensure that a computer remains infected with stealthy malware that runs at the kernel mode or user mode, even after the operating system is reinstalled or a hard drive is replaced.
The number of UEFI vulnerabilities discovered in recent years and the failures in patching them or revoking vulnerable binaries within a reasonable time window hasn’t gone unnoticed by threat actors. As a result, the first publicly known UEFI bootkit bypassing the essential platform security feature—UEFI Secure Boot—is now a reality. In this blogpost we present the first public analysis of this UEFI bootkit, which is capable of running on even fully-up-to-date Windows 11 systems with UEFI Secure Boot enabled. Functionality of the bootkit and its individual features leads us to believe that we are dealing with a bootkit known as BlackLotus, the UEFI bootkit being sold on hacking forums for $5,000 since at least October 2022.
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It’s capable of running on the latest, fully patched Windows 11 systems with UEFI Secure Boot enabled.
It exploits a more than one year old vulnerability (CVE-2022-21894) to bypass UEFI Secure Boot and set up persistence for the bootkit. This is the first publicly known, in-the-wild abuse of this vulnerability.
Although the vulnerability was fixed in Microsoft’s January 2022 update, its exploitation is still possible as the affected, validly signed binaries have still not been added to the UEFI revocation list. BlackLotus takes advantage of this, bringing its own copies of legitimate—but vulnerable—binaries to the system in order to exploit the vulnerability.
It’s capable of disabling OS security mechanisms such as BitLocker, HVCI, and Windows Defender.
Once installed, the bootkit’s main goal is to deploy a kernel driver (which, among other things, protects the bootkit from removal), and an HTTP downloader responsible for communication with the C&C and capable of loading additional user-mode or kernel-mode payloads.
Criminals using Google search ads to deliver malware isn’t new, but Ars Technica declared that the problem has become much worse recently.
The surge is coming from numerous malware families, including AuroraStealer, IcedID, Meta Stealer, RedLine Stealer, Vidar, Formbook, and XLoader. In the past, these families typically relied on phishing and malicious spam that attached Microsoft Word documents with booby-trapped macros. Over the past month, Google Ads has become the go-to place for criminals to spread their malicious wares that are disguised as legitimate downloads by impersonating brands such as Adobe Reader, Gimp, Microsoft Teams, OBS, Slack, Tor, and Thunderbird.
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It’s clear that despite all the progress Google has made filtering malicious sites out of returned ads and search results over the past couple decades, criminals have found ways to strike back. These criminals excel at finding the latest techniques to counter the filtering. As soon as Google devises a way to block them, the criminals figure out new ways to circumvent those protections.
I don’t know how much of a thing this will end up being, but we are seeing ChatGPT-written malware in the wild.
…within a few weeks of ChatGPT going live, participants in cybercrime forums—some with little or no coding experience—were using it to write software and emails that could be used for espionage, ransomware, malicious spam, and other malicious tasks.
“It’s still too early to decide whether or not ChatGPT capabilities will become the new favorite tool for participants in the Dark Web,” company researchers wrote. “However, the cybercriminal community has already shown significant interest and are jumping into this latest trend to generate malicious code.”
Last month, one forum participant posted what they claimed was the first script they had written and credited the AI chatbot with providing a “nice [helping] hand to finish the script with a nice scope.”
The Python code combined various cryptographic functions, including code signing, encryption, and decryption. One part of the script generated a key using elliptic curve cryptography and the curve ed25519 for signing files. Another part used a hard-coded password to encrypt system files using the Blowfish and Twofish algorithms. A third used RSA keys and digital signatures, message signing, and the blake2 hash function to compare various files.
ChatGPT-generated code isn’t that good, but it’s a start. And the technology will only get better. Where it matters here is that it gives less skilled hackers—script kiddies—new capabilities.
Let’s assume you manage a job advert site. On a daily basis job-seekers will be uploading their CVs, cover letters and other supplementary documents to your servers. What if someone tried to upload malware instead?
Today we’re making your security team job easier by providing a file content scanning engine integrated with our Web Application Firewall (WAF), so that malicious files being uploaded by end users get blocked before they reach application servers.
Enter WAF Content Scanning.
If you are an enterprise customer, reach out to your account team to get access.
Making content scanning easy
At Cloudflare, we pride ourselves on making our products very easy to use. WAF Content Scanning was built with that goal in mind. The main requirement to use the Cloudflare WAF is that application traffic is proxying via the Cloudflare network. Once that is done, turning on Content Scanning requires a single API call.
Once on, the WAF will automatically detect any content being uploaded, and when found, scan it and provide the results for you to use when writing WAF Custom Rules or reviewing security analytics dashboards.
The entire process runs inline with your HTTP traffic and requires no change to your application.
As of today, we scan files up to 1 MB. You can easily block files that exceed this size or perform other actions such as log the upload.
To block a malicious file, you could write a simple WAF Custom Rule like the following:
if: (cf.waf.content_scan.has_malicious_obj)
then: BLOCK
In the dashboard the rule would look like this:
Many other use cases can be achieved by leveraging the metadata exposed by the WAF Content Scanning engine. For example, let’s say you only wanted to allow PDF files to be uploaded on a given endpoint. You would achieve this by deploying the following WAF Custom Rule:
if: any(cf.waf.content_scan.obj_types[*] != "application/pdf") and http.request.uri.path eq "/upload"
then: BLOCK
This rule will, for any content file being uploaded to the /upload endpoint, block the HTTP request if at least one file is not a PDF.
More generally, let’s assume your application does not expect content to be uploaded at all. In this case, you can block any upload attempts with:
if: (cf.waf.content_scan.has_obj)
then: BLOCK
Another very common use case is supporting file upload endpoints that accept JSON content. In this instance files are normally embedded into a JSON payload after being base64-encoded. If your application has such an endpoint, you can provide additional metadata to the scanning engine to recognise the traffic by submitting a custom scan expression. Once submitted, files within JSON payloads will be parsed, decoded, and scanned automatically. In the event you want to issue a block action, you can use a JSON custom response type so that your web application front end can easily parse and display error messages:
The full list of fields exposed by the WAF Content Scanning engine can be found on our developer documentation.
The engine
Scanned content objects
A lot of time designing the system was spent defining what should be scanned. Defining this properly helps us ensure that we are not scanning unnecessary content reducing latency impact and CPU usage, and that there are no bypasses making the system complementary to existing WAF functionality.
The complexity stems from the fact that there is no clear definition of a “file” in HTTP terms. That’s why in this blog post and in the system design, we refer to “content object” instead.
At a high level, although this can loosely be defined as a “file”, not all “content objects” may end up being stored in the file system of an application server! Therefore, we need a definition that applies to HTTP. Additional complexity is given by the fact this is a security product, and attackers will always try to abuse HTTP to obfuscate/hide true intentions. So for example, although a Content-Type header may indicate that the request body is a jpeg image, it may actually be a pdf.
With the above in mind, a “content object” as of today, is any request payload that is detected by heuristics (so no referring to the Content-Type header) to be anything that is nottext/html, text/x-shellscript, application/json or text/xml. All other content types are considered a content object.
Detecting via heuristics the content type of an HTTP request body is not enough, as content objects might be found within portions of the HTTP body or encoded following certain rules, such as when using multipart/form-data, which is the most common encoding used when creating standard HTML file input forms.
So when certain payload formats are found, additional parsing applies. As of today the engine will automatically parse and perform content type heuristics on individual components of the payload, when the payload is either encoded using multipart/form-data or multipart/mixed or a JSON string that may have “content objects” embedded in base64 format as defined by the customer
In these cases, we don’t scan the entire payload as a single content object, but we parse it following the relevant standard and apply scanning, if necessary, to the individual portions of the payload. That allows us to support scanning of more than one content object per request, such as an HTML form that has multiple file inputs. We plan to add additional automatic detections in the future on complex payloads moving forward.
In the event we end up finding a malicious match, but we were not able to detect the content type correctly, we will default to reporting a content type of application/octet-stream in the Cloudflare logs/dashboards.
Finally, it is worth noting that we explicitly avoid scanning anything that is plain text (HTML, JSON, XML etc.) as finding attack vectors in these payloads is already covered by the WAF, API Gateway and other web application security solutions already present in Cloudflare’s portfolio.
Local scans
At Cloudflare, we try to leverage our horizontal architecture to build scalable software. This means the underlying scanner is deployed on every server that handles customer HTTP/S traffic. The diagram below describes the setup:
Having each server perform the scanning locally helps ensure latency impact is reduced to a minimum to applicable HTTP requests. The actual scanning engine is the same one used by the Cloudflare Web Gateway, our forward proxy solution that among many other things, helps keep end user devices safe by blocking attempts to download malware.
Consequently, the scanning capabilities provided match those exposed by the Web Gateway AV scanning. The main difference as of today, is the maximum file size currently limited at 1 MB versus 15 MB in Web Gateway. We are working on increasing this to match the Web Gateway in the coming months.
Separating detection from mitigation
A new approach that we are adopting within our application security portfolio is the separation of detection from mitigation. The WAF Content Scanning features follow this approach, as once turned on, it simply enhances all available data and fields with scan results. The benefits here are twofold.
First, this allows us to provide visibility into your application traffic, without you having to deploy any mitigation. This automatically opens up a great use case: discovery. For large enterprise applications security teams may not be aware of which paths or endpoints might be expecting file uploads from the Internet. Using our WAF Content Scanning feature in conjunction with our new Security Analytics they can now filter on request traffic that has a file content object (a file being uploaded) to observe top N paths and hostnames, exposing such endpoints.
Second, as mentioned in the prior section, exposing the intelligence provided by Cloudflare as fields that can be used in our WAF Custom Rules allows us to provide a very flexible platform. As a plus, you don’t need to learn how to use a new feature, as you are likely already familiar with our WAF Custom Rule builder.
This is not a novel idea, and our Bot Management solution was the first to trial it with great success. Any customer who uses Bot Management today gains access to a bot score field that indicates the likelihood of a request coming from automation or a human. Customers use this field to deploy rules that block bots.
To that point, let’s assume you run a job applications site, and you do not wish for bots and crawlers to automatically submit job applications. You can now block file uploads coming from bots!
if: (cf.bot_management.score lt 10 and cf.waf.content_scan.has_obj)
then: BLOCK
And that’s the power we wish to provide at your fingertips.
Next steps
Our WAF Content Scanning is a new feature, and we have several improvements planned, including increasing the max content size scanned, exposing the “rewrite” action, so you can send malicious files to a quarantine server, and exposing better analytics that allow you to explore the data more easily without deploying rules. Stay tuned!
Mandiant is reporting on a trojaned Windows installer that targets Ukrainian users. The installer was left on various torrent sites, presumably ensnaring people downloading pirated copies of the operating system:
Mandiant uncovered a socially engineered supply chain operation focused on Ukrainian government entities that leveraged trojanized ISO files masquerading as legitimate Windows 10 Operating System installers. The trojanized ISOs were hosted on Ukrainian- and Russian-language torrent file sharing sites. Upon installation of the compromised software, the malware gathers information on the compromised system and exfiltrates it. At a subset of victims, additional tools are deployed to enable further intelligence gathering. In some instances, we discovered additional payloads that were likely deployed following initial reconnaissance including the STOWAWAY, BEACON, and SPAREPART backdoors.
One obvious solution would be for Microsoft to give the Ukrainians Windows licenses, so they don’t have to get their software from sketchy torrent sites.
The collective thoughts of the interwebz
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