F-Droid 2.0: A new chapter for Android freedom

Post Syndicated from jzb original https://lwn.net/Articles/1096444/

The F-Droid project has announced
the release of F-Droid 2.0, which is a complete redesign of the official
app. Notable changes in the release include making it easier to discover and
install applications, more useful app categories, improved search, and
much more.

For more than a decade, F-Droid has helped people discover and install free
and open source Android apps. F-Droid 2.0 builds on that foundation with a
modern interface, better app discovery, improved search, and a simpler
experience that works well, whether you’re new to F-Droid or have been using it
for years.

This isn’t just a visual refresh. The user experience was redesigned to
integrate smoothly with current Android patterns, like Material Design, while
keeping familiar F-Droid interactions in place. Key components were reworked and
rewritten using Kotlin Compose, the standard toolkit these days, creating a
foundation that will help us deliver improvements more quickly in the years
ahead.

Research into file-notification attacks on Linux

Post Syndicated from jzb original https://lwn.net/Articles/1096431/

Sudheendra Raghav Neela, a member of a group of researchers from Graz University of Technology, has announced the
release of research into file-notification attacks that would allow spying on
user activity on Android, Linux, macOS, and Windows. The group has published a paper with
details on the research as well as a web site
with demonstrations of the vulnerabilities.

On Linux, an attacker can use inotifywatch to
monitor a directory to conduct an inter-keystroke timing attack—even if
they do not have read access to the files within a directory. The group also
discovered a method to conduct a UI-redress
attack (or “clickjacking” attack) on
KDE 5 and KDE 6 by monitoring /usr/bin/pkexec to detect when Polkit spawns an authentication
prompt. An attacker could draw a fake password window on top of the real window
to collect a user’s credentials.

Both of these flaws are still present today,
though the Linux kernel did partially mitigate the issue with a
fix that was included in the 5.10.248, 5.15.198, 6.1.160, 6.6.120, 6.12.65,
and 6.18.3 kernels shipped in January. See the web site for more information and
a mitigation to prevent password-prompt windows from losing focus.

How Delivery Hero rebuilt real-time ad measurement with Apache Flink

Post Syndicated from Kirill Tishenkov original https://aws.amazon.com/blogs/big-data/how-delivery-hero-rebuilt-real-time-ad-measurement-with-apache-flink/

This post is co-written with Kirill Tishenkov, Alexandru Pisarenco, Upendra Kambhampati, and Sabariesh Ganesan from Delivery Hero.

Real-time ad measurement is one of the harder streaming problems in advertising. Every impression and click has to be accurate enough to bill a vendor for, and fresh enough for the ad server to act on. In this post, we describe how Delivery Hero moved its ad measurement pipeline from hourly batch processing to real time on Amazon Managed Service for Apache Flink. Delivery Hero, based in Berlin, Germany, is one of the world’s leading local delivery platforms, operating across Asia, Europe, Latin America, the Middle East, and North Africa. Working with more than 1.5 million restaurant partners and local vendors in around 65 countries, Delivery Hero handles millions of orders for food, groceries, and everyday essentials daily.

At the center of Delivery Hero’s business sits an advertising platform that connects vendors and brands with millions of active consumers. The platform handles tens of thousands of messages per second and processes billions of ad events per day, supporting an advertising revenue stream that reached almost EUR 1.5 billion in 2025. Every impression served and every click recorded must satisfy two requirements at once. The data must be accurate enough to bill vendors fairly, and fresh enough for the ad server to act on in real time. Delivery Hero replaced its batch-oriented measurement system with a fully real-time pipeline built on Amazon Managed Service for Apache Flink. The new pipeline cut infrastructure costs by more than half and reached a level of data quality the previous system could not.

Challenges with the legacy system

The legacy ads measurement system consumed impression, click, and order events from message queues. It enriched them through synchronous API calls for campaign metadata and product lookups, then wrote hourly aggregated metrics to a reporting database. This design worked at a modest scale, but five structural problems emerged as traffic grew.

No event-time semantics, and slow processing. The pipeline bucketed events by the time it processed them rather than the time they occurred, because most events arrived without a usable event timestamp. Results were internally consistent, but they skewed whenever ingestion lagged or events arrived out of order. That widened the error bar on every time-sensitive metric, including return on ad spend (ROAS). The bigger cost was speed. Metrics were assembled in hourly batches, so the average gap between when an event occurred and when it was recorded was 61 minutes. The platform was reacting to clicks and impressions up to an hour after the fact, far too late for budget pacing or ad serving.

Synchronous enrichment capped how far the system could scale. Enrichment is the step that attaches business context to a raw ad event: which campaign it belongs to, which vendor owns it, and which product was advertised. In the legacy system, every event triggered a chain of blocking external API calls to fetch that context. During traffic spikes, such as a flash sale or a back-to-school surge, exhausted connection pools cascaded into billing, ad serving, and reporting simultaneously. There was no back-pressure mechanism and no way to scale enrichment independently of event ingestion.

The database behind the pipeline was built for a very different access pattern. The pipeline kept its working data in a NoSQL document database: deduplication keys, attribution history, and running totals. The platform inherited that database from its pre-streaming era, when ad measurement looked like document storage and retrieval. The workload then evolved into continuous deduplication, multi-day attribution lookups, and rolling aggregation. Every event ended up triggering a full document read and write against a database designed for occasional access, not per-event mutation. Read/write amplification stored far more data than the logic needed, every write triggered index updates and collection scans, and storage costs grew in lockstep with query latency. At peak load, this often tipped into production outages.

Reprocessing was a project, not a capability. Recovery from a bug, a traffic spike, or a corrupted upstream batch required different tooling for every consuming system. Billing replay was a hand-rolled combination of Google Cloud BigQuery tables, Pub/Sub topics, and custom CLI scripts. Reporting replay ran as a separate daily Airflow job with a one-hour-per-day cost and a six-month horizon. Campaigns and credits events had no replay path at all. Every recovery was a coordination exercise across teams. Every event type that could not be replayed was a class of problems that could only be patched manually after the fact.

Incomplete event context corrupted downstream data quality. Enrichment was synchronous and best-effort, so the pipeline still wrote through events that failed a lookup or arrived malformed, leaving their fields blank. The pipeline had no mechanism to recover the missing context later. Three gaps mattered most:

  • Missing session rate: the share of events that landed without a usable session ID, leaving the interaction unattached to the user browsing session it belonged to. At 30–40 percent, roughly a third of all events could not be tied back to a session, breaking any session-scoped analysis or feature.
  • Missing customer identifiers (IDs): the share of events with no customer ID, severing the link between an ad interaction and the customer who generated it and weakening attribution and personalization.
  • Missing impression timestamps: the share of impression events lacking a reliable event-time timestamp (the same root cause as the processing-time fallback described earlier). At 91 percent, most impressions had no trustworthy event time, forcing the processing-time approximation and widening the error bar on every time-based metric.

These omissions propagated silently into the reporting metrics and into the session-scoped features consumed by machine learning (ML) models for campaign ranking, conversion-rate estimation, and anomaly detection.

The team set three non-negotiable requirements. First, fault-tolerant data processing, to eliminate data loss. Second, stateful stream processing that could hold multiple days of interaction history in low-cost, low-latency storage. Third, fully managed infrastructure, so engineers could focus on application logic rather than cluster operations.

The team selected Apache Flink because it satisfies all three requirements natively, without bolting on external systems. Its event-time watermark model helps place out-of-order events in the correct time window even when they arrive late. Its RocksDB state backend holds large keyed state on disk without Java Virtual Machine (JVM) heap pressure.

The team chose Amazon Managed Service for Apache Flink over self-hosted Flink on Amazon Elastic Kubernetes Service (Amazon EKS) to eliminate the operational burden of managing JobManagers, TaskManagers, and checkpoint storage. Amazon Kinesis Data Streams serves as the upstream event bus, with two streams: one for user event actions (impressions and clicks) and one for orders. The team chose Kinesis Data Streams over Amazon Managed Streaming for Apache Kafka (Amazon MSK) for cost efficiency at this topology.

Amazon DynamoDB holds campaign and product reference data, queried through Flink’s Async I/O API to enrich events without blocking the processing pipeline. AWS Secrets Manager stores ad event decryption keys, retrieved once at job startup. Amazon Simple Storage Service (Amazon S3) stores granular event logs in Avro format and serves as the incremental checkpoint store for Flink state. Amazon EventBridge Pipes bridged Amazon Simple Queue Service (Amazon SQS) to Kinesis in the minimum viable product (MVP) phase without any custom code, cutting time-to-production by two weeks.

Solution architecture

The following diagram shows the end-to-end pipeline.

Architecture diagram. Two Amazon Simple Notification Service (Amazon SNS) topics receive user event actions and order events. Amazon SQS buffers them, and Amazon EventBridge Pipes or AWS Fargate forwards them into two Amazon Kinesis Data Streams. Amazon Managed Service for Apache Flink then decrypts, deduplicates, enriches from Amazon DynamoDB, attributes, and aggregates the events. It writes granular events and checkpoints to Amazon S3, aggregated metrics to the reporting database, and billing events to Apache Kafka topics consumed by the ad server and budget service.

Figure 1: End-to-end architecture of the real-time ad measurement pipeline

Two Amazon Simple Notification Service (Amazon SNS) topics ingest events: one receives user event actions (compressed, encrypted ad tokens containing campaign, vendor, and placement metadata), the other receives order events. Amazon SQS buffers both before Amazon EventBridge Pipes (MVP) or an AWS Fargate service (production) forwards them into Kinesis.

Amazon Managed Service for Apache Flink runs a five-stage Java pipeline:

  1. Decompress and decrypt. The pipeline decrypts the ad event token using keys from AWS Secrets Manager.
  2. Deduplicate. The pipeline keys events on a composite of entity, ad, event, and customer identifiers. Flink’s RocksDB state tracks seen events over a 30-hour window (approximately 20 GB of state), filtering duplicates while preserving them in Amazon S3 for audit.
  3. Enrich. Flink’s Async I/O API queries Amazon DynamoDB concurrently for campaign metadata and product master codes, populated continuously from upstream Kafka topics by an AWS Fargate consumer.
  4. Attribute. A multi-day keyed interval join matches user event actions to subsequent orders on entity, customer, vendor, and campaign dimensions (approximately 100 GB of state). This stage emits attributed orders to Amazon S3.
  5. Aggregate. The pipeline accumulates impression, click, order, revenue, and ad spend metrics in RocksDB state, then batch-upserts them to the reporting database every 5 minutes.

The pipeline emits billing events (cost per mille (CPM) impressions and valid cost per click (CPC) clicks) to Apache Kafka topics. The ad server and budget service consume those topics in real time. Flink checkpoints all state incrementally to Amazon S3, so the job restores from the last checkpoint after a failure. Kinesis Data Streams and the upstream sources deliver at-least-once, and the deduplication stage in step 2 drops any event replayed during recovery. Billing is therefore effectively exactly-once, even though the transport underneath it is at-least-once.

Results and impact

The redesigned architecture achieved quantifiable performance gains across data fidelity, processing throughput, and operational expenditure, while introducing capabilities that were not feasible under the legacy model.

Processing latency: From hourly windows to real time

The average gap between when an event was published and when it was recorded dropped from 61 minutes to 1.2 seconds. Budget pacing and aggregated metrics now reflect activity within seconds rather than the following hour. Downstream ad serving and budget pacing systems act on real-time signals instead of reconciling after the fact.

Cost efficiency

The migration reduced monthly operational costs by approximately 57 percent, which more than halves the annual run rate for the pipeline. The saving came alongside stronger reliability, not at its expense.

System reliability

Durable attribution window. The multi-day attribution window lives in RocksDB-backed keyed state, roughly 100 GB on local TaskManager disks, checkpointed incrementally to Amazon S3. Per-key lookups stay in the low-millisecond range regardless of state size, and a crash or shard rebalance restores state from the last checkpoint rather than triggering a reconciliation job.

Elasticity replacing fragility. Async I/O against DynamoDB removed the synchronous enrichment chain that previously gated every event. The pipeline sustains 20,000 messages per second at peak without back-pressure leaking into ad serving or billing, and enrichment scales independently of ingestion. Flash sales and seasonal surges no longer threaten upstream systems.

Replayable history. The pipeline persists every raw event to Amazon S3 in Avro format the moment it lands, and Kinesis Data Streams retains the source stream for up to 7 days. When a logic bug surfaces or a downstream contract changes, the team reprocesses the affected time range deterministically against the original inputs. There is no bespoke backfill job and no reconciliation against external systems. Past data is a first-class input, not a frozen artifact.

Data quality at the source

The following table compares the three data quality gaps before and after the migration.

Metric Before After
Missing session rate 30–40% 0%
Missing customer IDs 5% 0.8%
Missing impression timestamps 91% 0.2%

Downstream applications now receive fully enriched transactional and session context. Machine learning models use session-scoped features for campaign ranking, conversion-rate estimation, and anomaly detection. The pipeline now computes those features from a complete event stream, rather than one in which roughly a third of events were missing session context and 91 percent of impressions were missing a reliable timestamp.

What’s next

The pipeline described here is the first of several planned migrations to Amazon Managed Service for Apache Flink. The team is extending the same architecture to additional ad formats, and connecting real-time Flink aggregations directly to the ad serving layer for sub-second budget pacing. The real-time data layer built for measurement also serves as the foundation for AI-driven use cases. The team plans to explore live user interaction streams feeding personalization ranking models and grounded large language model (LLM) recommendations, which were impractical with batch-oriented infrastructure.

Conclusion

Delivery Hero’s migration to Amazon Managed Service for Apache Flink shows that effectively exactly-once billing, multi-day stateful attribution, and manageable operational complexity are not competing goals. The combination that made it work: Kinesis Data Streams for ingestion, DynamoDB for low-latency enrichment, Amazon S3 for event storage and checkpointing, and Amazon EventBridge Pipes for rapid MVP delivery. Together they produced a system that is more accurate, more resilient, and less expensive than the one it replaced. For advertising platforms where billing accuracy and attribution correctness are commercial imperatives, this architecture offers a replicable path from batch approximation to real-time measurement.

To get started with Apache Flink on AWS, see the Amazon Managed Service for Apache Flink Developer Guide.

Additional resources

About the authors

Kirill Tishenkov

Kirill Tishenkov

Kirill is a Senior Software Engineer at Delivery Hero specializing in distributed stream processing and large-scale state management.

Alexandru Pisarenco

Alexandru Pisarenco

Alexandru is a Senior Software Engineer at Delivery Hero focusing on real-time data pipelines, backfill strategies, and multi-market rollouts.

Upendra Kambhampati

Upendra Kambhampati

Upendra is an Engineering Manager at Delivery Hero leading the AdTech Data Engineering team.

Sabariesh Ganesan

Sabariesh Ganesan

Sabariesh is a Senior Engineering Manager at Delivery Hero responsible for the Vendor AdTech Data platform and Ads measurement domain.

Joseph Idicula Watasseril

Joseph Idicula Watasseril

Joseph (he/him) is a Senior Solutions Architect at AWS, based in Berlin. With over 15 years of experience in tech consulting and software development, Joseph works with Delivery Hero to apply cloud solutions to their business challenges.

Francisco Morillo

Francisco Morillo

Francisco is a Senior Streaming Solutions Architect at AWS, specializing in real-time analytics architectures. With over five years in the streaming data space, Francisco has worked as a data analyst for startups and as a big data engineer for consultancies, building streaming data pipelines. He has deep expertise in Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Amazon Managed Service for Apache Flink.

How Cloudflare addressed a cross-tenant data exposure vulnerability in Containers

Post Syndicated from Rushil Mehra original https://blog.cloudflare.com/containers-cross-tenant-vulnerability/

On September 4, 2026, Oren Yomtov, a security researcher from Accomplish, responsibly reported a vulnerability affecting Cloudflare Containers and Cloudflare Sandboxes (which is built on Containers), through Cloudflare’s bug bounty program. Cloudflare has fully remediated the vulnerability, and we have no evidence that customer data has been compromised. 

This post was prepared in collaboration with Oren Yomtov and the Accomplish security research team, whose detailed report and controlled testing helped us validate the issue and respond quickly.

Cloudflare Containers run workloads on multi-tenant infrastructure and automatically assign them to eligible servers; customers cannot select the underlying host. The researchers demonstrated that a customer with a Workers Paid account could recover residual disk blocks previously used by Containers on the same host. The technique could not target a particular customer, workload, host, or data, and residual data was not guaranteed to be present.

Cloudflare applied a fix across the Containers fleet, with no customer-side configuration changes required. Within the historical disk-I/O telemetry available to us, we identified no evidence of malicious exploitation. Activity we could attribute to the reported technique came from the researchers and Cloudflare engineers conducting authorized validation.

Here, we explain the underlying storage behavior, its potential impact, our investigation, and the actions we took in response.

How container storage allocation works 

Cloudflare Containers use Linux device mapper thin provisioning (dm-thin) to provide each container with a writable root disk. Each container lives inside a dedicated virtual machine powered by the Firecracker virtual machine monitor. Firecracker presents this disk to the virtual machine as /dev/vdc.

Thin provisioning allocates physical storage only when a virtual disk writes to a previously unmapped region. The affected storage pools used a 64 KiB thin-block size. When the thin volume backing a container's root disk was deleted, its physical blocks were returned to a pool that served workloads belonging to multiple customer accounts.

The affected pool configuration included the following option:

skip_block_zeroing

With this option configured, dm-thin skips zeroing newly allocated blocks before making them accessible. Consequently, when a previously-used 64 KiB block was reassigned, a full-block write replaced its previous contents, but a smaller write changed only the written portion. The remainder could retain data from the block’s previous owner.

How the exploit worked

Reading an unmapped region of a new thin disk did not reveal residual data. For an unmapped region of the thin device, dm-thin returned zeroes without allocating a physical block.

The proof of concept identified 64 KiB-aligned regions corresponding to free space in the guest’s ext4 filesystem and wrote one aligned 4 KiB block into each region.

When such a write reached an unmapped thin block, dm-thin allocated a physical 64 KiB block from the shared pool. The 4 KiB write replaced only that portion of the block, and because block zeroing was disabled, the remaining 60 KiB could retain data from a previous container.

A subsequent raw-device read could therefore observe bytes that the new container had never written.

The proof of concept performed the following steps:

  1. Create a container using a Workers Paid account.
  2. Open the writable root disk at /dev/vdc.
  3. Read the disk and record a baseline.
  4. Write one 4 KiB block into each selected 64 KiB region corresponding to ext4 free space.
  5. Read the resulting blocks again.
  6. Examine only the portions not overwritten by the new container.

The submission included counts, block offsets, sizes, checksum results, and truncated hash prefixes. Although the researchers recovered raw blocks to validate the issue, the materials provided to Cloudflare contained no third-party filenames, identifiers, credentials, hostnames, addresses, or recovered content values. As described below, the researchers have also confirmed that they securely deleted the recovered data.

How the vulnerability was validated 

The researchers used ext4 directory block checksums to distinguish blocks belonging to their own test filesystem created for the proof of concept from blocks originating from other filesystems.

When ext4 uses the metadata_csum feature, directory block checksums incorporate values associated with the filesystem and inode. 

Across six production placements, the researchers reported:

  • All 5,614 testable directory blocks.
  • Zero of those blocks were attributed to the researchers’ filesystem. 
  • 2,700 distinct foreign directory inodes identified through checksum analysis.

To validate the method, the researchers tested it against blocks they had deliberately created and deleted in the controlled test filesystem used for the proof of concept. The method correctly attributed all 162 blocks to that filesystem.

The researchers ultimately observed residual material on 18 of 24 placements and 20 of 22 underlying nodes across four continents. The recovered block types included directory structures, database pages, and structurally complete SQLite databases. The researchers reported using scripts that output only aggregate counts and format checks, not recovered file contents. The materials submitted to Cloudflare contained no recovered content values or third-party identifiers. The researchers subsequently confirmed that recovered data under their control remained confidential and was securely deleted following submission, consistent with Cloudflare’s HackerOne disclosure policy.

Impact 

The vulnerability would potentially have allowed for a customer with a Workers Paid account to recover residual data from storage blocks previously used by other customers’ Containers on the same underlying host.

A successful exploitation would have crossed the tenant-isolation boundary and could disclose filesystem metadata, directory structures, database pages, and application data.

However, an attacker could not select a particular victim or access an actively attached disk. Exposure depended on Cloudflare’s workload placement and which previously released blocks dm-thin reassigned. Moreover, the researchers did not demonstrate modification of another customer’s active data or impact to workload availability.

How we mitigated the vulnerability

Our first mitigation was to remove skip_block_zeroing from the dm-thin pool configuration across the fleet. This restored dm-thin’s default behavior of clearing newly allocated blocks before exposing them to a container. It stopped the reported technique, in which a small write triggered allocation and a larger read recovered residual data from the remainder of the block. The researchers independently confirmed that their proof of concept no longer worked after this change.

Zeroing new allocations did not sanitize blocks already mapped into existing thin devices. These mappings existed in running container disks and in each host’s cache of prepared dm-thin snapshots for OCI image layers. A new container could inherit mappings from a cached layer without allocating those blocks again, allowing residual bytes in unused regions, including ext4 free space, to remain readable through raw reads of /dev/vdc.

We therefore also retired all running container disks and removed cached image snapshots created before the mitigation. We drained hosts during off-peak hours, restarted the VMs on each host, and cleared each host's image cache so that disks and cached layers were recreated using zeroed allocations. We have completed this cleanup across the Containers fleet.

No evidence of exploitation

As part of our response, we investigated whether other workloads showed activity consistent with the reported exploitation technique. We reviewed retained historical disk-I/O telemetry from our container infrastructure, using the researchers’ proof of concept and our internal reproduction as reference activity.

The proof of concept produced a characteristic relationship between writes and reads. When a 4 KiB write reached a previously unmapped region, it could trigger allocation of a reused 64 KiB storage block. With zeroing disabled, the remaining 60 KiB could retain data from a previous container. Subsequent reads could therefore recover substantially more data than the new container had overwritten.

Using these characteristics, we developed detection signatures and applied them to the historical telemetry available to us. We identified activity attributable to the researchers and Cloudflare engineers conducting authorized validation, and did not identify additional activity consistent with the reported technique.

We saw no evidence that this specific attack vector was exploited by anyone else.  

Cloudflare customers are protected

As we noted above, Cloudflare has patched this vulnerability and remediation does not require any further action by Cloudflare customers. In addition, we found no evidence of any malicious actor abusing this vulnerability.

Moving quickly with transparency 

We thank Oren Yomtov and the Accomplish security research team for their thorough research, responsible disclosure, and collaboration on this post. We encourage the Cloudflare community to submit any identified vulnerabilities to help us continually improve the security posture of our products and platform.

We also recognize that the trust you place in us is paramount to the success of your infrastructure on Cloudflare. We take these vulnerabilities very seriously and will continue to do everything in our power to mitigate impact. We deeply appreciate your continued support and trust in our platform, and remain committed not only to prioritizing security in all we do, but also acting swiftly and transparently whenever an issue arises.

Timeline

  • September 4, 15:26 UTC: Oren Yomtov from Accomplish reported the issue through HackerOne.
  • September 4, 18:45 UTC: Cloudflare opened a security incident and confirmed the production setup that caused the flaw.
  • September 4, 21:27 UTC: Cloudflare merged the runtime fix and its reuse test.
  • September 4, 22:03 UTC: Cloudflare merged the changes for new and live pools.
  • September 4, 23:15 UTC: Cloudflare started rolling out the changes.
  • September 7, 06:13 UTC: Cloudflare completed rolling out the changes and began clearing old pool data.
  • September 14, 10:50 UTC: The researchers reported that their proof of concept had stopped working.
  • September 14, 12:52 UTC: Cloudflare awarded the researcher a bounty.
  • September 19, 15:03 UTC: Cloudflare completed cleanup of all pre-mitigation cached snapshots across the affected fleet.

[$] Listening to the radio with Rust

Post Syndicated from daroc original https://lwn.net/Articles/1095721/

Many of the transmissions sent over the radio spectrum can
be decoded with a relatively cheap hardware dongle. Thomas Eckert presented at

RustConf 2026 in Montreal about his hobby:
decoding radio transmissions with Rust.
In his presentation, he
covered all of the math necessary to get started with

software-defined radio,
and gave demonstrations of listening to AM and FM radio, as well as decoding
transmissions from
aircraft transponders. His slides and example code are

available on GitHub.

Security updates for Thursday

Post Syndicated from jzb original https://lwn.net/Articles/1096407/

Security updates have been issued by AlmaLinux (buildah, containernetworking-plugins, firefox, kernel, kernel-rt, openexr, perl-DBI, podman, postgresql, postgresql16, postgresql:15, runc, skopeo, and tar), Debian (libdatetime-timezone-perl, tzdata, xdg-dbus-proxy, and znc), Fedora (chromium, evolution, evolution-data-server, evolution-ews, kernel, libheif, mingw-pcre2, nginx-mod-modsecurity, unbound, and webkitgtk), Mageia (borgbackup, coreutils, firefox, nss, kbd, libnfs, libwebsockets, perl-URI, pipewire, and xdg-dbus-proxy), Oracle (apr-util, containernetworking-plugins, coreutils, curl, firefox, freerdp, gstreamer1-plugins-base, host-metering, libarchive, libtiff, libxml2, openexr, openssh, perl-DBI, podman, postgresql16, postgresql18-postgis, postgresql:15, rsyslog, runc, tar, and unbound), SUSE (apptainer, gimp, librepods, libX11-6, perl-Authen-SASL, podofo, python-WebOb, and python313-graphifyy), and Ubuntu (imagemagick, libgit2, moodle, network-manager, Open-iSNS, python-urllib3, sqlparse, and xdg-desktop-portal).

When Business Email Compromise Starts Rewriting Reality

Post Syndicated from Douglas McKee, Director, Vulnerability Intelligence original https://www.rapid7.com/blog/post/ve-business-email-compromise-rewriting-reality-zimbra-cve

Business Email Compromise (BEC) operates on a familiar playbook. Threat actors breach a mailbox, silently monitor operations, map approval chains, and ultimately exploit that access to divert funds or exfiltrate sensitive assets.

This dynamic is central to our analysis as we kick off a series around Rapid7’s collaborative research with Zimbra; upcoming installments will explore technical details and broader findings based within the Zimbra Collaboration Suite. Our investigation disrupted the traditional BEC model in unexpected ways. We uncovered over 50 vulnerabilities, and found that several allow attackers not just to observe environments, but to actively rewrite them by impersonating senders without credentials, controlling inbox visibility, and altering shared documents and calendars.

Business Email Compromise in action: Digital abuse of trust

None of this is theoretical for Zimbra. But don’t take my word for it, just ask Russia. CISA keeps putting Zimbra bugs into the Known Exploited Vulnerabilities catalog, and the last three years make the point on their own:

  • CVE-2024-45519, command injection in the postjournal service, unauthenticated command execution. Proofpoint saw attackers stuffing base64 payloads into CC fields on September 28, 2024. CISA added it to KEV on October 3.

  • CVE-2025-27915, stored XSS in the Classic Web Client, triggered by a crafted .ICS attachment. It is used as a zero-day against Brazilian military targets to steal mail and quietly set forwarding filters. It went into KEV in October, 2025.

  • CVE-2026-73570, unauthenticated command injection through SNMP notification handling. CISA added it on August 21 of this year and gave federal agencies three days. Shadowserver has been counting somewhere north of 260 compromised instances while hunting for exploitation artifacts.

Go back further and the pattern holds. Rapid7 tracked widespread exploitation of CVE-2022-27925 and CVE-2022-37042 in 2022, a path traversal chained with an authentication bypass that let attackers drop a JSP shell on a Zimbra server without credentials. Google’s Threat Analysis Group later documented four separate threat groups working the same zero-day known as CVE-2023-37580. Each of these groups went after email, credentials, and authentication tokens. Attackers figured out a long time ago that the system sitting in the middle of everyone’s communication is worth the effort. So when you find a set of bugs that let you write to that system instead of only reading from it, data theft stops being the interesting part.

Send an email as your CFO without ever touching their password, and you have the front half of a very convincing BEC. Keep control of the mailbox afterward and you have the back half, too. Here, the attacker has a strategic choice. They can delete the sent message to hide their tracks, effectively wiping the trail of the fraud OR they can choose to leave the message in the Sent Items folder. By doing so, they ensure the CFO sees ‘evidence’ of the email they supposedly sent, creating a gaslighting scenario where the victim is left questioning their own actions. Whether the attacker cleans up or leaves the trail, they are shaping the organization’s perception of reality. In the ensuing investigation, where Finance sees a sent request and the CFO sees no such activity, the organization is trapped in a conflict of evidence. At that point, BEC looks less like traditional fraud and more like a psychological operation.

Documents make it worse, as Zimbra is not just a mail server. The collaboration side holds the files employees actually use to make decisions. An attacker who can plant a fake HR memo or financial summary in an executive’s enterprise drive, and make it look like it came from a peer they trust, is starting from a much better position than someone attaching a PDF to a cold email.

Say a document shows up from HR about a confidential restructuring, and a few days later an email from a trusted executive references it. Neither piece has to carry the whole deception, as each one props up the other.

Calendar warfare and manufactured enterprise reality

Then there is the thing I have started calling ‘calendar warfare.’ Meetings can be modified or deleted without generating the notification trail users expect to see. RSVP status can also be flipped. Maybe a key executive is changed from Accepted to Declined and leadership might reschedule, or move ahead without them, or read the whole thing as a deliberate opt-out.

It works in the other direction too. An “Emergency Board Meeting” lands on an executive’s calendar with a believable organizer, a popup reminder, and a malicious Zoom link. When the reminder fires, the victim is not sizing up a suspicious email that arrived thirty seconds ago. They are joining a meeting that has been sitting in their calendar for two days. And the calendar is not some exotic attack surface nobody has thought of. If we look back at CVE-2025-27915, the delivery vehicle was a calendar invite.

Stack all of it together now – a financial document appears, a trusted executive emails about it, then a mandatory meeting shows up to discuss it. And the attacker still has the ability to clean up some of what gets left behind. Every artifact the victim checks lives inside a system they have no reason to question, and all of them tell the same fabricated story.

I keep coming back to the phrase ‘manufactured enterprise reality‘. I have touched on the idea in The Monday Brief, that attackers get to borrow whatever trust an organization has already extended to its own tooling. Zimbra makes it concrete. The platform supplies the credibility, so the attacker does not have to build any.

Collaboration suites quietly became systems of record. Email is the record of who said what. Calendars are the record of who agreed to be where. Classic BEC abuses the trust between two people. The scenario we’ve discussed here abuses the machinery those people use to decide who to trust in the first place. Once employees are making real business decisions off fabricated context, stealing data is the least of your problems.

ICYMI: August 2026 @AWS Security

Post Syndicated from Rodolfo Brenes original https://aws.amazon.com/blogs/security/icymi-august-2026-aws-security/

Read all about the latest AWS security features, compliance updates, and hands-on resources in our monthly digest posts. You’ll find expert blog posts, new service capabilities, code samples, and workshops.

AWS Security Blog posts

August brought 20 AWS Security Blog posts organized across seven categories. Identity and access management led the month with five posts covering self-service rate limits for Amazon Cognito, a decade of AWS Managed Microsoft AD, a redesigned sign-in experience, console Private Access for isolated VPCs, and automated IAM Identity Center governance. Data protection followed with four posts on AWS KMS data key caching, ACME protocol support in AWS Certificate Manager, Amazon S3 over-permissioned access remediation, and the upcoming deprecation of email-based domain validation. AI security continued to grow with four posts on custom authentication in Amazon Bedrock AgentCore Gateway, user authorization propagation in AI agents, and extending Bedrock Guardrails to tool interactions. Threat detection, governance and networking.

Identity

From 2 weeks to 2 minutes: Amazon Cognito launches provisioned limits for self-service rate limit management

Authors: Kiran Dongara, Howie Li | Published: August 5, 2026

Learn to use Amazon Cognito provisioned limits for on-demand authentication rate limit adjustments, replacing the previous 10–14 day support ticket process with self-service capacity scaling in minutes.

A decade of enterprise identity in the cloud with AWS Managed Microsoft AD

Authors: Vladimir Provorov, Tekena Orugbani, Rodney Underkoffler | Published: August 7, 2026

AWS Managed Microsoft AD celebrates 10 years of fully managed Active Directory in the cloud, now offering Standard, Enterprise, and Hybrid editions with multi-Region replication and 20+ AWS service integrations.

Updates to your AWS sign-in experience

Authors: Vaibhav Chowla, Ella Segura | Published: August 17, 2026

AWS is gradually rolling out a redesigned sign-in page with a unified email entry point, social identity provider options, and an updated session selection experience for managing multiple active sessions.

Extend your data perimeter to the AWS Management Console with Private Access

Authors: Madhur Kulkarni, Abhijit Barde, Sujay Ghosh, Mateusz Jaworski | Published: August 28, 2026

AWS Management Console Private Access now supports VPCs without internet connectivity, routing all console traffic – authentication, static assets, and service API calls – through AWS PrivateLink endpoints to strengthen your data perimeter.

Automate IAM Identity Center governance with continuous discovery and reporting

Author: Jonathan Nguyen | Published: August 31, 2026

Learn to deploy automated discovery and reporting for AWS IAM Identity Center applications and assignments across your organization, with event-driven monitoring that validates naming conventions and enables near real-time enforcement of governance policies.

Data Protection

Caching KMS data keys in multi-thread environments: per-tenant encryption for event-driven systems at scale

Authors: Maria Gutovsky, Hemmy Yona | Published: August 6, 2026

Learn to solve the cache stampede problem in multi-tenant envelope encryption using the AWS-recommended hierarchical keyring pattern or a custom Caffeine-based caching approach to reduce AWS KMS costs.

Automate certificates with ACME support in AWS Certificate Manager

Authors: Anthony Harvey, Chandan Kundapur | Published: August 6, 2026

Learn to use ACME protocol support in AWS Certificate Manager to automate public certificate issuance and renewal using standard clients like Certbot and cert-manager, with enterprise controls for domain scoping and centralized visibility.

Securing your Amazon S3 buckets: identifying and remediating over-permissioned access

Authors: Hetal Kolekar, Fernando Chiera di Vasco Freitas, Manonmayi Vedam | Published: August 7, 2026

Learn to detect and fix over-permissioned Amazon S3 buckets across multi-account environments using AWS Lambda, AWS Config, and AWS Security Hub, with automation for continuous monitoring.

AWS Certificate Manager will discontinue email validation to prove domain validation for certificates

Authors: Adam Aboudi, Poojil Tripathi | Published: August 13, 2026

ACM will discontinue email-validated public certificates by September 30, 2027, aligning with CA/B Forum standards – learn the timeline and how to migrate to DNS validation in place.

AI Security

Implement custom authentication for tools integration using request Lambda interceptor in AgentCore Gateway

Authors: Nishant Mainro, Ram Ramani | Published: August 18, 2026

Learn to use a request Lambda interceptor in Amazon Bedrock AgentCore Gateway to bridge legacy authentication mechanisms like Basic Auth, isolating credentials from AI agents using AWS Secrets Manager.

Propagate user authorization context in AI agents with Amazon Bedrock AgentCore

Authors: Anshu Bathla, Prafful Gupta, Rohit Verma | Published: August 19, 2026

Learn to enforce least-privilege access in AI agents by propagating user identity through Amazon Bedrock AgentCore to Amazon DynamoDB, Knowledge Bases, and Salesforce, without embedding authorization logic in agent code.

Extend Amazon Bedrock Guardrails to tool interactions using the Strands Agents SDK

Authors: Stephan Traub | Published: August 27, 2026

Learn to extend Amazon Bedrock Guardrails beyond the model boundary to tool calls, external data, and MCP server interactions using three validation checkpoints built with Strands Agents SDK lifecycle hooks.

Threat detection and incident response

Security Hub Extended adds supply chain security as its tenth category

Author: Michael Fuller | Published: August 18, 2026

AWS Security Hub Extended now includes supply chain security with Chainguard and Socket as curated partners, helping you verify open source dependencies and block malicious packages through a single AWS billing relationship.

Detecting multi-stage attacks on AWS: a guide to cross-service signal correlation

Authors: Nisha Kashyap | Published: August 26, 2026

Learn to correlate signals across AWS CloudTrail, VPC Flow Logs, and Route 53 Resolver logs to detect multi-stage attacks by layering your business context, data classification, access norms, and change windows – on top of Amazon GuardDuty Extended Threat Detection.

AWS partners with Anthropic and OpenAI to bring AWS Continuum into developer workflows

Author: Chet Kapoor | Published: August 5, 2026

AWS Continuum for code vulnerabilities now integrates with Anthropic Claude Code, OpenAI Codex, and Kiro, enabling developers to discover, prioritize, validate, and remediate vulnerabilities within their coding environments.

We invited a direct competitor into Security Hub Extended. Here’s why.

Authors: Michael Fuller | Published: August 31, 2026

AWS Security Hub Extended now includes Upwind, a runtime-first cloud security company, offering eBPF-based workload protection with pay-as-you-go pricing through a single AWS bill, reinforcing customer choice even where capabilities overlap with AWS offerings.

Infrastructure security

AWS Network Firewall now supports rule hit count

Authors: Preetkumar Shah, Amit Gaur, Cheriyan Mundapuzha, Santosh Shanbhag, Srivalsan Mannoor Sudhagar | Published: August 20, 2026

AWS Network Firewall now tracks how often stateful rules match traffic, helping you identify unused rules, validate security controls for compliance, and accelerate incident response at no additional cost.

Governance and compliance

Landing Zone Accelerator independent assessment report for C5:2020 now available on AWS Artifact

Authors: Kevin Donohue, Michael Wahlers | Published: August 11, 2026

An independent assessment by Schellman evaluates how Landing Zone Accelerator on AWS aligns to C5:2020 requirements, implementing 325 security controls to accelerate your compliance journey.

Fast track ISM-ready cloud environments and IRAP assessments with Landing Zone Accelerator on AWS

Authors: Kevin Donohue, Dave Connell, Dan Friebe | Published: August 25, 2026

A new independent assessment by gwi.digital evaluates Landing Zone Accelerator against 1,081 ISM controls, achieving 91% coverage of addressable scope to help Australian customers accelerate IRAP assessment readiness.

How Moeve scales AWS governance with automated AWS Organization Service Control Policies

Authors: Gonzalo Guerrero, Jonatan De Martín, Rayco Martinez | Published: August 26, 2026

Learn how Moeve manages 150 SCPs across 300+ accounts in three AWS Organizations using a governance-as-code model; policies live in Git, deploy through GitHub Actions pipelines, and attach dynamically based on account metadata during onboarding, with Amazon EventBridge and AWS Lambda providing real-time observability of every organizational change.

August Security Bulletins

In August 2026, AWS published 23 security bulletins addressing vulnerabilities across open-source SDKs, MCP servers, developer tools, and the OpenSearch ecosystem. A dominant theme was the AI agent tool surface: prompt-injection consent bypasses in Strands Agents Tools enabled command and code execution, an insecure direct object reference exposed cross-tenant agent memory, and credential disclosure and authorization flaws affected the Amazon MQ, DocumentDB, and AWS Transform MCP servers and the Bedrock AgentCore harness. Remote code execution recurred throughout, from prototype pollution and Java deserialization in OpenSearch to path-traversal-to-root in amazon-ssm-agent, Zip Slip in awsdac, and an uncontrolled search path in the Kiro IDE and CLI on Windows.

Other notable issues include disabled SSH host key verification in the AWS CLI, memory-safety flaws in the AWS SDK for C++, privilege escalation in the FreeRTOS-Kernel, and memory-amplification denial of service in Amazon ion-java. OpenSearch accounted for a large share of the month, spanning authorization, input validation, SSRF, stored XSS, and denial of service, while Athena Federated Query connectors exposed Secrets Manager secrets. A common thread: insufficient authorization and input validation at the boundary between AI agents and the systems they reach. All patches are available, upgrade promptly. For more information, see AWS Security Bulletins.

AWS Samples

In August 2026, we published 17 new code samples organized into five categories: governance and compliance (7), AI security (3), data protection and privacy (3), identity and access management (2), and infrastructure security (2). This month’s collection reflects the rapid growth of agentic AI workloads: most samples focus on governing, auditing, and securing AI agents built on Amazon Bedrock AgentCore, from platform-level governance and telemetry to fraud investigation and biosecurity screening.

Governance and compliance

Agentic Governance Platform

Learn to deploy an AWS-native control plane for governing AI agents across your enterprise with centralized registry, Microsoft Entra ID single sign-on, Cedar tool policies, multi-vendor agent inventory, and Langfuse observability, all self-hosted on Amazon Bedrock AgentCore.

Enterprise Agentic AI Platform Accelerator

Learn to deploy a secure, governed foundation for production AI agents on Amazon Bedrock AgentCore with modular CDK stacks covering identity, gateway, memory, runtime, and observability; supporting Strands Agents, LangGraph, and Claude Agent SDK with opt-in security controls.

Video Compliance Agent

Learn to deploy an end-to-end pipeline that automatically verifies video content against broadcast compliance guidelines such as Ofcom; extracting frames, audio transcripts, and OCR text shot by shot, then using Amazon Bedrock to flag potential violations and produce a structured per-shot compliance report.

Intelligent Security for Healthcare APIs

Learn to add behavioral anomaly detection, automated data sensitivity classification, and HIPAA compliance reporting to your FHIR API using Amazon Bedrock; running asynchronously so clinical workflows are never blocked, with Amazon Comprehend Medical and Bedrock Guardrails anonymizing PHI throughout the monitoring path.

Governed Agentic Companion

Learn to deploy a governed, orchestrator-driven multi-agent builder companion on Amazon Bedrock AgentCore, reachable from Kiro, Claude Code, or any MCP client; the kit enforces 13 codified tenets through an always-on governance gate with no off switch, routing each request to a specialist while blocking deploys, secret leaks, and ungrounded answers by construction.

Audit the Agent

Learn to deploy a serverless daily executive audit pipeline for AWS AI agents (AWS DevOps Agent, AWS Security Agent) using AWS Step Functions and AWS Lambda; the report answers five questions: what the agent accessed, who authorized it, what it cost, its risk posture across five trust dimensions, and whether you should be concerned, all sourced deterministically from AWS CloudTrail, CUR, and IAM with AI-generated summaries bounded by layered guardrails.

AI Security

Telemetry Enablement for AgentCore CloudFormation

Learn to deploy a single AWS CloudFormation stack that enables Amazon CloudWatch logs and X-Ray traces for every Amazon Bedrock AgentCore resource type – Runtime, Gateway, Memory, Browser, CodeInterpreter, and WorkloadIdentity – using native and custom telemetry rules.

Bedrock Guardrails to OCSF on CloudWatch

Learn to transform Amazon Bedrock Guardrails intervention events into OCSF Detection Finding records and land them in the Amazon CloudWatch unified data store; enabling you to query guardrail violations alongside AWS CloudTrail, Amazon VPC Flow Logs, and other sources with Amazon Athena or CloudWatch Logs Insights.

Bedrock Readiness Agent

Learn to deploy a read-only assessment agent built with the Strands Agents SDK that evaluates your Amazon Bedrock environment across six dimensions: IAM governance, data retention, quota headroom, model selection fitness, cost projection, and operational observability; generating severity-rated findings with AWS CloudFormation and Terraform remediation templates you can apply directly.

Infrastructure security

DDoS Guardian

Learn to install an agent skill that reviews an AWS WAF web ACL as a system: evaluation order, rule interactions, and L7 DDoS posture; then delivers a severity-ranked HTML report with ready-to-apply remediation. Offline, read-only, no AWS resources modified.

Biosecurity Screening Policy on Amazon Bedrock AgentCore Gateway

Learn to use Policy in Amazon Bedrock AgentCore to deterministically screen AI agent tool requests for biosecurity risks, combining Cedar policies with three independent screening layers: MMseqs2 sequence alignment, ESMC-600M embedding similarity, and Foldseek structural homology; enabling defense-in-depth controls that block high-risk protein sequences before they reach downstream tools.

MCP Fraud Investigation Agent

Learn to deploy an end-to-end AI-powered e-commerce fraud investigation agent built with the Strands SDK on Amazon Bedrock AgentCore, connecting through an AgentCore Gateway over MCP to query transaction history, customer profiles, login activity, support cases, and fraud playbooks; a React dashboard on AWS Amplifystreams the agent’s reasoning token by token as it works each case.

Identity

IAM Account Access Manager with ABAC

Learn to implement workforce access using IAM account access manager and attribute-based access control, where one IAM role per project shares a single policy document and access decisions are made by comparing role tags against resource tags at request time; onboarding a new project requires only tagging and entitlement configuration with no policy authoring.

Operationalizing Least Privilege: Automate IAM Remediation through Your CI/CD Pipeline

Learn to automate remediation of unused IAM permissions using AWS IAM Access Analyzer, AWS CloudTrail, and Amazon Bedrock for AI-generated AWS CDK code; the solution attributes each role to its origin (IaC or manual), then creates pull requests for IaC-managed roles or issues for manually created roles in GitLab or GitHub, with configurable exclusion rules and policy diffs for human review.

Data Protection

Data Residency Chatbot with Amazon Bedrock AgentCore

Learn to deploy a data-residency-compliant natural-language chatbot on Amazon Bedrock AgentCore where all data and AI inference stay within a single AWS Region; the solution uses a Strands agent that answers plain-English questions from Aurora PostgreSQL through governed, whitelist-validated read-only tools exposed via an AgentCore Gateway, with a residency guard that rejects any cross-region inference profile, demonstrated with a rooftop-solar subsidy program and adaptable to any sector or geography.

LLM-based PII Detection

Learn to detect personally identifiable information in conversational text using Amazon Bedrock; the solution prompts any Converse-compatible model to return PII spans with category, value, and character offsets, handles long inputs via word-boundary chunking, recovers near-miss labels, and supports custom categories, few-shot examples, and pluggable backends beyond Bedrock.

Agentic Data Classification and Redaction

Learn to build a conversational AI research assistant that automatically classifies documents for MNPI, PII, and security sensitivity, then enforces per-user redaction at query time using Amazon Bedrock AgentCore, Guardrails, and Amazon OpenSearch Serverless vector search.

Conclusion

August 2026 provides comprehensive guidance and runnable code for governing agentic AI workloads at enterprise scale, from orchestrator-driven governance gates and biosecurity screening policies to daily executive audit pipelines and OCSF-normalized guardrail telemetry. The posts and samples provide patterns for console Private Access in isolated VPCs, attribute-based access control with IAM account access manager, automated least-privilege remediation through CI/CD, and cross-service signal correlation for multi-stage threat detection. Each resource includes deployment steps or runnable code so you can validate in your own environment before adopting. Subscribe to the AWS Security Blog RSS feed to receive updates as they publish, and revisit this digest monthly for a consolidated view of what changed and what to act on.

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Rodolfo Brenes

Rodolfo Brenes

Rodolfo is a Principal Solutions Architect focused on Cloud Governance and Compliance. With over 18 years of experience, he currently leads a technical field community in AWS helping customers scale and improve their security and governance frameworks. Besides work, Rodolfo enjoys video games, playing with his four cats, and won’t say no to a good outdoor adventure.

Anna Brinkmann

Anna has 18 years of experience in the technical content space and has spent the last 6 years managing the AWS Security Blog. Outside of work, she enjoys spending time with her family.

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