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The issue is straightforward: when Node.js resolves modules, the runtime searches for packages in C:\node_modules as part of its default behavior. Since low-privileged Windows users can create this directory and plant malicious modules there, any Node.js application with missing or optional dependencies becomes vulnerable to privilege escalation.

This issue is not new. Concerned discussions about Node.js's module search path behavior date back to 2013 and 2014.

Node.js has explicitly stated that they consider this behavior intentional:

"Node.js trusts the file system."

They do not treat CWE-427 (Uncontrolled Search Path Element) as a vulnerability, pushing responsibility onto application developers.

As the case studies below demonstrate, this stance has dangerous consequences. Developers are largely unaware of this attack surface, and the result is a proliferation of exploitable applications. We will show examples in npm CLI and Discord, but there are likely many more applications that are impacted by this.

Root Cause

The root cause lies in the way Node.js performs module resolution. This is documented here. Although UNIX paths are used in the documentation provided by Node.js, the same logic is applied on Windows.

When a Node.js application calls require(‘bar’), the runtime searches for the module in the following order:

1. C:\Users\Administrator\projects\node_modules\bar.js
2. C:\Users\Administrator\node_modules\bar.js
3. C:\Users\node_modules\bar.js
4. C:\node_modules\bar.js <-- The problem

If the legitimate package is missing, whether due to optional dependencies, development packages removed in production, or installation failures, the resolution search will eventually reach the root of the drive. Any user can create C:\node_modules and place a malicious package there. Once the low-privileged user has populated C:\node_modules\bar.js, Node.js will load and execute it in the context of the current user. In the following case studies, we will provide evidence of how, despite properly following NPM’s guidelines, third-party dependencies end up triggering this vulnerability anytime you launch the application.

Case Studies: Real-World Manifestations

The Optional Dependency Pattern: npm supports optional dependencies to be specified in the project’s package.json file. The recommended pattern for checking for these dependencies is as follows:

This pattern silently catches errors when optional packages are missing, allowing execution to continue. So what’s the problem? On Windows, Node.js will search all the way up to C:\node_modules where an attacker may have planted a malicious replacement. This search behavior mirrors UNIX conventions where /node_modules at the filesystem root is typically only writable by root. Windows systems by default allow any user to create C:\node_modules. Once require is called, Node.js will traverse the search path and execute any matching module it finds.

Important things to note:

1. This pattern can be found in third party libraries deep in a dependency tree, as we will see in the following examples.
2. There is no runtime indication to either the developers or the end users that such a vulnerability exists without looking at the filesystem logs with Procmon.
3. The optional dependency pattern itself would not be dangerous if Node.js did not search for packages in C:\node_modules.

Let’s take a deeper look at both cases and see why this is so dangerous.

Case 1: npm CLI (ZDI-26-043 / ZDI-CAN-25430 / CVE-2026-0775).

Prior to version 11.2.0, npm CLI used a library called “promise-inflight”, which contained an optional dependency on a package called “bluebird”.

When Node.js is installed on the system, npm is included by default without the bluebird package. This vulnerability was introduced when bluebird was removed through a well-intentioned pull request (https://github.com/npm/cli/pull/1438/changes), demonstrating how easy it is for developers to unknowingly create this attack surface.

We can see Node’s package resolution logic at work in the screenshot below:

First, the application looks for the bluebird.js package in the Node.js installation directory. Node.js sequentially searches back to the system root until it finds the package. If an attacker has placed C:\node_modules\bluebird.js, the require call will find, read, and execute the malicious payload in the context of any user running npm on the system.

This vulnerability is especially dangerous because it is triggered when many npm * cli commands are used. Common development commands such as npm install, npm –l, and npm prune will all execute the malicious bluebird.jspackage.

Case 2: Discord (ZDI-26-040/ ZDI-CAN-27057 / CVE-2026-0776/ UNPATCHED)

On April 22, 2025, ZDI received a report for a similar vulnerability in Discord reported by T. Doğa Gelişli. Discord uses the ws WebSocket library, which contains an optional dependency on utf-8-validate for compatibility with older Node.js versions:

Discord does not ship with the utf-8-validate package. As a result, the following Procmon logs show the same behavior as Case 1. Anytime Discord is launched, the attacker controlled C:\node_modules\utf-8-validate.js is executed.

The ws library does support disabling this check via the WS_NO_UTF_8_VALIDATE environment variable, but this requires the consuming application (Discord) to set it explicitly. Here’s a quick video demonstrating the bug by popping the calc app when opening Discord:

Discord automatically opens on login by default, so in practice code execution happens immediately without any user interaction. Strangely, the Discord Security team made it clear to us in their responses that they do not consider local attack vectors as valid security issues.

The Bigger Picture

The cases above represent only a few of the applications affected by this pattern. During our investigation we found many other independent reports.  These issues in Mongo DB Compass and Mongo DB Shell are just two other examples.

Every Windows application built on Node.js with missing or optional dependencies is potentially vulnerable. This includes desktop applications that utilize Electron as well as popular web frameworks such as Next.js and React.

Each vendor has clearly stated that they will not treat these issues as vulnerabilities:

NPM’s response to our report:

“exploits that require local access to a machine are considered ineligible for npm CLI

Discord’s response to our report:

“We do not consider physical/local attacks as valid security issues”

Node.js, in the “Examples of non-vulnerabilities” section of their Security Policy:

“Node.js trusts the file system in the environment accessible to it. Therefore, it is not a vulnerability if it accesses/loads files from any path that is accessible to it.”

Conclusion

The vulnerability pattern described in this blog stems from a deliberate design decision by Node.js maintainers. While Node.js's position that “applications should trust their filesystem” may hold true on properly administered UNIX systems, it creates a systemic vulnerability on Windows where low-privileged users can write to C:\node_modules. Without a fix from Node.js, the burden silently falls on application developers.

Making matters worse, the vulnerable code may not live in the application code itself. The optional dependencies that trigger this behavior could come from third-party libraries buried in the dependency tree as we saw with both Discord and npm CLI.

We encourage security researchers to further review this issue and investigate other applications for this dangerous behavior. You can find us online at @bobbygould5 and @izobashi, and follow the team on Twitter, Mastodon, LinkedIn, or Bluesky for the latest in exploit techniques and security patches.

DISCLOSURE TIMELINES

NPM CLI:

2024-11-13 – ZDI submitted the report to the vendor

2024-11-13 – The vendor acknowledged the receipt of the report

2024-11-13 – The vendor communicated that the reported behavior was by design and they do not consider local attacks as valid security issues

2025-08-05 – ZDI encouraged the vendor to re-assess the issue

2025-12-18 – ZDI notified the vendor of the intention to publish the case as a 0-day advisory

DISCORD:

2025-07-08 – ZDI notified vendor

2025-09-11 – ZDI followed up with vendor

2025-09-15 – Vendor stated they do not consider local attacks as valid security issues

2025-12-01 – ZDI explained why we believe the issue is still valid

2025-12-10 – Vendor replied that the vulnerability is still out of scope 

2025-12-11 – ZDI informed vendor of intent to publish 0-day 

REFERENCES

https://nodejs.org/api/modules.html#loading-from-node_modules-folders

https://docs.npmjs.com/cli/v10/configuring-npm/package-json#optionaldependencies

https://groups.google.com/g/nodejs/c/5BGr5dliUIk/m/abJEH3sPymcJ?pli=1

https://github.com/nodejs/node-v0.x-archive/issues/8830

https://bounty.github.com/ineligible.html#vulnerability_in_upstream_dependencies:~:text=eligible%20for%20rewards.-,Local%20access,-Vulnerabilities%20which%20require

https://github.com/nodejs/node/security/policy#examples-of-non-vulnerabilities

Adobe Patches for May 2026

For May, Adobe released 10 bulletins addressing 52 unique CVEs in Adobe Commerce, After Effects, Adobe Connect, Illustrator, Media Encoder, Premiere Pro, Substance 3D Painter, Substance 3D Sampler, Content Authenticity SDK, and the Adobe Substance 3D Designer. Here’s this month’s overview table:

The obvious priority this month is the patch for Commerce, with its 15 bugs and deployment priority of 2. The Connect fix should also rank up there since both of its CVEs are CVSS 9s. Beyond those, it’s a pretty typical month for Adobe, with most of the bugs either being cross-site scripting (XSS) or open-and-own code executions.

Microsoft Patches for May 2026

This month, Microsoft released a whopping 138 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Copilot Chat, Github Copilot, M365 Copilot, SQL Server, TCP/IP, and the Telnet Client – yes, the Telnet client. Two of these bugs were reported through the TrendAI ZDI program. 30 of these bugs are rated Critical, three are rated as Moderate, one is rated Low, and the rest are rated Important in severity.

This large volume of fixes follows the largest monthly release in Microsoft’s history and reflects the trend across the industry of a high number of submissions. While not all of these bugs were found by AI, it’s likely they had an AI-related component – even if it was just AI writing the submission. I should also point out the Pwn2Own Berlin occurs in just a few days, and it’s typical for vendors to patch as much as they can before the event.

None of the bugs patched by Microsoft this month are listed as publicly known or under active attack at the time of release, so we’ve got that going for us. Let’s take a closer look at some of the more interesting updates for this month, starting with a nasty-looking bug in DNS:

-    CVE-2026-41096 - Windows DNS Client Remote Code Execution Vulnerability
This patch fixes a heap-based buffer overflow in the DNS Client triggered by a malicious DNS response. No authentication or user interaction needed, and since the DNS Client runs on virtually every Windows machine, the attack surface is enormous. An attacker with a position to influence DNS responses (MitM, rogue server) could achieve unauthenticated RCE across your enterprise.

-    CVE-2026-41089 - Windows Netlogon Remote Code Execution Vulnerability
This update covers another CVSS 9.8 bug, which is a stack-based buffer overflow that lets an unauthenticated remote attacker execute code on a domain controller by sending a specially crafted network request — no credentials, no user interaction required. Yup – that makes it wormable. This is the highest-impact bug that requires immediate patching: a compromised domain controller is a compromised domain.

-    CVE-2026-42898 - Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability
This bug rates a CVSS 9.9(!) and represents a code injection in Dynamics 365. It allows any authenticated user to execute code with a scope change, meaning exploitation can break out and affect resources beyond the vulnerable component itself. Scope changes are pretty rare, so if you’re running Dynamics 365 On-Prem, definitely test and deploy this patch quickly.

-    CVE-2026-40415 - Windows TCP/IP Remote Code Execution Vulnerability
This bug in the TCP/IP stack results from a use-after-free (UAF) and could allow a remote, unauthenticated threat actor to execute code without user interaction. That makes this another wormable bug. However, this one is much less likely to be exploited. The target needs to be under sustained low-memory (memory pressure) conditions, which is pretty rare. Still, no need to tempt fate here. Test and deploy this one quickly.

Here’s the full list of CVEs released by Microsoft for May 2026:

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

Looking at the other Critical-rated bugs in this month’s release, there are quite a few scary-looking bugs (including a CVSS 10!), but there’s no action for the end user as Microsoft has already mitigated these bugs and is just now documenting them. There’s also this month’s crop of Office bugs where the Preview Pane is an attack vector. However, the bug in Office for Android does not have the Preview Pane vector; it’s simple open and own. The bug in the WiFi driver needs a network adjacent attacker. The SharePoint bug requires authentication, but anyone with site privileges has the authentication needed. The bug in SSO Plugin for Jira & Confluence should really be called an authentication bypass, since it allows an unauthenticated attacker to gain access to a system.

Looking at the other code execution bugs, most are of the open and own variety as expected. The bug in Dynamic 365 (On Prem) requires high privileges. The Message Queueing bug requires an adjacent attacker. The bug in SQL Server requires authentication, but as usual, patching won’t be straightforward. Finally, there’s a bug in the kernel that leads to code execution. Most kernel bugs are privilege escalations, but this one could allow code execution if an attacker sends specially crafted NVMe over Fabrics (NVMe‑oF) response messages during the connection handshake process that contains an invalid header length value. Neat.

As usual, the vast majority of the Microsoft release fixes Elevation of Privilege (EoP) bugs. Also as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. There are also a few bugs that just state the attacker could “gain ELEVATED privileges.” How obtuse. The bugs in Azure allow an attacker to access data otherwise hidden from them. The Edge bug allows threat actors to elevate to the privileges of the running application. The bug in Visual Studio allows attackers to get permissions associated with the MCP Server’s managed identity. Finally, there are a couple of sandbox escapes, too, which are always useful.

This month's update includes six Security Feature Bypass vulnerabilities. The most severe is in the Azure SDK for Java (CVSS 9.1). An attacker over the network can bypass the integrity protection provided by authentication tags on encrypted data, effectively manipulating encrypted input in a way that slips past integrity checks during decryption. Close behind is the bypass affecting the GitHub Copilot integration in Visual Studio Code (CWE-74). This one requires a user interaction, but it allows an attacker to circumvent the path validation safeguards that normally control which files Copilot is permitted to modify. The other Visual Studio Code bypass involves cross-site scripting, improper link resolution, and information exposure triggered when a user opens or views a maliciously crafted notebook. On the Windows networking side there are two bypasses. The first hits the Windows TCP/IP driver via an authentication bypass using an alternate channel. The other impacts the Windows Filtering Platform through improper access control, allowing a local, low-privileged attacker to bypass FQDN-based network security rules. Finally, there’s a Secure Boot bypass that, you guessed it, bypasses secure boot features.

Moving on to the Information Disclosure bugs fixed this month, we have 15 different CVEs. As usual, the majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The bug in Power Automate could expose data marked “Sensitive” within Power Automate Desktop flows. One of the Word bugs could disclose NLTM hashes. The bug in Edge could disclose your cookies, which seems rude. The bug in Visual Studio could expose file path information. Finally, there’s a bug in Telnet for Windows 11 that leaks information being used by Telnet at the time. I didn’t even realize Windows 11 still had a telnet client.

The May release contains 10 spoofing bugs (plus the ones already addressed by Microsoft). The bug in Azure Machine Learning Notebooks vulnerability requires user interaction, but it could expose info through the Azure ML web interface to the attacker. There’s a cluster of fixes for Microsoft's mobile Office suite on Android. Excel, Word, and PowerPoint for Android all carry spoofing flaws rooted in improper access control. Two Copilot products are also affected by spoofing vulns. The M365 Copilot for Desktop has no details provided. The M365 Copilot for Android variant requires low privileges and producing only limited impact on confidentiality and integrity. Microsoft Teams for Android rounds out the mobile app spoofing bugs. Three Edge bugs close things out, all involving misrepresentation of information in the browser UI.

There are two Tampering bugs in this month’s release. The one in .NET Core allows threat actors to write files to an affected system. The other is in Outlook for iOS and manifests as a command injection bug.

There are eight DoS bugs in the May release, but as always, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting from a practical standpoint are two TCP/IP bugs that allow a low-privilege Hyper-V guest to crash the host. Both are triggered from the adjacent network. On the broader network-exposure side, the ASP.NET Core bug is a straightforward infinite loop condition — an unauthenticated attacker sends a crafted request over the network and the server stops responding.

No new advisories are being released this month.

Looking Ahead

Assuming I survive Pwn2Own Berlin (which is looking iffy at the moment), I’ll return on June 9th on what will hopefully be a smaller release than this one. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

A command injection vulnerability has been reported in Arista NG Firewall. The vulnerability is due to improper validation of user data in the diagnostics component.

A remote, authenticated attacker could exploit this vulnerability by sending crafted requests to the target server. Successful exploitation could result in arbitrary command execution under the security context of the root user.

The Vulnerability

Arista NG Firewall is an open-source firewall appliance. It was originally developed under the name Untangle. Some features of Arista Firewall include spam blocking, bandwidth control, and IPS, etc. NG Firewall can be managed through a web user interface, or a JSON-RPC API using HTTP.

HTTP is a request/response protocol described in RFCs 7230 - 7237 and other RFCs. A request is sent by a client to a server, which in turn sends a response back to the client. An HTTP request consists of a request line, various headers, an empty line, and an optional message body

where CRLF represents the new line sequence Carriage Return (CR) followed by Line Feed (LF). SP represents a space character. Parameters can be passed from the client to the server as name-value pairs in either the Request-URI, or in the message-body, depending on the Method used and Content-Type header. For example, a simple HTTP request passing a parameter named “param” with value “1”, using the GET method might look like:

A corresponding HTTP request using the POST method might look like:

If there is more than one parameter/value pair, they are encoded as '&'-delimited name=value pairs:

          var1=value1&var2=value2&var3=value3...

The component relevant to this report is the JSON-RPC endpoint. A JSON object has the following syntax:

•            An object is enclosed in curly braces {}.
•            An object consists of zero or more items delimited by a comma (",") character.
•            An item consists of a key and a value. A key is delimited from its value by a colon (":") character.
•            A key must be a string (enclosed in quotes).
•            A value must be a valid type. Valid types include string, number, JSON object, array, Boolean, or null.
•            An array is an object enclosed in square braces []. An array consists of zero or more string, number, JSON object, array, Boolean or null type-objects delimited by a comma (",") character.

An example JSON object is as follows:

The following is an example of a JSON-RPC request to the runTroubleshooting() method that is relevant to this report:

A command injection vulnerability has been reported in Arista NG Firewall. The vulnerability is due to improper validation of user data that is used in a command line. The runTroubleshooting() method of the class NetworkManagerImpl will be used to handle JSON-RPC requests to the runTroubleshooting method. The command parameter passed to the method will be the first element in the params JSON array in the body of the request. This value must be one of the strings in the TroubleshootingCommands enum defined in the NetworkManager class. The second parameter of the method will contain additional arguments passed to the JSON-RPC call.

The method will first iterate through each of the additional arguments and combine each key value pair into a single string, separated by a "=" character that will later be used as an environment variable. Next, a switch case statement is used to ensure the provided command is one of the values in TroubleshootingCommands. Each command value will be processed using the same code.

The method will next iterate through each environment variable, and inspect it for the following common command injection strings:

          ; & | > $(

If any are found, the request will be rejected, and an exception is thrown. If each environment variable is valid, the method execEvil() is called to create and execute a command line for the network-troubleshooting.sh script, with the environment variables passed as a parameter. The execEvil() method in turn will call Runtime.getRuntime().exec() to run the script, with the second parameter passing the environment variables that will be used by the script. Each command value will have a function in network-troubleshooting.sh, such as run_dns() for the “DNS” command value. Each function will follow a similar structure, by creating a CMD string using the environment variables passed by exec() and then calling eval to execute it.

However, the values of the parameters passed to the runTroubleshooting JSON-RPC method are not completely sanitized before it is used in the command line. While the parameters passed to the endpoint are inspected for some shell metacharacters, the list is incomplete. For example, the backtick character (`) is not included in the check and may be used to inject a command.

For example:

The example above will write and execute a python script on the server to achieve code execution without using any restricted characters.

A remote, authenticated attacker could exploit this vulnerability by sending a JSON-RPC request to the runTroubleshooting method containing a crafted “HOST” or “URL” parameter containing shell metacharacters not present in the runTroubleshooting() check. Successful exploitation in the worst case will result in arbitrary command execution under the security context of the root user.

Detection Guidance

To detect an attack exploiting this vulnerability, the detection device must monitor and parse traffic on the following ports:
          - HTTP, over port 80/TCP
          - HTTPS, over port 443/TCP

Traffic to Arista NG Firewall may be encrypted and must be decrypted prior to applying this guidance.

The detection device must search for HTTP POST requests made to the request-URI /admin/JSON-RPC. If found, the body of the request must be parsed as JSON. The JSON object in the body must be inspected for a method key, and its value must be inspected to contain the substring runTroubleshooting. If found, the object must also be inspected for the JSON key "params", with a value containing a JSON array. The first entry in the JSON array must be inspected for any of the following strings:

If found, the second entry in the array must be inspected for a JSON object, and inspected for any of the following keys:

If either is found, the corresponding value to the key must be inspected for any of the following command injection characters:

If found, the traffic should be treated as suspicious; an attack exploiting this vulnerability is likely underway.

The following regular expression can be applied to find malicious requests:

          /\x22(HOST|URL)\x22\s*:\s*\x22(?:[^\x22\\]|\\.)*?[\x60\x27\x24\x3c]/

Notes:

• String matching on the request-URI and all JSON strings should be done in a case sensitive manner.
• The JSON strings may be encoded and must be decoded prior to applying this guidance.
• The request-URI may be URL-encoded and must be decoded before applying this guidance.

Conclusion

This vulnerability has been addressed by Arista with their Security Advisory 0123. They note that the Arista Edge Threat Management - Arista Next Generation Firewall (Formerly Untangle) is affected by this bug, but other product versions are not. They also state the following mitigation can be applied:

Do not allow non-authorized administrative access or access to the administrative browser.

However, the more appropriate action is to apply the provided vendor security patch by upgrading to version 17.4 or higher.

Special thanks to Jonathan Lein and Simon Humbert of the TrendAI Research team for providing such a thorough analysis of this vulnerability. For an overview of TrendAI Research services, please visit https://go.trendmicro.com/tis/vulnerabilities.html.

The threat research team will be back with other great vulnerability analysis reports in the future. Until then, follow the team on Twitter, Mastodon, LinkedIn, or Bluesky for the latest in exploit techniques and security patches.

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Executive summary 

A group of Russian state-supported cyber actors has been targeting and compromising various Western government and commercial organizations using the Zimbra Collaboration Suite (ZCS) software since at least July 2025. The Russian state-supported advanced persistent threat (APT) group’s activity is tracked in the cybersecurity community under several names (see Cybersecurity industry tracking), primarily as “LAUNDRY BEAR,” a name initially coined by the Netherlands General Intelligence and Security Service (AIVD) and Defence Intelligence and Security Service (MIVD) [1].

LAUNDRY BEAR’s targeting is almost certainly to gather sensitive information for the Russian Federation, with these actors primarily focusing on the covert acquisition of email data. Previous campaigns indicated LAUNDRY BEAR relied on unsophisticated initial access techniques—including password spraying, phishing, and pass-the-cookie—allowing the group to successfully run high-volume operations. The latest campaign targeting ZCS uses a novel exploit that was a zero-day vulnerability when first exploited and continues to be successfully exploited. The vulnerability, Common Vulnerabilities and Exposures (CVE) CVE-2025-66376, was patched in November 2025. This demonstrates LAUNDRY BEAR’s intent and ability to deploy increasingly sophisticated technical capabilities.

Unlike traditional phishing campaigns that persuade a user into taking an action, such as clicking a link or opening a file, LAUNDRY BEAR’s latest campaign leverages a view-based exploit that only requires a user to view a malicious email within a vulnerable version of the webmail service. Once viewed, the exploit attempts to exfiltrate the victim’s last 90 days of email communications, the organization email directory (i.e., Global Address List [GAL]), and other sensitive information to servers controlled by LAUNDRY BEAR. The exploit also attempts to establish persistent access to victim accounts through a variety of means as detailed in the Persistence and credential access section.

This Cybersecurity Advisory (CSA) warns of this ongoing malicious threat activity and urges organizations to update their vulnerable software and implement additional mitigations to thwart these Russian state-supported actors’ continued success. The CSA is being released by the following authoring and co-sealing agencies:

• United States National Security Agency (NSA)
• United States Federal Bureau of Investigation (FBI)
• Netherlands Defence Intelligence and Security Service (MIVD)
• Netherlands General Intelligence and Security Service (AIVD)
• United States Cybersecurity and Infrastructure Security Agency (CISA)
• United States Defense Counterintelligence and Security Agency (DCSA)
• United States Department of Defense Cyber Crime Center (DC3)
• United States Department of the Treasury
• United States Naval Criminal Investigative Service (NCIS)
• Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
• Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
• New Zealand National Cyber Security Centre (NCSC-NZ)
• United Kingdom National Cyber Security Centre (NCSC-UK)
• Czech Republic National Cyber and Information Security Agency (NÚKIB)¹
• Danish Defence Intelligence Service (DDIS)²
• Estonian Foreign Intelligence Service (EFIS)³
• Finnish Defence Intelligence (FDI)⁴
• Finnish Security and Intelligence Service (SUPO)⁵
• French General Directorate for Internal Security (DGSI)⁶
• French National Cybersecurity Agency (ANSSI)⁷
• Italian External Intelligence and Security Agency (AISE)⁸
• Italian Internal Intelligence and Security Agency (AISI)⁹
• Security and Intelligence Service of the Republic of Moldova (SIS RM)¹⁰
• Polish Foreign Intelligence Agency (AW)¹¹
• The Military Counterintelligence Service of Poland (SKW)¹²
• Spain National Intelligence Centre (CNI)¹³
• Sweden National Cyber Security Centre (NCSC-SE)¹⁴

The authoring agencies urge any organizations using ZCS to implement the recommendations listed within the Mitigations section of this advisory to reduce the risk associated with this activity. This CSA also includes specific remediations for organizations to implement if they discover the presence of the listed Indicators of compromise (IOCs).  

As more organizations update their ZCS software based on this CSA, LAUNDRY BEAR may discontinue the current campaign exploiting this vulnerability; however, based on the success of this and previous campaigns, it is very likely that the group will continue to target ZCS and other email systems used by organizations in Western countries. The actors will almost certainly continue to rely on email to engage potential victims by exploiting novel vulnerabilities and, when necessary, use social engineering techniques to assist with their efforts. The authoring agencies recommend organizations regularly update their mail service software and continuously monitor their email systems and emails for malicious activity.

For a downloadable list of IOCs, see:

• AA26-204A.stix.xml (STIX XML)
• AA26-204A.stix.json (STIX JSON)

Cybersecurity industry tracking

The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to these Russian state-supported cyber actors. While not exhaustive, the following are threat group names commonly used for these actors within the cybersecurity community:

• LAUNDRY BEAR
• Void Blizzard [2]
• CL-STA-1114 [3]
• TA488 (formerly UNK_PitStop) [4]

Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the U.S. government’s understanding for all activity related to these groupings.

Background

Public advisories from Netherlands General Intelligence and Security Service (AIVD), Netherlands Defence Intelligence and Security Service (MIVD), and Microsoft highlighted these Russian state-supported advanced persistent threat (APT) actors in May 2025, calling them LAUNDRY BEAR and Void Blizzard respectively [1] [2]. Both advisories assessed that the group was engaged in malicious cyber activity as early as April 2024.  

The May 2025 advisories highlighted a cluster of activity targeting cloud-based email environments, including Microsoft Exchange in particular, and abusing legitimate APIs to perform data exfiltration in bulk [T1114.002]. The group relied on unsophisticated means of initial access, including procuring stolen credentials on criminal marketplaces [T1078], and using social engineering techniques to lure targets into interacting with a malicious site masquerading as a legitimate one. As of April 2025, one of these sites resembled a European Defence & Security Summit registration portal that required registrants to sign in to their Microsoft account to view. Once a user entered their Microsoft credentials into this malicious site, LAUNDRY BEAR’s modified version of the open source adversary emulation toolkit, Evilginx, intercepted the user’s credentials. LAUNDRY BEAR then used this authentication data, including passwords and session tokens, to access the compromised account and conduct mass email exfiltration, as well as harvest other information. This method of compromise is commonly known as an adversary-in-the-middle (AiTM) technique [T1557].  

Beginning around July 2025, LAUNDRY BEAR shifted toward a more technical method of email compromise, highlighting their continued efforts to covertly acquire email communications from a variety of Western organizations of interest and deliver them to the Russian Federation. Using a custom-developed capability [T1587.001] named “Улей” or “Ulej” (Russian for beehive), LAUNDRY BEAR successfully targeted and exfiltrated sensitive user information from organizations who use the Zimbra Collaboration Suite (ZCS) product [T1114]. Data LAUNDRY BEAR attempted to exfiltrate from compromised accounts included:

• Last 90 days of emails,
• Email address,
• Password [T1589.001],
• Global Address List (GAL) [T1087],
• Two-factor authentication (2FA) tokens, and
• Newly-created Application Passcode [T1098].

The covert and persistent nature of this activity, along with the absence of any known financial extortion, almost certainly indicates this group’s involvement in espionage activities with Russian government backing. Additionally, extensive Ukrainian targeting, prior to use against U.S. and other NATO allies, outlines an increasing trend within Russian cyber threat groups to target Ukrainian users first—both as a priority target and as a testbench for malicious cyber techniques before broader global deployment.

Targeting details

LAUNDRY BEAR has targeted and compromised users in various organizations, including those associated with:

• the Defense Industrial Base (DIB),  
• the federal and local government,
• education,
• energy,
• law enforcement,  
• media,  
• non-governmental organizations, and
• technology.

Technical details

Note: This advisory uses the MITRE ATT&CK® Matrix for Enterprise framework, version 19. This advisory also uses MITRE D3FEND^TM version 1.4.0¹⁵. See Appendix A and Appendix B for tables of the activity mapped to MITRE ATT&CK and D3FEND tactics, techniques, and countermeasures.

Ulej is a novel data exfiltration and aggregation capability, that currently (as of the publication of this report) supports a campaign specifically targeting users of ZCS webmail servers. This capability is used to exploit CVE-2025-66376 [Common Weakness Enumeration (CWE) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')], but likely could be adapted to exploit other vulnerabilities. It exfiltrates emails and other sensitive user data from a victim’s system immediately after exploitation and stores the data in an actor-controlled unattributable virtual private server (VPS) [T1074.002] running LAUNDRY BEAR’s “Flowerbed” collection framework. The collected data is almost certainly further exfiltrated to internal network resources for review and long-term retention.

Reconnaissance

LAUNDRY BEAR uses the Ulej capability to exploit the CVE-2025-66376 vulnerability in organizations using ZCS. This campaign’s targeted victimology and limited exploitation capabilities likely indicate this group manually identifies and targets the victim organizations. LAUNDRY BEAR likely identifies organizations with public-facing Zimbra infrastructure by port scanning [T1595] and fingerprinting datasets easily procured through various commercial vendors [T1596.005].  

After identifying a target organization, the group likely compiles email addresses for individual users to target with the exploit [T1589.002] from datasets offered by commercial vendors [T1597.002], open source intelligence [T1593], or previously exfiltrated data [T1597].  

Resource development

The actors procure VPSs from a variety of providers [T1583.003], including those with Know Your Customer (KYC) requirements, and often use fabricated identities. LAUNDRY BEAR primarily uses Mullvad VPN [T1583] when interacting with these servers, further demonstrating the group’s intent to mask their identity and maintain operations security (OPSEC). After the server is provisioned, an automated process deploys the Docker containers necessary for Ulej’s Flowerbed framework [T1608], which then receives and aggregates the data Ulej exfiltrates. These servers are typically only used for 7-60 days before moving to new infrastructure.

Flowerbed framework

Flowerbed is a Python project that uses Docker for containerization. The project includes four different Docker containers:

• Catcher,
• Certbot,
• Nginx, and
• Gardener.

Catcher acts as both a DNS and HTTP server to receive and aggregate exfiltrated victim information [T1048]. For additional information on Catcher, refer to the Exfiltration section of this advisory. Flowerbed’s next container, Certbot, is based on one of the official Certbot containers, which allows for automated generation of Let’s Encrypt certificates using DNS challenges through Cloudflare. This certificate can then be used by the Nginx container, which serves as an HTTPS reverse proxy for Catcher, enabling Flowerbed to disguise some of its exfiltration activity through an encrypted communications channel [T1048.002]. The Nginx reverse proxy also validates that the Server Name Indicator (SNI) value contains “*.i.*” prior to forwarding the traffic to Catcher. If the SNI does not contain that string, the Nginx server returns a 444 error to the client. This is likely an attempt to reject non-Ulej connections. Finally, the Gardener container functions as a health check for the Catcher service. Gardener is a simple Python script that validates Catcher correctly receives and processes data.

The simplistic Flowerbed codebase has indications that artificial intelligence (AI) played a role in its development. This highlights how AI is increasingly being used to develop malicious capabilities [T1588.007]. The dependence on AI for a simple capability, such as Flowerbed, alongside a previous reliance on open source capabilities, such as Evilginx2 [T1588.002], likely indicates a lack of advanced technical knowledge within LAUNDRY BEAR, especially in relation to true software development capabilities.

Initial access

To gain initial access, LAUNDRY BEAR sends an email containing a malicious JavaScript payload to the target [T1566]. Through exploitation of CVE-2025-66376, this JavaScript payload is immediately executed once the user views the malicious email [T1203], such as the one shown in Figure 1, in the ZCS webmail platform. Since at least November 2025, LAUNDRY BEAR began sending these phishing emails from victim infrastructure through compromised accounts [T1199], as shown in the email metadata in Figure 2. These compromised accounts were likely previous victims of this, or another LAUNDRY BEAR, campaign and their use is intended to further obfuscate and frustrate anti-phishing tools and training.

According to the National Vulnerability Database (NVD), CVE-2025-66376 was initially published on 5 January 2026. This vulnerability allows for execution of a JavaScript payload included in email content due to improper sanitization of Cascading Style Sheet’s (CSS) @import directives within an email [5]. Because the activity attributed to this campaign began in July 2025—months before Synacor released a patch and the CVE was published—the payload initially exploited a zero-day vulnerability at that time [T1587.004].  

Utilization of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability.

Hidden in LAUNDRY BEAR’s email is a Base64 encoded payload within the “onload” field of a Scalable Vector Graphics (SVG) element [T1027.017], as shown in Figure 3. Leading up to the inclusion of this payload in the SVG element are various instances of @import directives, as required to leverage CVE-2025-66376. This payload includes an XOR encrypted final script encoded in a Base64 inner payload (see Figure 3) [T1027.013]. The outer payload decodes and decrypts the inner payload using an XOR function and a hardcoded key and then executes the script contained within the inner payload containing the collection and exfiltration logic. By changing the key used for the XOR encryption of the inner payload or adding additional @import directives with non-functional code [T1027.010], LAUNDRY BEAR can easily generate new payloads that bypass basic threat detection signatures. This malicious payload attempts to collect and exfiltrate information in 12 asynchronous stages [T1119]. The stages in order of appearance within the payload are as follows:

1. sendStartPing,
2. gather_email,
3. gather_environment,
4. gather_2fa_codes,
5. gather_app_password,
6. gather_device_status,
7. gather_oauth_consumers,
8. gather_autocomplete_password,
9. enable_mail_protocols,
10. gather_gal,
11. sendArchives, and
12. sendFinishPing. 

Use of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability [T1587].

Persistence and credential access

To establish sustained persistence into the victim’s email account, the script attempts to modify account preferences and collect authentication information. Any collected credentials are later exfiltrated, as further described in the Exfiltration section below. Other campaigns attributed to LAUNDRY BEAR also demonstrated the group’s ability to circumvent multi-factor authentication through session token replay [T1550.004], and the Zimbra campaign follows a similar trend.

The script used in this campaign tries to discover the victim’s email address during the gather_email stage [T1087]. The script searches for this email address in two ways. First, it examines the batchInfoResponse variable, which an HTML script element on the webpage can define, for an email address. Even if the script finds an email address there, it also checks whether it acquired a Cross-Site Request Forgery (CSRF) token as described later in the Collection section of this advisory. If so, the script uses the “GetIdentitiesRequest” Simple Object Access Protocol (SOAP) command under the “ZimbraAccount” namespace to determine the victim’s email address [T1185] and then exfiltrates it. However, if the script does not have a CSRF token or the SOAP request fails, the script exfiltrates the email value recovered from the first method instead. If both attempts fail to capture the victim’s email, the script sends a JavaScript Object Notation (JSON) payload with a key of “email” and value of null over HTTPS and does not attempt DNS exfiltration.

During the gather_autocomplete_password stage, the script attempts to collect the victim’s saved password via the autocomplete feature of the victim’s password manager. The script injects two HTML div elements requesting login credentials onto the page outside of the victim’s view, as shown in Figure 4 and Figure 5. After waiting five seconds, the script then attempts to extract the password provided automatically by the password manager from the input element shown in Figure 4. If there is no value in that input field, it checks the password input field shown in Figure 5. If neither input field contains a value, a JSON payload with a key of “autocomplete_password” and value of null is sent over HTTPS and DNS exfiltration is not attempted.

LAUNDRY BEAR almost certainly relies on a mail client using the Internet Message Access Protocol (IMAP) for persistent access to the victim’s mailbox. During the enable_mail_protocols stage, a SOAP request leveraging the “ModifyPrefsRequest” command under the “ZimbraAccount” namespace is sent. This request attempts to set the “zimbraPrefImapEnabled” preference to TRUE. While the default setting for “zimbraPrefImapEnabled” is not well documented, this action is almost certainly intended to ensure that IMAP access to the victim’s mailbox is enabled.

ZCS does not support 2FA for some mail clients, including IMAP. To support users who rely on IMAP clients, ZCS allows for the generation of Application Passcodes. Application Passcodes are randomly generated passwords that can be used for clients that cannot support the normal 2FA process to authenticate. During the gather_app_password stage, the script makes a SOAP request using the “CreateAppSpecificPasswordRequest” command under the “ZimbraAccount” namespace to create a new Application Passcode [T1556.006]. The SOAP request uses “ZimbraWeb” as the name of the application.

Additionally, the script also attempts to collect 2FA tokens. During the gather_2fa_codes stage, the script makes a SOAP request using the “GetScratchCodesRequest” command under the “ZimbraAccount” namespace. The script then attempts to exfiltrate any non-null 2FA codes collected this way. The number of codes can vary, and each code is exfiltrated to Flowerbed individually.

Collection

As demonstrated in the Persistence and credential access section, this script relies heavily on SOAP requests to collect victim information. To make these requests, the script aims to acquire the victim’s current CSRF token, which it attempts to access within the webpage’s local storage using localStorage.getItem("csrfToken"). If the script is unable to acquire this CSRF token, it will be unable to make any SOAP requests. In addition to the SOAP commands documented in the Persistence and credential access section, other SOAP commands executed to collect victim information are shown in Table 1.

The script attempts to collect the victim’s GAL through brute force by searching for each two-character combination from a character set of “abcdefghijklmnopqrstuvwxyz1234567890.-_”. These queries are conducted using 20 batches of SOAP requests with 77 “SearchGalRequest” SOAP commands in each batch except for the last request containing only 58.

During the gather_environment stage, the script attempts to determine which type of ZCS webmail client the victim is using. The script checks the user’s current URL to determine the client type being used, checking for certain indicators (shown in Table 2) to determine the client type. The corresponding value is then used as the payload when exfiltrating the client type.

As part of collection, the script attempts to harvest any emails not marked as “junk” from the last 90 days from the victim’s account. Emails are collected daily by an HTTP GET request to the URL path, “/home/~/?fmt=tgz&meta=0&query=date:-{DAY_OFFSET}d AND (not in:junk)”. The {DAY_OFFSET} value would be between 0 and 89 representing how many days ago the email was sent or received. To prevent redundant collection and exfiltration of emails, a variable with a name based on the email date being queried, using a format of zd_comp_YYYY-MM-DD, and value of true, is saved to the window.top.localStorage property. This variable is saved regardless of whether the email is successfully exfiltrated.  

According to Mozilla documentation, if the user is not in a private browsing session, any data stored to localStorage does not typically expire. This means that if the user happens to execute the script again from the same computer, the script avoids attempting to re-exfiltrate previously captured emails. However, the script always attempts to pull any emails with a {DAY_OFFSET} of zero. In other words, the script always pulls emails sent or received the same day it is run. After email results are returned from the query for each day of email activity, those results are then passed to Flowerbed as described in the Exfiltration section.

The script also provides LAUNDRY BEAR with telemetry on any errors that occur during the collection process. This is accomplished by executing any collection or exfiltration code through helper functions that contain error handling logic. If an error occurs, a payload containing information on the error itself, the context of the error happening, and the stage in which the error occurred is sent to Flowerbed as described in the Exfiltration section below. For cases where the error occurs within a SOAP request, “:api” is concatenated to the stage value in the payload. If an error occurs during the batch SOAP requests that occur when collecting the GAL of the victim, the stage value will use a format of gather_gal:{VAL}:api. The {VAL} placeholder indicates which batch request, a number from 0 to 19, the error occurred in. Errors that occur during the password autocomplete interception process will use “gather_autocomplete_password:dom” for the stage value. Finally, if an error occurs when attempting to collect or exfiltrate a specific day’s emails, the stage will include which day the error occurred on, using the previously defined placeholder {DAY_OFFSET}, with a format of sendArchive:day-{DAY_OFFSET}.

Exfiltration

At the end of each stage in the collection process, the script attempts to exfiltrate acquired information to Flowerbed. The script primarily relies on two forms of data exfiltration: DNS [T1048.003] and HTTPS. Some information is exfiltrated over both the DNS and HTTPS channels.

Prior to exfiltration, a randomized 10- or 11-character alphanumeric string is generated as an identifier for the victim. This identifier is included in the URL of both the DNS- and HTTPS-based exfiltration.  

DNS exfiltration

DNS exfiltration occurs through DNS A record queries. To ensure data exfiltrated through DNS is not corrupted when traversing through non-actor-controlled DNS infrastructure, Ulej maintains compliance with RFC 1035, Domain Names - Implementation and Specification, specifically accounting for the case insensitivity and subdomain length requirements. Base32 encoding is used to create a case-insensitive payload. Once the payload is encoded, a period (“.”) is added every 60 characters to ensure each subdomain is under 63 characters long. The script then creates a new image object sourced from a URL with the scheme defined in Figure 6. Any traffic involving DNS exfiltration will have “d-“ prefixing the victim identifier, and the subdomain immediately following indicates the type of information being exfiltrated.

When the script generates an image object, the browser tries to retrieve the complete domain of the URL specified as the source of the image. This triggers a DNS request sent to the actor-controlled server and processed by Flowerbed. Table 3 lists both the information exfiltrated via DNS and their corresponding data type identifiers in the DNS queries.  

HTTPS exfiltration

Any information exfiltrated via DNS is also exfiltrated through HTTPS, as well as additional data including email content, contacts, attachments, and error logging information. By using Let’s Encrypt certificates, this group can quickly deploy new infrastructure and leverage encrypted HTTPS communications with valid server certificates when exfiltrating information from the victim’s environment. The HTTPS exfiltration capability only uses two HTTP content types, defined in Table 4. Traffic associated with HTTPS exfiltration will use the URL scheme shown in Figure 7.  

Some of the data transmitted via HTTPS uses the standard JSON content type format. The script includes the information in a POST request to actor-controlled infrastructure.  

Table 5 provides a summary of the JSON-based exfiltration.

The script transmits all HTTPS exfiltration not identified in Table 5 using the Octet-Stream content type as binary data. The POST requests for this method include a filename in the “X-Filename” header. Traditionally, developers use headers prefixed with “X-” to denote custom headers that do not follow a defined standard. The purpose of including this header remains unclear since the Catcher capability ignores the provided filename when saving the data. Table 6 summarizes the data exfiltrated in this format.

The script sends all exfiltrated data identified in Table 6 to the Catcher service exactly as received from the SOAP request in a JSON payload, except for email exfiltration. For email exfiltration, the script sends it as a GZIP compressed archive [T1560]. Although most of the exfiltration consists of valid JSON, the script still attempts to exfiltrate all information identified in Table 6 using the application/octet-stream content typing rather than application/json.

At the beginning and end of the collection and exfiltration activity, during the sendStartPing and sendFinishPing stages respectively, the script submits a POST request with a JSON payload to indicate that the script is starting or finishing execution. Throughout execution, the script also logs error events and send the logs using similar JSON payloads. The script sends the JSON in a POST request to the URL documented in Figure 2, using a URL path of “/v/p” and with a “subtype” key that shows which type of action it logged (start, finish, or error).  

Catcher

Ulej exfiltrates information to Flowerbed to be handled by a service named Catcher. Catcher is a containerized Python application, running in Docker as part of Flowerbed, which is detailed in the Resource development section. It receives exfiltrated data and temporarily stores it, enabling its eventual transfer to infrastructure designed for long-term, secure storage.

Catcher acts as an HTTP server over port 8000 and a DNS server on port 53. As described in the Resource development section, the Flowerbed project uses an additional Docker container running an Nginx reverse proxy to enable HTTPS support. This reverse proxy uses a certificate generated by Let’s Encrypt and forwards all traffic with an SNI containing “*.i.*” to port 8000 within the Catcher container.

The DNS service can accept A, AAAA, MX, TXT, and CAA queries. For any MX, AAAA, or CAA queries, the server will always provide an empty response. The system only supports TXT records as needed to process Automatic Certificate Management Environment (ACME) requests, which enable the assignment of Let’s Encrypt certificates. If the server receives an A query, Catcher will always respond with the public IP address of the Flowerbed server.  

However, if a query includes a domain formatted as shown in Figure 6 and Figure 7, the service saves a log file in JSON format to disk containing the following details of the DNS query:

• Time of query,
• Source IP address for query,
• Queried domain, and
• Type of query.

The HTTP server typically responds with OK, except in cases where the path is “pixel.gif” when the response contains a 1x1 gif image with a SHA-256 hash of ef1955ae757c8b966c83248350331bd3a30f658ced11f387f8ebf05ab3368629. Like the DNS service, the HTTP service will only log entries when the domain found in the host header of the request follows the expected formatting as seen in Figure 6 and Figure 7. As the HTTPS exfiltration uses non-standardized binary and JSON-formatted payloads when exfiltrating to Catcher, Catcher will check the content type of the request. If the content type is set to “application/json”, Catcher encodes the data in Base64 and includes it in the JSON log entry written to disk. If the content type is set to any other value, Catcher leaves the Base64 payload in the JSON log entry blank and saves the payload to a separate file with the same filename as the JSON log entry with a “.bin” file extension. An HTTPS exfiltration event causes Catcher to save a JSON formatted log file to disk containing the following information from the HTTP request:

• Time,
• Source IP address,
• Request method,
• Host,
• Path,
• Query string,
• Headers, and
• Base64 payload.

These JSON event log files and binary output files are then initially saved to the directory /root/hits/tmp and later moved to the /root/hits/ready directory once processed. This prevents incomplete files, which are still being uploaded to Catcher, from premature exfiltration from the server. Approximately every 60 seconds, a likely automated workflow establishes a Secure Shell (SSH) connection with the server hosting Flowerbed for a few seconds, almost certainly exfiltrating the data processed by Catcher to non-public-facing infrastructure. The command in Figure 8 also executes hourly to remove all files last modified at least two days ago from the /root/hits/ready directory.

Response strategies

Mitigations

In many cases, by the time an organization identifies a compromise related to this campaign, numerous sensitive and proprietary emails have already been exfiltrated. The significant risk posed by this cyber threat emphasizes the importance for organizations that use ZCS and other similar webmail solutions to take proactive steps to mitigate this risk.

All organizations that use the ZCS webmail service should immediately prioritize ensuring that their ZCS is not running a vulnerable version. A patch for CVE-2025-66376 was released for both 10.1.13 and 10.0.18 versions of ZCS [D3-AH]. If immediate patching is not feasible, organizations should advise employees to use alternative mail clients to access email and avoid using the Classic ZCS webmail client until ZCS is updated to a non-vulnerable version [d3f:Isolate].

System administrators should closely monitor any Internet-connected ZCS or other email systems and the workstations that access those systems and promptly apply available software updates [D3-AH]. Administrators can maintain awareness of active vulnerability exploitation by referencing open source resources, including CISA’s Known Exploited Vulnerabilities Catalog and NCSC-UK’s Responding to active exploitation of vulnerabilities guidance.

Organizations should consider using a third-party authentication service that supports passkeys for authentication to mediate access to ZCS and other services that do not natively support passkeys. By doing so, organizations can work to eliminate the possibility of automated password collection from autocomplete or password reuse [D3-CH]. However, Application Passcodes may still be necessary and should be monitored closely.  

Organizations should implement network monitoring capabilities with collection and short-term retention of packet capture or NetFlow data and maintain log collection and storage [CPG 3.Q]. This will allow organizations to monitor for and identify suspicious network activity [CPG 4.B], such as:

• Significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization [D3-NTA];
• Frequent DNS queries for a suspicious domain with seemingly random subdomains [D3-DNSTA];
• A sudden spike of connections to a server associated with a recently established domain [D3-NTCD]; and  
• Connections to internal services, such as webmail, from VPN providers frequently leveraged by this group for nefarious activity, such as Mullvad VPN [D3-NTCD].

Additionally, for organizations that can inspect the content of outbound HTTPS connections via break-and-inspect infrastructure, security teams should identify traffic matching the characteristics described in the Exfiltration section of this advisory.

Indicators of compromise (IOCs)

Flowerbed infrastructure

The following indicators have been attributed to use by LAUNDRY BEAR for their campaign targeting ZCS’s webmail service as of the publication of this advisory. (Disclaimer: Due to the frequency of operational structure changes by this group, these indicators are intended solely for historic attribution purposes. Some indicators, such as IPs, compromised emails, and domains, may be outdated, so organizations should check for current activity before acting on these IOCs.) Table 7 provides details about the server infrastructure used to host Flowerbed, and Table 8 lists the corresponding SHA-1 hash values for the Let’s Encrypt certificates used by that infrastructure [D3-IAA].

LAUNDRY BEAR has used the following email addresses to procure resources used for this campaign:

• ivanka.zurabishvili@proton[.]me,
• zmul1@buildandconsulting[.]com,
• garrysmithme@pinmx[.]net, and
• hostingclient@pinmx[.]net.

Phishing distribution

LAUNDRY BEAR primarily relied on ProtonMail for distribution of malicious email. However, as stated above, LAUNDRY BEAR’s more recent efforts likely have shifted to distributing the payload through previous victims.  

The following email addresses have distributed payloads attributed to this campaign:

• c.laurent.ejfa@proton[.]me,
• j.moreau.epsc@proton[.]me,
• liberty.insights@proton[.]me,
• certain email addresses (presumably compromised) at the isofts.kiev[.]ua domain (i.e., ending with @isofts.kiev[.]ua), and
• certain email addresses (presumably compromised) at the navs.edu[.]ua domain (i.e., ending with @navs.edu[.]ua).

Additionally, the following are SHA-256 hashes of email samples containing the malicious payload attributed to this campaign:

• 98df604ecc57f884a2e6ce3266a0013ad64455cac48442c2312cfa4765007aaf,
• 60db9abae75cd8ccc49dd7ea5feb41677566dcd442f12ebc5745ffd2810fb874,
• b1f5beb1175fc5c7d1806a2f0d900eb124c54f0286c5c52b66eea7a6633adb1d, and
• 1517b3caa495f6c4e832df9c75fc94667e3c233773f7fa4e056d5e30e5ead760.

Post-compromise artifacts

Currently, the script does not remove artifacts. This leaves additional opportunities to identify victims of this activity. While emphasis should always be placed on consistent monitoring of network traffic and endpoint activity, there are a variety of persistent artifacts described below that can be used to identify victims of this campaign.

This Ulej capability relies on creating a significant number of SOAP requests to collect account information for exfiltration. ZCS logs from these requests are stored, by default, in the /opt/zimbra/log/mailbox.log file [D3-PA]. A significant amount of SOAP request activity that aligns with what was described in the Persistence and credential access and Collection sections of this advisory could indicate a potential compromise. Specific examples of high-risk SOAP request activity might include:

• Many SearchGalRequest command requests from a single user over a short period of time;
• Use of the CreateAppSpecificPasswordRequest command, especially in cases where it is creating an Application Passcode named “ZimbraWeb”; and
• Use of the GetScratchCodesRequest command.

While LAUNDRY BEAR uses the localStorage property to track what days had emails previously exfiltrated, defenders can use this property to identify victims of this campaign and determine the scope of exfiltrated information [D3-PA]. Review of the items stored in that property for an organization’s ZCS webmail client page on an endpoint device could indicate compromise if there are items named with a format of zd_comp_YYYY-MM-DD, as explained in the Collection section of this advisory.

While Application Passcodes have non-malicious purposes, in this case instances of these passcodes with the name “ZimbraWeb” are almost certainly malicious. The ZCS webmail application can support 2FA natively and does not require the use of an Application Passcode, so there is no reason that there should be one named “ZimbraWeb.”

In instances where organizations identify victims of this campaign, they should also examine the inbox of the suspected victim for the original phishing email [D3-MA]. If an email that has a payload exploiting CVE-2025-66376 is discovered, steps should be taken immediately to identify and quarantine other instances of emails with similar body content, senders, and subject lines to prevent further exploitation and exfiltration.  

Remediation

In the event an organization identifies activity associated with this campaign, that organization should take steps to minimize further exploitation. The organization should consider requesting that employees minimize use of the ZCS webmail client until the organization updates to a patched version that is not vulnerable to CVE-2025-66376.

Organizations should use identifiers from the IOCs section of this report to identify any individuals compromised by this campaign and record the date(s) of compromise(s) to determine the scale and scope of emails exfiltrated.

All users from the organization should have all Application Passcodes and 2FA scratch keys revoked. Affected organizations should require all employees to change passwords in line with establishing minimum password strength requirements [CPG 3.B] and creating unique credentials [CPG 3.C], specifically noting that compromised employees might have had any password stored in a password manager exfiltrated.

Works cited

[1] Netherlands General Intelligence and Security Service (AIVD) and Netherlands Defence Intelligence and Security Service (MIVD). AIVD and MIVD identify a new Russian cyber threat actor. 2025. https://www.aivd.nl/site/binaries/site-content/collections/documents/2025/05/27/aivd-en-mivd-onderkennen-nieuwe-russische-cyberactor/Advisory+AIVD+en+MIVD+Public+report+on+new+cyber+actor.pdf

[2] Microsoft Corporation. New Russia-affiliated actor Void Blizzard targets critical sectors for espionage. 2025. https://www.microsoft.com/en-us/security/blog/2025/05/27/new-russia-affiliated-actor-void-blizzard-targets-critical-sectors-for-espionage/

[3] Palo Alto Networks Unit 42. Russian Global Webmail Espionage. 2026. https://unit42.paloaltonetworks.com/russian-webmail-espionage/ 

[4] Proofpoint. TA488 Targets Zimbra Mailservers with Half-Click Exploits. 2026. https://www.proofpoint.com/us/blog/threat-insight/ta488-zcs-exploit

[5] Seqrite. Operation GhostMail: Russian APT exploits Zimbra Webmail to Target Ukraine State Agency. 2026. https://www.seqrite.com/blog/operation-ghostmail-zimbra-xss-russian-apt-ukraine/  

Footnotes

¹ Národní úřad pro kybernetickou a informační bezpečnost
² Forsvarets Efterretningstjeneste
³ Välisluureamet
⁴ Sotilastiedustelu
⁵  Suojelupoliisi
⁶ Direction générale de la sécurité intérieure
⁷ Agence nationale de la sécurité des systèmes d’information
⁸ Agenzia Informazioni e Sicurezza Esterna
⁹ Agenzia Informazioni e Sicurezza Interna
¹⁰ Serviciul de Informații și Securitate al Republicii Moldova
¹¹ Agencja Wywiadu
¹² Służba Kontrwywiadu Wojskowego
¹³ Centro Nacional de Inteligencia
¹⁴ Nationellt Cybersäkerhetscenter
¹⁵ MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE D3FEND is a trademark of The MITRE Corporation.

Acknowledgements

The authoring agencies acknowledge the contributions to this advisory from Palo Alto Networks Unit 42 and Proofpoint.

Disclaimer of endorsement

The information and opinions contained in this document are provided "as is" and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

Organizations have no obligation to respond or provide information back to the authoring organizations in response to this joint advisory. If, after reviewing the information provided, an organization decides to provide information to the authoring organizations, reporting must be consistent with all applicable laws and policies.

Purpose

This document was developed in furtherance of the authoring agencies’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders.

Contact

Appendix A: MITRE ATT&CK tactics and techniques

See Table 9 through Table 19 for all the threat actor tactics and techniques referenced in this advisory.

Appendix B: MITRE D3FEND countermeasures

See Table 20 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

The post Vulnerability Scanning with OpenVAS 9 part 1: Installation & Setup appeared first on Hacking Tutorials.

While app performance is often equated with a smooth UI and fast start times, memory serves as the silent foundation upon which these visible metrics are built. It's no secret that we're seeing a shift where device memory is more important than ever. Not only have we made strides in Android memory optimizations with Android 17, we're providing the tooling and API support to help you stay ahead of stricter memory requirements later this year.

To ensure device stability, starting in Android 17, the system will begin enforcing app memory limits based on the device's total RAM. If an app exceeds those limits, Android will kill the process with no associated stack trace.

Optimize image loading

Detect and fix memory leaks with Android Studio

Memory leaks in Android occur when your code holds onto an object's reference long after its lifecycle has ended. This prevents the Garbage Collector (GC) from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError (OOM).

Android Studio Panda 3 features a dedicated LeakCanary profiler task, allowing developers to analyze real-time memory leaks and map traces within the IDE.

The LeakCanary profiler task in Android Studio actively moves the memory leak analysis from your device to your development machine, resulting in a significant performance boost during the leak analysis phase as compared to on-device leak analysis.

Additionally, the leak analysis is now contextualized within the IDE and fully integrated with your source code, providing features like go to declaration and other helpful code connections that drastically reduce the friction and time required to investigate and fix memory leaks.

Trim memory when app leaves visible state

Android can reclaim memory from your app or stop your app entirely if necessary to free up memory for critical tasks, as explained in Overview of memory management. Android will usually reclaim memory from your app when it’s not visible to the user, such as by discarding some of your app’s code and data pages in memory or compressing your heap allocations. When the user resumes your app and your app tries to access some memory that’s been reclaimed, the OS will swap that memory back in on demand. This swapping behavior can be slow, and cause unexpected jank or stutters in your app.

If you leave it to the OS to decide what memory to reclaim from your app, you may find that the OS reclaimed memory that you’ll need shortly after resuming your app. Instead, your app can voluntarily discard memory allocations that it can regenerate later, on demand and at a low cost. To do so, you can implement the ComponentCallbacks2 interface. You can implement onTrimMemory in your Activity, Fragment, Service, or even your custom Application class. Using it in the Application class is highly effective for global cache management.

The provided onTrimMemory() callback method notifies your app of lifecycle or memory-related events that present a good opportunity for your app to voluntarily reduce its memory usage.

In terms of memory lifecycle management, your implementation should focus exclusively on TRIM_MEMORY_UI_HIDDEN and TRIM_MEMORY_BACKGROUND. Since Android 14, the system has ceased delivering notifications for other legacy constants, which were formally deprecated in Android 15.

TRIM_MEMORY_UI_HIDDEN: This signal indicates that your application's UI has transitioned out of the user's view. This provides an opportunity to release substantial memory allocations tied strictly to the interface—such as Bitmaps, video playback buffers, or complex animation resources.

TRIM_MEMORY_BACKGROUND: At this level, your process is residing in the background and is now a candidate for termination to satisfy the system's global memory needs. To extend the duration your process remains in the cached state, and reduce the number of app cold starts, you should aggressively release any resources that can be easily reconstructed once the user resumes their session.

import android.content.ComponentCallbacks2
// Other import statements.

class MainActivity : AppCompatActivity(), ComponentCallbacks2 {

    /**
     * Release memory when the UI becomes hidden or when system resources become low.
     * @param level the memory-related event that is raised.
     */
    override fun onTrimMemory(level: Int) {

        if (level >= ComponentCallbacks2.TRIM_MEMORY_UI_HIDDEN) {
            // Release memory related to UI elements, such as bitmap caches.
        }

        if (level >= ComponentCallbacks2.TRIM_MEMORY_BACKGROUND) {
            // Release memory related to background processing, such as by
            // closing a database connection.
        }
    }
}

Note: The onTrimMemory integration may depend on SDK support. For instance, certain games rely on their game engine to enable this capability. Please check out the game memory optimization documents.

Advanced memory observability with ProfilingManager

To catch and diagnose memory issues in the field that cannot be reproduced locally, you should leverage the ProfilingManager API. Introduced in Android 15, this advanced observability API allows you to programmatically collect real-user Perfetto profiles.

For teams that lack a dedicated infrastructure to manage and host performance artifacts, Crashlytics is exploring a specialized solution to streamline this workflow. They are inviting developers to provide feedback.

Android 17 introduces new event-driven triggers, most notably TRIGGER_TYPE_OOM and TRIGGER_TYPE_ANOMALY:

• The OOM trigger automatically collects a Java heap dump at the exact moment an OutOfMemoryError crash occurs, providing precise allocation states. A collected OOM profile is provided the next time the app starts and registers the registerForAllProfilingResults callback.
• The Anomaly trigger detects severe performance issues, such as excessive binder spam or breached memory thresholds. The memory anomaly delivers a heap dump just prior to the system terminating the app.

  val profilingManager = 
applicationContext.getSystemService(ProfilingManager::class.java)
    val triggers = ArrayList()  


    triggers.add(ProfilingTrigger.Builder(
                 ProfilingTrigger.TRIGGER_TYPE_ANOMALY))
    val mainExecutor: Executor = Executors.newSingleThreadExecutor()
    val resultCallback = Consumer { profilingResult ->
        if (profilingResult.errorCode != ProfilingResult.ERROR_NONE) {
            // upload profile result to server for further analysis          
            setupProfileUploadWorker(profilingResult.resultFilePath)
        } 

    profilingManager.registerForAllProfilingResults(mainExecutor, resultCallback)
    profilingManager.addProfilingTriggers(triggers)

Once you’ve collected the heap dump, you can download the profile from the server, or locally via adb pull and drag and drop the file into the Perfetto UI. To streamline your memory debugging workflow, use the Heap Dump Explorer, this is the new default view for heap dumps in Perfetto UI. This tool provides an intuitive interface for inspecting Java heap dumps, allowing you to visualize object allocation hierarchies, compute retained memory sizes, and identify the shortest path from garbage collection root. By leveraging the Heap Dump Explorer, you can rapidly pinpoint memory leaks, bloated retained objects such as excessive bitmap allocations, and analyze heap object allocations all in one place.

Conclusion

Optimizing bytecode with R8, adopting image loading best practices, and resolving memory leaks are critical steps toward delivering a high-quality user experience while managing resources effectively under pressure. Adopting these proactive measures helps maintain app stability and performance, preventing unexpected terminations while safeguarding user context. To further your performance expertise, explore our revised memory guidance.

Posted by Alice Yuan, Developer Relations Engineer at Google, Arti Arutiunov, Product Manager at Datadog and Nikita Ogorodnikov, Staff Software Engineer at Datadog

Performance regressions are notoriously hard to reproduce, making regressions a massive bottleneck for mobile developers. Although signals like ANR rates indicate what issues occur in production, pinpointing the specific line of code that resulted in the performance issue has historically necessitated exhaustive manual reproduction or speculative trial-and-error experimentation.

Datadog collaborated with Google to mitigate this frustration by integrating the ProfilingManager API (available on Android 15+ devices) into its Real User Monitoring (RUM) and Continuous Profiling platforms. This integration transforms the debugging workflow, allowing developers to move beyond surface-level symptoms to being able to detect the why behind a performance bottleneck.

The impact of ProfilingManager

ProfilingManager is a system service introduced in Android 15 that enables apps to programmatically collect performance data such as call stack samples, field traces and memory heap dumps directly from production environments. This capability shifts the engineering paradigm from reactive manual reproduction to proactive field analysis.

Solving the Android code-level visibility challenge

Prior to the implementation of ProfilingManager, Datadog’s Real User Monitoring (RUM) focused on high-level application health and session-level telemetry to assess the user journey. Engineering teams could monitor Android performance signals like time to initial display, ANR rates, CPU load, and frozen frames. These insights extended to granular interactions, such as network latency, touch events, and main thread hangs. However, while this data effectively highlighted which performance bottlenecks were surfacing in the field, it provided no clear path to identifying the root cause of these failures.

To address this, Datadog needed a profiling engine capable of capturing Android traces directly from devices in production with minimal performance impact. After evaluating alternative approaches, such as writing their own trace processor using Android Debug APIs, the team selected ProfilingManager because it is the most performant solution of the profiling options they evaluated and offloads the sampling decisions overhead to the OS.

ProfilingManager supports a wide range of collection methods, including CPU traces, call stack sampling, memory analysis through Java heap dumps and native heap profiles. It enables developers to profile production builds, upload trace files to external storage, and review them in the Perfetto trace analyzer UI. As a SaaS provider, Datadog uploads, visualizes, and analyzes these profiles collected via its SDK, providing a unified view of application health.

• Granular session diagnostics: ProfilingManager enhances debuggability by delivering direct OS-level trace data, overcoming the visibility and alignment challenges typical of custom logging with system services. To dive deeper, developers can download these traces from Datadog to investigate further in visualization tools like the Perfetto UI.
• Automated telemetry triggers: By leveraging native system events to initiate trace recordings at key optimization points, Datadog reduces the need to build custom collection logic. While the initial rollout focuses on the APP_FULLY_DRAWN signal, there are already plans to expand this observability to include ANR, OOM, and COLD_START triggers.
• Proactive trace snapshots: By interfacing directly with the system-level Perfetto service (traced), ProfilingManager utilizes a proactive background recording model designed to capture unpredictable issues. This ensures that developers receive a precise visualization of the events leading up to a performance anomaly, offering a level of insight that exceeds what is possible through manual instrumentation.
• Bottleneck detection at scale: Datadog is able to synthesize telemetry from across Datadog’s global customer base to uncover regressions that only emerge under unique hardware configurations and variable network environments.
• System-enforced resource stability: The API leverages sampling trace collection to ensure performance and user experience impacts remain unnoticeable.
• On-device data controls: ProfilingManager filters out irrelevant information from other processes on-device before the profile is delivered to the app. This minimizes file sizes and ensures that only data relevant to the app's processes is provided.

Processing millions of weekly profiles to optimize real-world apps

Conclusion

By integrating Android’s ProfilingManager API, Datadog successfully closed the visibility gap between backend systems and mobile client applications for their customers. By processing millions of profiles weekly with negligible device overhead, Datadog equips Android developers with the code-level insights necessary to diagnose complex performance bugs instantly, helping developers build smoother applications and improve their app’s performance signals in the Play Store. To adopt the ProfilingManager API directly into your performance observability framework, check out our documentation.

In the future, Datadog aims to make Android profiling data a first-class input for coding agents to autonomously resolve performance bottlenecks, closing the feedback loop between detection and remediation. Datadog is working toward making Android profiling broadly accessible to developers.

To get started using the Datadog real user monitoring feature powered by ProfilingManager, visit Datadog Mobile Real User Monitoring.

Every year, Google I/O brings new announcements and resources across ecosystems and products, including Android development. As development shifts toward AI and agent-assisted tooling, we’ve expanded our offerings to better support you, however you decide to build for Android.

To help you stay up to date, here is a summary of the top 3 announcements for Android Developer Productivity at I/O.

1. Android CLI is now stable

Android CLI is now stable at version 1.0, with more capabilities and integrations.

The latest version of Android CLI introduces many new features, like programmatic version lookup and support for Journeys, and bridging capability to allow agents to integrate directly with Android Studio, via the studio command.

Running Android Studio alongside the agent and Android CLI enables more efficient navigation in your project, more precise output, and access to Android Studio’s unique tooling, such as performance profilers, Compose Previews, and Android Device Streaming.

Android CLI now integrates seamlessly with Android Studio

Additionally, Google Antigravity now officially supports Android development, with the Android resources bundle, which includes the Android CLI and skills.

You can either install the bundle during onboarding after installation, or later from the Settings > Customizations > Build With Google Plugins menu. This provides Antigravity with all the powerful tools and knowledge of Android CLI to enable it to perform core tasks—from creating projects to deploying your app on a new virtual device—much more easily and efficiently.

Google Antigravity now offers the Android resources bundle

Android CLI is now available through more package managers: like npm and homebrew. For more information, check out the Android CLI blog post and official documentation.

2. Android skills keep growing

To help models gain expertise for specific development patterns that follow our best practices, we are continuing to expand our repository of Android skills, available through Android CLI and GitHub.

Android skills ground LLMs in specialized workflows and domain knowledge, for the most common and more complex user journeys they might struggle with. We’ve shipped a fresh new batch of skills, with now more than 17 skills for areas such as:

• Adaptive UI
• Display Glasses and Jetpack Compose Glimmer for XR
• Migration to CameraX
• Perfetto SQL and Trace Analysis
• Jetpack Compose Styles API
• AppFunctions
• Verified email retrieval with Android Credential Manager
• Engage SDK integration
• Testing setup
• Wear OS Jetpack Compose Material3

Android skills keep growing

You can browse skills and install using the Android CLI commands:

android skills list
android skills add –skill=<skill-name>

For more information, check out the official documentation.

3. Android Bench adds new models

Earlier this year, we launched Android Bench - our leaderboard for testing LLMs on real-world Android development challenges and tasks, with the goal of accelerating model improvements, so you have more helpful options for AI assistance.

Latest results from Android Bench leaderboard

You asked us to evaluate open models. So, at I/O, we added more commonly used ones, including our local model Gemma 4, to the leaderboard. We also added the latest models including Gemini 3.5 Flash.

We are also working on increasing the difficulty of challenges we’re giving LLMs, including creating long running tasks, to continue encouraging improvements. These tasks will be coming soon to Android Bench. Check out the Android Bench leaderboard to see the latest results.

Android development anywhere

By expanding our AI-assisted Android development offerings to Antigravity, through Android CLI and Android skills, and solidifying with the pro capabilities and production grade polish of Android Studio, we’re supporting Android developers wherever they choose to build.

Have fun bringing your ideas to life faster and easier than ever before - we’re excited to see what you build in this new era of agentic development.

Check out the full Developer productivity at Google I/O 2026 YouTube playlist for more information.

Posted by Ben Weiss, Senior Developer Relations Engineer, Android Developer Relations

Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post, we explored how to leverage Firebase AI Logic to build cloud-hosted and hybrid AI features.

In this article, we'll show you how we designed and integrated these capabilities into our travel planning app, JetPacker, using Android AppFunctions. We'll explore the rationale behind our feature choices, discuss the specialized tooling we used to accelerate development, and dive into the code that makes it all work.

Designing AI-ready features: making choices that matter for your users

To select which features to provide to the intelligence system, we looked for tasks where a voice or text command is objectively faster than tapping through screens. In this side-by-side screen recording you can see this contrast perfectly: on the left, a user tapping through multiple screens to log an expense; on the right, the same task completed instantly in the background via a privileged agent.

Our first choice was expense tracking. Logging a coffee expense during a trip usually takes quite a few taps—unlocking the phone, opening the app, finding the active trip, navigating to the expenses tab, tapping the add button, taking a picture of the receipt, and checking the result. By providing the addExpense and getExpenses features as AppFunctions, the system agent handles the heavy lifting. When the user says, "Add a five-dollar coffee expense to my Paris trip," the agent automatically searches for the correct trip ID in the background and inserts the expense, skipping the manual UI flow entirely.

We also prioritized itinerary management. Finding what activity is next on a busy trip itinerary usually requires scrolling through a dense timeline view. By providing getItinerary and addItineraryEvent to the system, the user can simply ask, "What am I doing next in Paris?" and get an immediate answer.

Finally, we focused on hands-free note capturing. Typing out reminders or notes while walking down a busy street is difficult and unsafe. Exposing a voice note capability allows the user to say, "The flight was amazing, I saw a beautiful sunset and managed to sleep well," and the privileged agent automatically transcribes and saves it directly into the travel database  using the addVoiceNote AppFunction.

Android MCP powered by AppFunctions

This entire experience is built on Android MCP. Under this design, the app acts as a local MCP server. Rather than remote APIs, you provide your app features directly to the on-device intelligence system.

Android AppFunctions is the API that brings this concept to life. It reads annotated Kotlin functions and compiles them into type-safe, sandboxed tool definitions that the privileged agent can discover and invoke locally on the device.

Diagram highlighting our apps, the android platform, and system agents coordinate AppFunctions.

Under the Android MCP model, your app acts as a local MCP server that exposes structured tools, while the Android platform serves as the central tool registry. On the MCP client side, agent apps are registered with the intelligence system after being granted system-privileged permissions to access the registry.

When a user interacts with a registered agent, its LLM determines if the request can be handled by an AppFunction, queries the platform's metadata, and executes the appropriate registered functions in the background. This local MCP client-server design gives you full control: you choose exactly which features are accessible to the agent, keeping the rest of your app's data private.

How we accelerated development with Android skills

To streamline the integration process, we leveraged the AppFunctions development skill. The AppFunctions development skill is a complete development companion. It guided us through the entire lifecycle: mapping Kotlin data classes to serialize parameters, generating the necessary Service entry points, refining our KDoc documentation to ensure the LLM understands parameter boundaries, and setting up automated testing using ADB.

Providing app features to the intelligence system

Enough with the theory, let's dive into the implementation.

Configuration and dependency setup

We begin by adding the AppFunctions dependencies. One for the API and one for the Kotlin Symbol Processing compiler.

implementation("androidx.appfunctions:appfunctions:1.0.0-alpha10")
ksp("androidx.appfunctions:appfunctions-compiler:1.0.0-alpha10")

Modeling custom data types

Any custom object exchanged with the agent must be annotated with @AppFunctionSerializable. In our TripSerializable.kt file, we define our trip data model:

@AppFunctionSerializable(isDescribedByKDoc = true)
data class TripSerializable(
    /** The trip's unique identifier. */
    val id: String,
    /** The trip's title. */
    val title: String,
    /** The trip's destination location. */
    val location: String,
    /** The trip's start date in milliseconds. */
    val startDate: Long,
    /** The trip's end date in milliseconds. */
    val endDate: Long,
    /** A list of participants. */
    val participants: List<String>,
)

Providing features using the @AppFunction annotation

Next, the skill wrote the Kotlin functions that perform the database queries and annotate them with @AppFunction. We can view this in searchTrip:

/**
 * Looks for trips based on optional filters like id, title (name), location, and dates.
 *
 * @param id The unique identifier of the trip.
 * @param title The title or name of the trip.
 * @param location The destination location.
 * @param startDate The minimum start date in milliseconds.
 * @param endDate The maximum end date in milliseconds.
 * @return A list of trips matching the filters.
 */
@AppFunction(isDescribedByKDoc = true)
suspend fun searchTrip(
    id: String? = null,
    title: String? = null,
    location: String? = null,
    startDate: Long? = null,
    endDate: Long? = null
): List<TripSerializable> {
    return withContext(Dispatchers.IO) {
    // implementation
}

Since AppFunctions run on the UI thread by default, we use withContext(Dispatchers.IO) to switch to a background dispatcher. Additionally, we refine our KDoc to use clear, imperative verbs and specify parameter constraints. This documentation compiles directly into the tool's schema, which the privileged agent uses to resolve parameters and handle runtime errors.

The service entry point and Hilt integration

To register these features with the intelligence system, we create an abstract base class that extends AppFunctionService. We annotate it with @AppFunctionServiceEntryPoint:

@RequiresApi(36)
@AndroidEntryPoint
@AppFunctionServiceEntryPoint(
    serviceName = "JetPackerAppFunctionService",
    appFunctionXmlFileName = "jetpacker_app_function_service"
)
abstract class BaseJetPackerAppFunctionService : AppFunctionService() {
    @Inject internal lateinit var tripDao: TripDao
    // DAOs and database references are injected here...
}

During compilation, KSP generates the final concrete service subclass, JetPackerAppFunctionService, as declared with the serviceName parameter. We also register app_metadata.xml in the app's manifest. This file provides global operational rules for JetPacker's declared AppFunctions.

Testing and verifying your AppFunctions

Once implemented, you should verify that your AppFunctions are registered and working correctly.

Running devices or emulators with Android 17 or newer, you can use ADB commands from your terminal to list and invoke your functions. Running adb shell cmd app_function list-app-functions displays all registered functions for your package. You can then execute a specific function and test its database integration by running adb shell cmd app_function execute-app-function while passing a raw JSON parameters string.

Instead of these ADB commands, you can also use the AppFunctions Testing Agent to inspect your configuration, list and execute AppFunctions, and even see how your AppFunctions behave in a real conversational flow.

Wrapping it up

When thinking about app features that can be contributed to the intelligence system using AppFunctions requires a slight shift in how we think about code and documentation. AppFunctions enable you to use this new interaction model for apps, which allows using an agent to access app features..

First, the AppFunctions development skill is an essential lifecycle tool, helping you discover features, implement and refine AppFunctions for your apps. Second, KDoc comments are a compiled API asset; clear parameter descriptions directly impact the execution accuracy of the system agent. Finally, Android MCP provides local-first execution allowing apps to safely collaborate with AI agents.

Contributing app features through AppFunctions makes your application ready for the intelligence system. Let us know how you are adapting your apps for the agentic era!

Learn more

Check out the other parts of this blog post series:
Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4 (this post!): System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

Read more of this story at Slashdot.

In the February meeting, the Bolt Design System Committee aligns on initial setup and ownership for the Bolt Design System repo, began defining cross-platform icon naming conventions, plus works towards component exposure in the documentation site.

Have any questions or want to join the conversation? Come find us in our Matrix room (https://matrix.to/#/#bolt-design-system:mozilla.org). New to Matrix? Find a Quick Start guide here: https://matrixdocs.github.io/docs/getting-started/quick-start

If you have a previous version of Rust installed via rustup, you can get 1.96.0 with:

$ rustup update stable

If you don't have it already, you can get rustup from the appropriate page on our website, and check out the detailed release notes for 1.96.0.

If you'd like to help us out by testing future releases, you might consider updating locally to use the beta channel (rustup default beta) or the nightly channel (rustup default nightly). Please report any bugs you might come across!

What's in 1.96.0 stable

New Range* types

Many users expect Range and related core::ops types to be Copy, but this is not the case: they implement Iterator directly, and it is a footgun to implement both Iterator and Copy on the same type so this has been avoided. RFC3550 proposed a set of replacement range types that implement IntoIterator rather than Iterator, meaning they can also be Copy. The standard library portion of that RFC is now stable, introducing:

• core::range::Range
• core::range::RangeFrom
• core::range::RangeInclusive
• Associated iterators

A Rust version in the near future will also add core::range::RangeFull and core::range::RangeTo as re-exports from core::ops (these do not implement Iterator and already implement Copy), and core::range::legacy::* as the new home for the current ranges. Range syntax like 0..1 still produces the legacy types for now, but will be updated to core::range types in a future edition.

With these stabilizations, it is now possible to store slice accessors in Copy types without splitting start and end:

use core::range::Range;

#[derive(Clone, Copy)]
pub struct Span(Range<usize>);

impl Span {
    pub fn of(self, s: &str) -> &str {
        &s[self.0]
    }
}

The new RangeInclusive also makes its fields public, unlike the legacy version which avoided exposing the exhausted iterator state. This isn't a concern with the new type since it must be converted to begin iteration.

Library authors should consider making use of impl RangeBounds in public API, which accepts both legacy and new range types. If a concrete type is needed, prefer using new ranges as this will eventually become the default.

Assert matching patterns

The new macros assert_matches! and debug_assert_matches! check that a value matches a given pattern, panicking with a Debug representation of the value otherwise. These are essentially the same as assert!(matches!(..)) and debug_assert!(matches!(..)), but the printed value improves the possibility of diagnosing the failure.

These new macros have not been added to the standard prelude, because they would collide with popular third-party crates that provide macros with the same name. Instead, they should be manually imported from core or std before use.

use core::assert_matches;

/// [Random Number](https://xkcd.com/221/)
fn get_random_number() -> u32 {
    // chosen by a fair dice roll.
    // guaranteed to be random.
    4
}

fn main() {
    assert_matches!(get_random_number(), 1..=6);
}

Changes to WebAssembly targets

WebAssembly targets no longer pass --allow-undefined to the linker which means that undefined symbols when linking are now a linker error instead of being converted to WebAssembly imports from the "env" module. This change prevents modules from linking unless all linking-related symbols are defined to catch bugs earlier and prevent accidental issues with symbol naming or similar.

Undefined linking-related symbols are often indicative of build-time related bugs or misconfiguration. If, however, the old behavior is intended then it can be re-enabled with RUSTFLAGS=-Clink-arg=--allow-undefined or by editing the source code and using #[link(wasm_import_module = "env")] on the block defining the symbol.

This change was previously announced on this blog, and now takes effect in Rust 1.96.

Stabilized APIs

• assert_matches!
• debug_assert_matches!
• From<T> for AssertUnwindSafe<T>
• From<T> for LazyCell<T, F>
• From<T> for LazyLock<T, F>
• core::range::RangeToInclusive
• core::range::RangeFrom
• core::range::RangeFromIter
• core::range::Range
• core::range::RangeIter

Two Cargo advisories

Rust 1.96 contains fixes for two vulnerabilities for users of third-party registries.

• CVE-2026-5223 is a medium severity vulnerability regarding extraction of crate tarballs with symlinks.

• CVE-2026-5222 is a low severity vulnerability regarding authentication with normalized URLs.

Users of crates.io are not affected by either vulnerability.

Other changes

Check out everything that changed in Rust, Cargo, and Clippy.

Contributors to 1.96.0

Many people came together to create Rust 1.96.0. We couldn't have done it without all of you. Thanks!

• James enabled adaptive autofill in Nightly for testing, which we believe should provide better results in the URL bar when doing autocomplete!
• Jack updated the illustrations shown on some of our error pages to match the latest approved designs, giving users more polished artwork when the browser encounters connection or security errors!

• Controls for the Memories feature can now be set during Smart Window onboarding

• We’ve disabled the CSS filter implicitly applied to WebExtension pageAction SVG icons across all release channels starting in Firefox 152, completing the deprecation
  • NOTE: The blog post published at WebExtensions API changes in Firefox 149-152 provides to extensions developers more details about this deprecation and links to the related MDN docs.

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

• Amin Amir
• Pranjali Srivastava
• Sam Johnson

New contributors (🌟 = first patch)

•  🌟:23rd: Regression: The new swipe-to-navigation indicator stucks for a moment, when deciding not to navigate the other page
• 🌟Akeem Omosanya: Remove commented-out code in SearchService.sys.mjs
• Amin Amir:
  • 🌟Fix browsingContext.sys.mjs to assign to #contextCreatedHandled instead of contextCreatedHandled
  • Fix missing WITHOUT ROWID SQLite performance optimization in SERPCategorization.sys.mjs
• 🌟Sahaj: Suggest the default target language for translation after changing the detected source language
• 🌟JIANG Zhirui: Breakpad build failed on Windows using VS2026 due to removal of stdext
•  John Iweh: Add “Open in New Tab” and “Open in New Container Tab” options to the context menu for Tabs from Other Devices
• Jak: Bookmarks and History – should respect the “When you open a link, image or media in a new tab, switch to it immediately” setting
• 🌟Andy [:rgbcmy]: Autoplayed next video should also be PIP
•  konyhéa: “Escape” key should collapse the expanded on hover sidebar launcher even if hover is still active.
•  Pranjali Srivastava:
  • Remove icon property from sidebar extensions
  • Show language-agnostic SelectTranslations context menu item when the source and target languages are the same

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons

• Fixed long-standing regression on the autocomplete and datalist popups for extension inline options pages on about:addons (introduced in Firefox 68 by Bug 1532724, fix shipping in Firefox 152) – Bug 1595158

WebExtensions Framework

• Fixed access to web-accessible resources declared with <all_urls> from sandboxed documents (null-principal URLs), restoring extension redirects from the context-menu search flow, starting in Firefox 152 – Bug 2033905

WebExtension APIs

• Added exhaustive test coverage for tabs.move() against additional edge cases related to split-view tabs – Bug 2029092

DevTools

• Andreas Farre improved the Session History tab in the Application panel (still behind devtools.application.sessionHistory.enabled)
  • added support for remote debugging (#2014064, #2016121)
  • made sure that calls to History.replaceState are reflected in the UI (#2037359)
• Julian Descottes [:jdescottes] fixed the most frequent DevTools crash we were observing in Telemetry, adding a guard against IDBTransaction errors when retrieving breakpoints in the Debugger (#2030260)
• Nicolas Chevobbe [:nchevobbe] fixed the image preview tooltip for relative URLs images in constructed stylesheet (#2035503)
• Julian Descottes [:jdescottes] reduced the overhead we had because of network requests monitoring by only decoding response content when the user actually want to see the response (#2026228)

WebDriver

• Amin Amir cleaned up an incorrect variable assignment in our browsingContext module.
• Logan Rosen updated stale references and broken links in our documentation about Marionette.
• Sameem improved the Marionette and WebDriver BiDi screenshot commands to enforce maximum allowed dimensions.
• Leo McArdle fixed the regression in the “log.entryAdded” event, which lacked an error message in the “text” field for the messages of type “error”.
• Henrik Skupin fixed an issue in Marionette where WebDriver:Navigate and WebDriver:Refresh did not handle errors when the underlying navigation failed.
• Henrik Skupin improved geckodriver to detect an early Firefox exit during startup on Android, avoiding up to 60 seconds of unnecessary connection attempts.
• Henrik Skupin updated the geckodriver CI build job to produce a universal macOS binary supporting both x64 and aarch64.

Lint, Docs and Workflow

• Sylvestre ported some linters (e.g. file-whitespace, test-manifest-toml, license, file-perm, rejected-words & more) to Rust to help improve the runtime of the code review bot.
• Dale has been working on migration to moz-src for customkeys, dom/quota and odom/geolocation
  • https://arewemozsrcyet.com/

New Tab Page

• We did our first region-specific trainhop on May 11th (just 15% of the US), and turned on HNT Nova (and sometimes Widgets) for those clients to get some advance-data of its behaviour in the wild! A note that HNT Nova gets turned on for everybody when Firefox 151 ships on May 19th.
  • We’ll be launching a similar experiment in the DE, probably on May 12th, also at 15% population.
• Most of the team is heads down building out a sports-tracking widget, attempting to get that ready in time to be generally available for the upcoming World Cup event.
• Dre landed a new world clock widget, which is currently off by default, but pretty snazzy!

Search and Urlbar

• Nova (URL Bar Design Refresh)
  • Drew and Daisuke continued their work on Nova styling for the Address bar (input and view).
• Search and Suggest
  • Drew finalized two bugs for World Cup and sports suggestions, which were landed and uplifted: one to update the localization string for scheduled games and another to show both teams’ icons in suggestions. Drew also landed and uplifted a fix for rich search suggestion icons being forced into a square aspect ratio.
  • Standard8 updated Ecosia favicons to the latest branding, including QA testing and publishing.
• Settings Redesign (SRD)
  • Stephanie landed a test to ensure search suggestion settings are hidden when quicksuggest is disabled, as well as a patch to resolve TypeScript issues in search.mjs, and is adding test coverage to confirm removed search engines are not displayed in the default engines dropdown.
• General URL Bar and Component Updates
  • Daisuke landed implementation of the context menu on URL bar results, and a fix to show the loading URL in the URL bar when starting up with a homepage.
    • Marco is working on several tasks, including a PDF download / focus stealing issue and allowing arrays to be bound in Sqlite.sys.mjs. Marco also worked on fixes related to Places, such as avoiding replacing the favicons database if it is not corrupt.
  • Standard8 finalized the URL bar test manifest split. Standard8 also upgraded us to TypeScript 6.
  • Moritz landed a fix for URL bar abandonment telemetry being recorded when clicking an engine in the unified search button popup (Bug 2032973), which was also uplifted. Moritz also simplified search mode switcher item activation in tests, and made it so that the unified search button popup closes when installing an open search engine.

Smart Window

• natural language starting with tab close/undo 2035343 with expandable action log 2031508

• assistant rendering feedback up/down 2032994 and markdown table 2027029
• nova styling blur 2027877 and suggestions 2026823
• accessibility screen reader 2028676 and keyboard focus 2037565
• optimize conversation starters extra requests 2030005 and caching 2033430

Storybook/Reusable Components/Acorn Design System

• Nova token updates occasionally, focused on SRD

UX Fundamentals

• Added support for the “SEC_ERROR_CA_CERT_INVALID” certificate error to the Felt Privacy error pages. – 2035942

Settings Redesign

• Settings redesign is being tested and will hopefully go out in Firefox 152!

• Dao added a new Eyedropper quick action! Check it out by typing “color” or “eyedropper” in the URL bar (Bug 1803575) on Nightly.

• Henrik Skupin released geckodriver 0.37.0, which includes support for several new APIs and various bug fixes. See the release page for details.
• Starting from Firefox 153, access to local file: URLs is being restricted by default.
  • Extensions now require an explicit “Access local files on your computer” permission, separate from broad host permissions, that users must grant.
  • Extensions can call the extension.isAllowedFileSchemeAccess() API to determine whether they have been granted access to file: URLs (Bug 2034168).

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

• :Vincent
• Chris Vander Linden
• DrSeed
• Khalid AlHaddad
• Sam Johnson

New contributors (🌟 = first patch)

• Francis :mckenfra: tld service for webextensions
• any1here: about:preferences#privacy is broken with MOZ_DATA_REPORTING false
• pullmana8: Fix protocol/Actor.js to throw an Error instead of an Actor
• RAN1: Firefox Crashes on Quit When Running Two Browsers With Separate Profiles

Project Updates

Accessibility

• Morgan added a new accessibility-specific, front-end review skill to mozilla central! 🎉 You can read about it, and learn how to use it in the accessibility review source docs.

Add-ons / Web Extensions

Addon Manager & about:addons

• Migrated addon-page-header and addon-card action buttons to the reusable moz-button web component as part of the ongoing Nova restyling of about:addons – Bug 2042200 / Bug 2042204
• Extended moz-page-nav-button with a forwarded title property to fix an accessibility issue where the component lacked a label in collapsed state; Landed in Firefox 153, and uplifted to Firefox 152 for about:settings which was already riding the 152 release train – Bug 2040971
• Fixed a shutdown-timing bug where a pending GMP update-check timer could fire after XPCOMShutdownThreads started, causing a pref write assertion; Fixed in Firefox 153 – Bug 2043803

WebExtensions Framework

• Fixed MV2 content scripts incorrectly injecting into guarded hosts because MozDocumentMatcher::MatchesURI was not consulting CheckGuarded when mCheckPermissions was false; Fixed in Firefox 153 – Bug 2041393
• Added support for accessing ObservableArray attributes (such as adoptedStyleSheets) from XrayWrappers and extension content scripts, unblocking extensions that rely on this Web API; Fixed in Firefox 153 – Bug 1751346
• Wired runtime_blocked_hosts and runtime_allowed_hosts enterprise policy settings through ExtensionSettings to allow administrators to restrict extension host permissions on managed devices, starting in Firefox 153 – Bug 1805205
  • Thanks to Mike Kaply for implementing this enterprise policy enhancement.

WebExtension APIs

• Fixed promiseTabWhenReady blocking indefinitely on discarded tabs, preventing cleanup of associated resources and potentially causing memory leaks; Fixed in Firefox 153 – Bug 1653876
• Fixed webNavigation.onCommitted being dispatched twice for cross-origin iframes loaded under Fission, caused by a redundant OnStateChange trigger firing in addition to OnLocationChange; Fixed in Firefox 153 – Bug 1750196

DevTools

• Chris Vander Linden made the Search input component shared as we plan to use it in the Netmonitor as well (#2019260, #2027580, #2027582)
• pullmana8 improved error management in the DevTools protocol (#2031735)
• Chris H-C :chutten removed Legacy Telemetry devtools instrumentation (#2039650)
• :glob ✱ fixed an issue in the Inspector where the swatch color for variable in @starting-style rule could have the wrong color (#2016778)
• Nicolas Chevobbe [:nchevobbe] exposed heading level more clearly in the accessibility tree (#1588784) and in the accessibility highlighter (#2044904)

• Julian Descottes [:jdescottes] migrated the markup view to HTML (from XHTML) to fix an issue when editing the markup (CodeMirror 6 does not support XHTML) (#2028058)
• Nicolas Chevobbe [:nchevobbe] fixed an issue in Netmonitor search where it could appear the the search stalled (#2042405)
• Hubert Boma Manilla (:bomsy) added more connection information (ECH, Delegated Credentials, OCSP, Private DNS, …) in Netmonitor Security tab (#2036404)

WebDriver

• Khalid AlHaddad improved the window manipulation commands in Marionette and WebDriver BiDi to allow individual window geometry properties, such as x, y, width, and height, to be adjusted independently.
• Khalid AlHaddad updated our codebase to use constants instead of hardcoded strings for all our session data types.
• Alexandra Borovova updated the “emulation.setLocaleOverride” command to also apply a locale emulation in dedicated and shared workers.
• Alexandra Borovova fixed a regression when there would be no “script.realmCreated” events after the cross-origin navigation.

Search and Urlbar

• Dharma updated context search actions to trigger search instead of entering search mode @ 1945080
• Daisuke and Drew worked on a lot of Nova updates, including ensuring Nova is tested @ 2041255, 2030183, 2019168, 2044849, 2033583
• Moritz has worked on several refactorings to allow the urlbar to be used in content @ 2039828, 2039298, 2041280
• Middle click paste replaces content was fixed by Moritz @ 2042893
• Michel added feature to show registrable domain on desktop after its implementation on mobile @ 1986161

If you have a previous version of Rust installed via rustup, you can get 1.97.0 with:

$ rustup update stable

If you don't have it already, you can get rustup from the appropriate page on our website, and check out the detailed release notes for 1.97.0.

If you'd like to help us out by testing future releases, you might consider updating locally to use the beta channel (rustup default beta) or the nightly channel (rustup default nightly). Please report any bugs you might come across!

What's in 1.97.0 stable

Symbol mangling v0 enabled by default

When Rust is compiled into object files and binaries, each item (functions, statics, etc) must have a globally unique "symbol" identifying it. To avoid conflicts when linking together different Rust programs, Rust mangles the original name of items to include additional context such as the module path, defining crate, generics, and more. Historically, this mangling was based on the Itanium ABI, also (sometimes) used by C++.

The new mangling scheme resolves a number of drawbacks from the previous one:

• Generic parameter instantiations preserve their values, rather than being tracked solely behind a hash
• Inconsistencies: not all parts used the Itanium ABI, meaning that custom demangling was still necessary

Since Rust 1.59, the compiler has supported opting into a Rust-specific mangling scheme via -Csymbol-mangling-version=v0. Since November 2025, this scheme has been enabled by default on nightly, and 1.97 is now enabling it on stable Rust. The legacy mangling scheme can only be enabled on nightly, and the current plan is to fully remove it.

See the previous blog post for more details.

Cargo support for denying warnings

It's common practice to deny warnings in CI. Historically, doing so is typically done through RUSTFLAGS=-Dwarnings. With Rust 1.97, Cargo controls how warnings interact with build success: either silencing them (via allow level), rendering without failing (default, warn), or denying them (via deny).

As a result of Cargo configuration determining the behavior, using this feature doesn't invalidate the underlying build cache, meaning that it's easy to temporarily opt-in. For example, if warnings are adding unwanted noise while working through fixing errors after a refactor, you can run CARGO_BUILD_WARNINGS=allow cargo check, temporarily silencing them.

In CI, jobs can instead set CARGO_BUILD_WARNINGS=deny to deny warnings. This can be combined with --keep-going to collect all errors and warnings rather than stopping on the first failing package.

See the documentation for more details.

Linker output no longer hidden by default

rustc invokes a linker on behalf of users. Historically, rustc has silenced linker output by default if the link completes successfully. This can mask real problems, though, so in Rust 1.97 we are enabling linker messages by default. These are emitted as a warning lint, for example:

warning: linker stderr: ignoring deprecated linker optimization setting '1'
  |
  = note: `#[warn(linker_messages)]` on by default

Common linker messages that have been diagnosed as false positives or intentional behavior are filtered out by rustc. Several defects have already been fixed as a result of no longer hiding this output on nightly.

Note that currently, linker_messages is a special lint that is not affected by the warnings lint group. This is intentional as rustc generally doesn't control linker output as precisely, and it's not uncommon for output to only appear on some platforms. If you are seeing what you think is a false positive output from the linker, please file an issue.

To silence the warning in the mean time, you can configure the lint level to allow. This can be done through Cargo.toml by adding a lints section like this:

[lints.rust]
linker_messages = "allow"

Stabilized APIs

• Default for RepeatN
• Copy for ffi::FromBytesUntilNulError
• Send for std::fs::File on UEFI
• <{integer}>::isolate_highest_one
• <{integer}>::isolate_lowest_one
• <{integer}>::highest_one
• <{integer}>::lowest_one
• <{uN}>::bit_width
• NonZero<{integer}>::isolate_highest_one
• NonZero<{integer}>::isolate_lowest_one
• NonZero<{integer}>::highest_one
• NonZero<{integer}>::lowest_one
• NonZero<{uN}>::bit_width

These previously stable APIs are now stable in const contexts:

• char::is_control

Other changes

Check out everything that changed in Rust, Cargo, and Clippy.

Contributors to 1.97.0

Many people came together to create Rust 1.97.0. We couldn't have done it without all of you. Thanks!

• The best games of 2026 so far

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From luxurious ergonomic thrones to cheap-and-cheerful bucket seats, the options available are bewildering. So I set out on an epic quest: to find the most comfortable, durable and stylish gaming chairs. Here’s what I discovered.

Best gaming chair overall:
Secretlab Titan Evo

Best budget gaming chair:
ThunderX3 Solo 360

Introduction 

Today, Google Threat Intelligence Group (GTIG) will begin rolling out a unified naming schema for tracking threat actors. This new naming taxonomy represents an effort to standardize tracking across platforms and public reporting.

Why are we Adopting a Different Naming System?

Historically, Mandiant and Google’s Threat Analysis Group (TAG) maintained distinct tracking systems, relying on parallel naming schemas that grew independently over time. The creation of GTIG has necessitated a new, fused tracking system, and a new naming system. Thinking to the future, GTIG’s new system will rely on cryptonyms. Relying on sequential numbers or disparate identifiers (e.g. APT1) fails to provide defenders the critical context needed to operate quickly. Threat tracking shouldn’t be an exercise in memorization, but rather one of intuition. The new naming convention aligns with industry standard threat actor naming systems. 

Our New Schema

Our new schema utilizes a cryptonym-based approach, employing memorable two-word combinations for each distinct threat actor:

• The first word is a unique and memorable term chosen to represent the specific actor, particularly names that may have been used in prior public reporting. If no previously used term exists, this word is randomly generated to remove bias, then vetted by our analysts.

• The second word categorizes threat clusters by motivation, attribution, or activity type based on which category we consider to be most important for defense and response strategies.

The table below provides a sample of how threat actor categories will map to the second word in each cryptonym:

Table 1: Examples of Google’s new threat actor naming system categories

We know there are many threat actor tracking schemas in the industry, so we are intentionally seeking to keep this system as simple as possible to streamline operations and facilitate mapping to other naming taxonomies. However, a significant caveat remains: because no two organizations have the exact same visibility into the threat landscape, direct, apples-to-apples comparisons between threat actors are rarely possible. Transitioning to a convention that is simpler to follow and remember is a practical step toward managing a highly intricate tracking problem. 

A Work in Progress

We have initially prioritized renaming several dozen of the most active groups, and will continue this process on a rolling basis. Previous names will remain indexed and searchable in the Google Threat Intelligence (GTI) platform, with MITRE ATT&CK mappings and other vendor aliases preserved, see Figure 1. 

Der Beitrag Glorious GMMK 3 PRO HE Wireless Keyboard im Test: Der Ferrari unter den kabellosen Tastaturen erschien zuerst auf appgefahren.de.

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Full Changelog: desktop-v0.0.3...desktop-v0.0.4