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.

A few years ago we deployed Matrix with docker-compose. Now, let's give it a go on Kubernetes!

00:00 Introduction
01:40 Kubernetes Makes Things Simple
04:59 Getting Started
06:21 Installing Kubernetes
14:41 Deploying and Removing a Wordpress
20:53 Deploying A Matrix Stack
31:02 More Services Almost For Free

This Week in Matrix: https://matrix.org/twim

Find Matrix.org on the internet:
🐘 https://mastodon.matrix.org/@matrix
🤝 https://www.linkedin.com/company/matrix-org
🦋 https://bsky.app/profile/matrix.org
[m] Office of the Matrix Foundation https://matrix.to/#/%23foundation-office:matrix.org

In this post we walk through what folks have found on their journey to learn the Rust programming language with ups and downs covered.

As a disclaimer, LLMs (Large Language Models) come up in this post because our interviewees brought them up. We're scoping discussion to their use as a learning tool, covering research and example generation, not broader questions about AI (Artificial Intelligence) in software development.

Many paths to needing Rust

The interviews surfaced several different paths into Rust: curiosity, embedded work, job-market pressure, organizational adoption, and reassignment after a team or company chose Rust. That last path matters because many learners are not evaluating Rust from a blank slate; they are trying to become productive after Rust has already arrived in their work.

"Funny enough, I've advocated for more niche languages than Rust in the past. Rust has pretty much stopped being as much of a niche language as it was, but it's not Java." -- Fractional CTO

Rust learning resources

Likely as expected, the folks that we talked to reach for a range of resources to learn Rust. Some reach for official documentation, such as The Rust Programming Language Book and find that sufficient to build on what the compiler was already showing them.

"I started with the official Rust documentation because there are a lot of great examples of how features like the borrow checker work." -- Software engineer at an Automotive supplier

Others needed more passes and more formats, sometimes reaching for resources the community maintains, such as Rustlings, The Little Book of Rust Macros, and Learn Rust With Entirely Too Many Linked Lists.

"The first time I went through the chapter in [The Rust Programming Language] on borrow checking, I was like, what is this? I read it again, then I watched a YouTube video of someone explaining the chapter." -- Rust freelance consultant

"Rust book, Rustlings, Zero to Production in Rust, Jon Gjengset tutorials. A bunch of books. It's not a one-pass reading. Can't say how many times I've gone through it." -- Software engineer working on video streaming and storage

These resources have brought up an entire generation of Rust programmers. But, to some, there is a perception that these resources have trouble keeping pace with the language.

"We'd like to use [The Rust Programming Language/'the book'], but we've found that it's out of date, unfortunately. We've looked at the GitHub repo and found it's got a lot of unresolved issues and unmerged PRs" -- Principal Software Engineering work on Rust adoption in a regulated industry

Whether or not this is factually true, Rust's growth has nonetheless put more scrutiny on these materials. Companies evaluating adoption and engineers getting reassigned to Rust teams are looking at them with fresh eyes and finding the gaps that affect their own evaluation.

Beginner stumblings and unlearning habits

It's pretty typical for Rust to be the 2nd, 3rd or Nth programming language that someone picks up. They'd end up writing their most familiar language in Rust, whether C++ patterns, Java patterns, or whatever they knew, for months or even years. Eventually they got comfortable enough to start writing idiomatic Rust.

"There's a bit of a drop in productivity compared to C if you're already familiar with it just because you're learning new rules, new syntax." -- Principal Firmware Engineer (mobile robotics)

"In the beginning it was more poking around the code and adding and removing some ampersands and asterisks to try to make sense of mut and not mut and whatever." -- Senior engineer with 20 years of Java experience in cloud and IoT

We also spoke with someone who found that not having much of a programming background seemed to benefit people picking up Rust. Not having worn-in grooves from other languages may play a role here, and it's worth investigating further.

"I had someone who had never programmed much before start working on the internals of [our Rust project]. She was just fine with getting into Rust. It's more of the senior people that struggle as they need to unlearn practices which may work in other languages, but it's not the 'Rust' way." -- Researcher, Automotive OEM R&D Lab

Learning to work with the borrow checker

We heard a lot about learning to work with the borrow checker instead of against it. People get there through different paths, but a few patterns came up repeatedly.

The compiler as teacher

Rust's diagnostics did the teaching on their own, especially around lifetimes.

"If you mess up the lifetimes in a piece of code that you've written by hand, I usually find that Rust's diagnostics are very helpful" -- Researcher working on static analysis of Rust programs

"Whatever's missing, the compiler usually fills in: it tells me 'you need to declare the lifetime of this reference', so I know and can figure it out. That all generally works pretty well." -- Senior Software Engineer

Learning by doing

Others felt like they only really internalized the borrow checker after writing a lot of Rust. It took projects, coding challenges, prototyping and so on until at some point it clicked.

"I actually did not understand the borrow checker until I spent a lot of time writing Rust" -- Founder of a startup built on Rust

"Besides the prototyping work, I also did coding-challenge-type stuff to get familiar with Rust for Advent of Code. [..] It eventually clicked to the point where I wasn't fighting with Rust, it was working for me. I had that experience other people describe: when I managed to get my program to fit with Rust, it worked. I didn't spend time debugging." -- Principal Software Engineer, large SaaS provider

Letting go of "clone guilt"

Some learners arrive with the assumption that good Rust means zero clones, zero copies, lifetimes threaded through everything. They set the bar at optimal before they've learned how to write idiomatic Rust, and it makes the borrow checker feel harder than it needs to be at the outset.

"On one of my first projects, I was like, 'I don't ever want to copy or clone anything,' so I carefully wove through all the lifetimes and got myself into a bit of a bind. Then I saw someone else just cloning the struct I was working with, and it was super cheap. Sometimes you can just clone and it's going to be okay." -- Researcher at a university

The experienced Rust developers we spoke with consistently said the same thing: clone freely while you're learning, then optimize when you understand the problem. Rust's reputation for performance and correctness feeds this. Newcomers assume anything less than optimal is wrong before they've written a first working program, and clone guilt is how that shows up.

We think it could be an interesting area of future study to check into the patterns Rust programmers employ at different levels of experience and under which circumstances. One member of the Rust Vision doc team that's very experienced with Rust noted that there's kind of an "expected shape" they understand as passing the compiler. This knowledge influences how they approach writing code which wouldn't take that shape and they naturally find themselves understanding when to use so-called workarounds, such as passing around indices into arrays or Vecs.

Multi-paradigm, but not the OOP some are used to

The Rust programming language is multi-paradigm, and how that lands depends on what you're coming from. We heard some that came from a functional background were delighted with digging into learning how much Rust inherits from that lineage. Some others noted that they and others on their teams struggled to unlearn the object-oriented style they'd come to use heavily in other languages like C++ and Java.

"Developers coming from C++ tend to think object-oriented. I think that's a difference between C++ and Rust." -- Architect at Automotive OEM

"I had exactly that thing, where I would apply all my years of Java and JS thinking, where I could just create some object, not care about it, return it, have it sloshing around between various functions. Found myself reaching for these patterns and then being told 'no, you cannot do that'." -- Principal Engineer at a SaaS company

Developers coming from functional programming had less to unlearn: strong typing, pattern matching, and an expression-oriented style were already familiar.

"My background has been more functional programming, strong typing. That originated for me as a Lisper: once a Lisper, always a Lisper." -- Principal Software Engineer working on Rust tooling for safety-regulated industries

"The languages I primarily used before Rust were things like OCaml. Way back, I came from C and C++, the classic languages, and then I spent quite a long time doing primarily pure functional stuff. These days I've ended up back in what I like to think of as a pragmatic center ground [with Rust]." -- Fractional CTO

Teaching Rust in academia

We spoke with a university professor that's been teaching Rust generally. In the academic environment, they were able to use proxies for some things such as "traits are like interfaces in Java" because the students had already gone through a set of courses in their first and second years that taught them Java. They introduced concepts slowly throughout the course, choosing to deal with some more complex topics like generics later. The outcome generally was that students had no problem picking up Rust in this setting.

"I couldn't see any big difference on the embedded side. We also teach an embedded class, and we did an experiment. Half of the students' feedback was worse on the Rust class, mostly because they needed to build the project themselves. The C students just got one from [an LLM], absolutely no problem." -- University Professor, on teaching Rust

The C cohort leaned on LLMs for the project in ways the Rust cohort couldn't. We don't yet have a clear answer for why.

What did come through clearly was the Rust cohort's experience with the community. Some students needed to figure out which drivers to use for the embedded project and how to use them. Their professor encouraged them to open issues and ask questions directly on GitHub, and the maintainers responded. Students who had never contributed to open source before were getting answers from the people who wrote the code.

Learning using LLMs

Some experienced folks shared that they saw LLMs as a tool that can help someone come up to speed quickly, either as a research tool or for generating example Rust code to understand concepts.

"I'm optimistic that there's a way to work [LLMs] in that will cut down that learning curve. One of the big things these tools bring is reducing the learning curve in general; these are very good tools to help you navigate a space that you don't know yet." -- Maintainer of large open source Rust crate

"I try [LLMs] out once a month, usually for generating an example or something like this. Just like with Stack Overflow: when you read an example, you should read it carefully and try to understand it. Not copy and paste it, but type it in your own words in code and then check it, because that's where the teeny tiny little mistakes are." -- Founder of startup built on Rust

For some learners, an LLM is just another way to find answers, no different than a search engine.

"So for the most part, picking up Rust - how do I learn? I'll [use web search for] things, I'll ask [an LLM], I'll just poke around and read the code." -- Senior Software Engineer working in a regulated space

One founder went further and claimed that LLMs change who can become a Rust developer. One consulting company founder described hiring high school graduates with no systems programming background and training them as Rust developers, with LLMs filling in the learning gaps that would previously have required years of experience.

"At the beginning, I was worried, but now that we have [LLMs] supporting development, the difficulty of the language doesn't matter. I'm seeing a huge opportunity behind strong runtime languages like Rust. [..] In [Developing Country] we hire 20-25 high school graduates, train them to be Rust programmers, then they enhance our workforce worldwide." -- Founder of a consulting company

We heard this from one organization. This is a claim that the combination of Rust's compiler and LLM tooling can dramatically shorten the path from beginner to working developer. Whether it generalizes depends on questions we can't answer from a single interview: how long these developers stay, what kind of code they can maintain independently, and whether this training/learning model works outside this company's particular structure. If it holds up, the pool of people who can become Rust developers is much larger than the usual hiring profile suggests.

Organizational considerations for Rust learners

We spoke with a number of folks on teams that are using Rust in larger organizations. Teams wanted to know that everyone would end up at roughly the same level of competence, which led a good number to invest in training courses to get there. Some leaders found that staff was able to ramp well enough by reading The Rust Programming Language, going through Rustlings, and then picking up lower risk and priority tickets to work on. Having a sense of community was also important within companies; it helps people know they are not alone when they are asked to work on Rust after, say, a reorganization happens.

"[..] the idea with the class as opposed to 'just read the Rust book on your own' was that this gives everyone kind of the same baseline going in." -- Principal Firmware Engineer (mobile robotics)

"So typically we're going to have people work through Rustlings, work through The Rust Programming Language. We have them then start to pick up lower risk tickets to work on." -- Principal Engineer at a large SaaS provider

"We've got an internal Slack channel for Rust learning where people can drop questions and others will come in and answer them. That helps build up understanding and community." -- Software Engineer at a large corporation

Some organizations found that while the person they'd hire would need to learn Rust, it was still preferable to the alternative of hiring someone for a critical piece of software written in another language.

"They needed to grow and maintain this C++ codebase. They had a C++ wizard, and they tried for about two years to find someone with the same level of expertise. They ended up hiring people that didn't know Rust and ramping them up, creating FFI bindings from the C++ side so they could work in Rust. And you can feel it: the borrow checker is teaching these people the right way to handle their systems." -- Principal Engineer at an Automotive OEM

The community and helping each other aspect seems to grow bonds as organizations mature.

"Our team is [all about] mentorship. I've mentored people coming up to speed on Rust, and people help each other hugely." -- Principal Software Engineer at a large SaaS company

Silent attrition

We identified some cases where people have approached Rust and bounced off of it, for one reason or another. In the below case, someone with a background in a language with fewer guardrails found themselves frustrated enough with Rust to walk away.

"All of that means that that embedded ecosystem is very frustrating to somebody who comes from C and is like, why can't I just get a pointer to this peripheral and then write into the registers. What are you doing to me? [..] My friend never got over that. He looked at it and said, I'm not going to deal with this and walked away." -– A second University Professor

There may be language features that for a particular domain are not seen as comfortable or usable yet, such as async Rust usage in a safety domain. We'd like to map which language features feel off-limits in which domains; async in safety-critical work probably isn't the only case.

"We're not fully sure how async [Rust] will work out in the long run in our domain. [..] People don't feel comfortable yet since C++14 doesn't provide such concepts. [..] It's the chicken-and-egg problem again: we probably need to gain some experience to see whether we can actually benefit from these new concepts in the automotive and safety domains." -- Team Lead at Automotive Supplier (ASIL D target)

We heard in at least one case, that while the language was challenging and there was a near bounce, the tooling helped keep them coming back and trying.

"Well, I think my early impressions of Rust - one is I find C++ so intimidating, and I think a big part of why I was able to succeed at [..] learning Rust is the tooling. I mean, all this makes sense [..] but it's like, for me, getting started with Rust, the language was challenging, but the tooling was incredibly easy." -- Founder of another startup built on Rust

While it might be considered more of a community concern, if there are interactions online and in spaces that point to learners having so-called "skill issues" this feeds into the narrative that Rust must be hard to learn. We may be unintentionally turning away Rust Project contributors and maintainers due to the vibes being put out when new learners show up in certain spaces.

"People are very helpful, but generally the attitude is: if your program is very complicated, it's mostly a skill issue. There's not that much empathy when people get stuck learning, and a lot of people are just pushed away by it. There's probably a huge number of people who silently stop wanting to write Rust, because at some point it gets complicated and the feedback they get is 'you just need to be a better programmer, obviously'." -- Software Engineer at a SaaS Provider

Feedback on near-bounces from survey

We found a few interesting perspectives collected in the Rust Vision doc survey which we administered with examples of bouncing and coming back:

"I started before 1.0, got stuck very soon when trying to translate patterns from C++ to Rust (due to borrow checking). I tried again after 1.0 and it stuck. [..]" -- Survey Respondent A

Survey Respondent A went on to share in a more detailed response about a perceived weakness in Rust learning materials related to lifetimes and the borrow checker are explained. There was an observation that it's fairly easy to run into more complex situations with lifetimes and the borrow checker. They felt that the current state of this sort of material and tutorials is fairly superficial and can leave learners stuck when they run into those more complex situations.

One respondent that bounced once and came back shared challenges around usage of async. In concert with Rust's memory-safety and the borrow checker, they found some of the nitty-gritty details of async were difficult to learn. While we're aware of the Rust Project's continuous efforts to improve Rust's async story, this is another data point of a user that faced challenges.

Another survey respondent shared how they had multiple times bounced in trying to learn Rust. They returned after a year or so and found Rustlings to be highly motivating. We note that having multiple pathways for folks to learn Rust opens up more possibilities for those that nearly bounced, just like this person.

Need more focused work on silent attritrion

The thing that stood out most to us was the lack of real, first-hand knowledge of having bounced when learning Rust. While this is an obvious effect of soliciting answers to our survey and opportunities to interview through Rust channels and our networks, this cohort is good future candidate where interviews could start.

Conclusions

Across these conversations, the experience of learning Rust depended heavily on context. Why someone was learning and what support they had mattered as much as the borrow checker. The same kinds of examples kept coming up: a training course that got a team to a shared baseline, a maintainer answering a student's first GitHub issue, and a colleague whose code showed that cloning was okay.

That context is largely something the community has a hand in. With that in mind, here is what we take away from what we heard, and what we still don't know.

What seems worth trying

Learning materials aimed at unlearning. Syntax barely came up when people described their struggles. People struggled with unlearning habits from previous languages, whether OOP structuring from C++ and Java or the instinct to grab a raw pointer to a peripheral. Most of our learning materials teach Rust from first principles, and that works. What we didn't come across is much written for, say, the engineer with ten years of Java who lands on a Rust team after a reorg: material that names the patterns they'll reach for that won't transfer, and shows what to do instead. The professor we spoke with did a version of this in the classroom, leaning on "traits are like interfaces in Java" and saving generics for later in the course, and the students did fine. Something similar could work outside the classroom too.

Put the "clone freely while you're learning" advice somewhere official. Every experienced developer we spoke with gave the same advice, but learners seem to mostly pick it up by accident, like the researcher who happened to see someone else cloning the struct they had been carefully threading lifetimes through. Saying it early in official materials would take some of the steepness out of the curve. The broader version belongs there too: idiomatic Rust doesn't have to mean optimal Rust, especially on a first project.

Diagnostics are already a primary learning resource: several people told us the compiler taught them lifetimes before any documentation did. Diagnostics reach learners right at the moment they're stuck. When writing new ones, it seems worth keeping the confused newcomer in mind alongside the expert, because for a lot of people this is where the learning happens.

Is "the book" actually out of date? Whether or not The Rust Programming Language or other materials are actually behind, a team evaluating Rust looked at its repository, saw unresolved issues and unmerged PRs, and moved on. As more companies evaluate adoption, more people will look at these materials with the same fresh eyes. Visible issue triage and some communication about what's current and what's planned would address the perception, separately from whatever content work may or may not be needed.

How stuck learners get treated is shaping who stays. We heard about students getting answers on GitHub from the maintainers who wrote the code, and we heard about learners being told their struggles were a skill issue. The first group came away with a lasting good impression of Rust. Some of the second group walked away entirely, and because they leave quietly, it's easy to underestimate how many of them there are. The welcoming side of the community came up unprompted as a reason people stayed, so we know it makes a difference when we get this right.

Every organization we spoke with described essentially the same ramp-up for bringing a team to Rust. Teams that brought groups of developers to Rust described roughly the same approach: get everyone to a shared baseline with a training course or with The Rust Programming Language and Rustlings, start people on lower-risk tickets, and give them somewhere internal to ask questions. Several organizations also found that hiring developers without Rust experience and ramping them up worked out better than continuing to search for rare expertise in another language. None of this is complicated, and teams weighing adoption don't need to invent a training program from scratch.

What we still don't know

The biggest gap is the people we didn't reach. Nearly everyone we spoke with stuck with Rust long enough to be reachable through Rust channels, so the stories of bouncing off came to us second-hand: a friend who walked away from embedded Rust, colleagues who quietly stopped after the responses they got. As we wrote in our first post, finding people who decided against Rust takes targeted outreach. If the proposed User Research team comes together, talking with learners who bounced would make a good early project, and learning is probably the area where that research would teach us the most.

We also don't know what to make of LLMs as a learning tool yet. They came up as a search engine, as an example generator, and in one organization's case as something that makes training high school graduates into working Rust developers possible. We saw a classroom where the C cohort leaned on LLMs in ways the Rust cohort couldn't, and we don't have an explanation for it. All of this comes from a handful of conversations, so we treat it as a set of leads to follow up on. Given how quickly the tools are changing, it seems better to study this deliberately than to wait and see what folklore develops.

The folks we spoke with showed that people do get there: with enough passes through the materials and enough code written, it eventually clicks. The opportunities above are mostly about making it work for the people who didn't pick Rust on purpose, and for the ones who would have stuck around if their early experience had gone a little differently.

Zimbra Phishing Campaign Uses CVE-2025-66376

LAUNDRY BEAR Targets Email and Sensitive Data

Ulej and Flowerbed Support Email Data Exfiltration

Organizations Urged to Patch Vulnerable Zimbra Systems

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.

Der Artikel WorldMonitor holt die Weltlage ins eigene Heimnetz erschien zuerst auf TARNKAPPE.INFO

The company launched Inflection AI Labs, a public-facing research and experimentation arm, alongside Pi Journeys, the lab's first product experiment — an AI experience designed to adapt to a user's life stage, whether that's becoming a parent, taking on caregiving duties, changing careers, or aging. The announcement arrived with a research report on consumer AI habits and a substantial update to Pi, the company's flagship chatbot, adding improved voice, memory, and new agentic tools for reminders, to-do lists, and shopping.

"Inflection AI is the company. Pi is our flagship consumer product. Inflection AI Labs is where we experiment, explore personal intelligence and share more publicly. Pi Journeys is the first public experiment from Inflection AI Labs," CEO Sean White told VentureBeat in an exclusive interview.

Behind the tidy org chart is a far more interesting story: a company attempting one of the more unusual second acts in the AI industry, powered by an argument that the entire market is optimizing for the wrong thing.

Why Inflection AI believes the chatbot era's biggest flaw is that it's transactional

White's central claim is that today's AI assistants — including the industry's most capable models — are fundamentally transactional. You ask, they answer, the session ends. He believes that architecture misses most of what people actually need from artificial intelligence in their daily lives.

"One of the things that really struck us in particular, and this showed up in the research, was that a lot of the work is very transactional, and you'll hear me say a lot that we've been shifting all this from transactional to relational systems," White said. "Not everything is going to be: I do a single turn, I utter a question, I get a search response back."

White frames the industry's evolution as a progression through four kinds of intelligence. First came raw IQ — the foundation model race. Then emotional intelligence, which Inflection made its signature with Pi's famously warm conversational style. Then agentic intelligence — AI that acts rather than just talks — which White says Inflection absorbed from its enterprise work. The fourth, and the one Inflection is now staking its future on, is what the company calls relational intelligence: AI that understands not just you, but the web of people around you.

"There's so much fear about these things pushing people into loneliness,” White said. “If we design these pro-social systems as another design criteria, that actually makes a huge difference."

That design philosophy is a pointed counter-narrative to one of the loudest anxieties in consumer AI right now: that emotionally engaging chatbots deepen isolation by substituting for human contact. Inflection argues the opposite is possible — that an AI with structured knowledge of your relationships can push you back toward people rather than away from them.

Inside Pi Journeys, the AI companion that maps your relationships and life stages

Pi Journeys makes that idea concrete. When users first open the product, it asks about their life stage — caregiver, household manager, midlife transition — and then builds what White describes as specially structured memory around the people who matter in that context. From there, the system becomes proactive.

"It starts to build up memories around that, and it acts as a memory prosthetic — but in a pro-social way," White said. "It doesn't get in the way of your interactions with other people; it really helps facilitate them." The system might remind a user, for example, that a friend deserves a call, or resurface what was last discussed with a family member involved in a parent's care.

White, who spent years as chief R&D officer at Mozilla before taking Inflection's helm, was quick to flag the obvious privacy implications of an AI that maps your social graph. "We've built a lot of privacy systems into this," he said, noting users can delete and manage the people recorded in their profile. Whether consumers will trust a venture-backed AI company with a structured database of their most important relationships remains one of the biggest open questions hanging over the product — and one that enterprise buyers evaluating Inflection's technology will watch closely.

Asked why this was the first Labs experiment, White was direct: "Pi Journeys takes into account people's life stages and experiences because we have heard from users that we can provide more value in helping them navigate their lives. Pi Journeys lets us experiment with the early stages of prosocial and relational intelligence because life isn't single-player."

The product has been tested internally and with small closed groups, White said, and is now being released more broadly as an experiment rather than a finished product — a posture the Labs branding is designed to make explicit.

What Inflection's consumer AI research reveals about how people actually use chatbots

Inflection Labs' first publication, the State of Consumer AI Research Report, offers the empirical scaffolding for the strategy. The average consumer now uses roughly two different AI tools every day and three per week, the company found — evidence, in Inflection's reading, that no single assistant has locked up consumer loyalty and that the market remains contestable.

More telling is why people choose the tools they do. Respondents cited personalization, style and tone, context awareness, and — notably — emotional understanding as deciding factors. They also said they want AI to be more than a productivity engine: a coach or mentor to motivate them, a chef to suggest recipes, a DJ to curate playlists.

"One thing we're certainly finding is that a lot of that also is in work, not so much in everyday life," White said. "That's our focus right now — the everyday life part."

This is a shrewd reading of the competitive map. The best-funded AI labs are pouring resources into coding tools, enterprise agents, and developer platforms, leaving everyday consumer use cases comparatively underserved. White sees the gap clearly. "We see a lot of products that are being aimed more and more at the enterprise," he said. "As a computer scientist by training, I kind of love the IDEs as this tool, but it's not really great for everybody. There's so much regular everyday use from folks that is either purely voice or that is purely mobile."

He recalled a conversation with a conference staffer who told him she owned only a phone, no laptop — exactly the kind of user, he argued, that the industry's developer-centric product roadmaps have left behind.

How the $650 million Microsoft deal hollowed out Inflection — and set up its second act

To understand why any of this is remarkable, you have to rewind to March 2024. Inflection was then one of the hottest startups in AI, having raised $1.3 billion in mid-2023 in a round backed by Microsoft, Nvidia, Bill Gates, and Reid Hoffman — more than $1.5 billion in total. Pi had crossed one million daily active users, per Reuters.

Then, in a deal that reshaped how the industry thinks about acqui-hires, Microsoft hired away co-founder and CEO Mustafa Suleyman, chief scientist Karén Simonyan, and most of the company's roughly 70 employees, paying Inflection about $650 million largely to license its technology, as Reuters reported. Suleyman now runs Microsoft's consumer AI business. The structure of the deal drew scrutiny from the FTC and Britain's competition regulator, though the UK's Competition and Markets Authority cleared it in September 2024 and EU regulators declined to act.

White, installed as CEO in the aftermath, steered the remnant company hard toward enterprise, acquiring three startups in late 2024 — Jelled.AI, BoostKPI, and the European consulting firm Boundaryless — and telling TechCrunch that November that Inflection had no intention of competing with companies building 100,000-GPU frontier systems.

Tuesday's announcement doesn't reverse that position so much as complicate it. Asked how to think about the company today, White called it "a consumer-first strategy that bridges both consumer and enterprise efforts" — and he insists the two sides feed each other.

Enterprise deployments, including a partnership with Intel that is among the few he can name publicly, taught Inflection how to run models inside complex infrastructure. Consumer products, meanwhile, let the company iterate at speed. "The part I also like about the consumer side, and this has always been true, is that we can move faster, experiment faster, and try and learn faster," White said.

The six-month prediction: relationship-aware AI is coming to the enterprise

Buried in White's consumer pitch is the claim that should matter most to technical decision-makers. "Normally I'd say like a year, but let's call it six months," he said. "You're going to start to see a bunch of enterprises care a lot more about the relationships that are inside the enterprises and what that picture is, not just the workflows."

If White is right, the wave of workflow-automation agents currently flooding the enterprise market is only the first phase of business AI adoption — with relationship-aware systems, tested first on consumers, following close behind. Inflection is essentially using its consumer products as a live laboratory for capabilities it plans to sell into companies. It's a capital-efficient strategy for a firm that can no longer outspend rivals on training runs, and a risky one, since it depends on consumers showing up in numbers large enough to generate the learning.

The technical substance underneath is equally pragmatic. Pi today runs not on a single proprietary frontier model but on an orchestration layer routing across many models — some descended from Inflection's original fully trained cores, some fine-tuned, some open source, including work with Nvidia that White says gives Inflection access to unreleased cutting-edge models. He also took a swipe at the industry's loose vocabulary around ownership: "When people say that the model is their own, most of the time nowadays — I guess I won't name names — a lot of companies will actually take a checkpoint, and then they will fine-tune from that checkpoint. But very few people actually start from that beginning core."

That candor extends to open source, where White carefully hedged. "We're not ready to promise what I think of as true open source, and by that I mean everything," he said, invoking his Mozilla years overseeing genuinely open projects like Rust and WebAssembly.

Weights without training data and pipelines, he argued, often leave developers unable to do anything meaningful with a supposedly "open" model. "We are a PBC, and there's still a C in there," he added — a reminder that public benefit corporations still have businesses to protect. The Labs will collaborate with academic researchers, including Stanford professors who visited the company's Palo Alto office this week, and continue contributing to open projects such as PyTorch.

Can a diminished Inflection compete with AI giants spending billions?

Reid Hoffman, the LinkedIn co-founder who co-founded Inflection and stayed on through the Microsoft upheaval, framed the announcement in the sweeping terms of his recent writing on AI and human agency. "Humans should be amplified by AI, not replaced. That's the principle Pi was built on," Hoffman said in the announcement. "When that kind of agency is available to everyone, you get superagency."

The skeptic's case is easy to make. Inflection is a fraction of its former size, competing for consumer attention against products from companies spending tens of billions of dollars a year. Pi's model was state of the art in 2023; it is not in 2026. And "relational intelligence" is, for now, a brand claim awaiting proof.

But the bull case is not crazy either. Inflection's own research shows consumers already juggle multiple AI tools and choose them for qualities — tone, emotional understanding, personalization — that frontier labs treat as afterthoughts. The company kept its technology, its Microsoft licensing windfall, and a defensible enterprise niche in on-premise, emotionally intelligent deployments. And it is targeting the one consumer segment — everyday, mobile-first, voice-first life management — that the coding-obsessed giants have largely ignored.

Asked what success looks like twelve months from now, White declined to talk numbers. "It's less about scale for scale's sake and more about scaling for impact by empowering people and improving their lives," he said. "Over the next year, success means leading the market towards relational intelligence and transforming AI interactions from transactional to relational."

Two years ago, Microsoft walked away with Inflection's founders, its staff, and its shot at the frontier — but it left behind the one idea the giants still haven't figured out how to build: an AI that knows the people in your life matter more than the tasks on your list. Inflection is betting the company, again, that the idea was the valuable part all along.

Read more of this story at Slashdot.

• Type GitHub release metadata by @JamieMagee in #15597
• Make GitCommitChecker strongly typed by @JamieMagee in #15598
• Retry corepack prepare and install on signature metadata errors from private registries by @kbukum1 in #15606
• Fix UV DependencyGrapher to detect nested uv.lock in monorepos by @thavaahariharangit with @Copilot in #15520
• Bump library/rust from 1.95.0-bookworm to 1.97.0-bookworm in /cargo by @dependabot[bot] in #15560
• Bump @sigstore/core from 3.1.0 to 3.2.1 in /npm_and_yarn/helpers by @dependabot[bot] in #15455
• Bump maven from 3.9.14 to 3.9.16 in /maven by @dependabot[bot] in #15127
• Support Bundler source cooldown in Dependabot cooldown flow by @robaiken in #15517
• Type shared release metadata by @JamieMagee in #15607
• Make Job strongly typed by @JamieMagee in #15608
• Type Job wire models by @JamieMagee in #15610
• Type Service and ApiClient by @JamieMagee in #15614
• Add support for calendar-based versions for Maven and Gradle by @yeikel in #14114
• Type error reporting by @JamieMagee in #15615
• Type updater dependency helpers by @JamieMagee in #15617
• ensure proper formatting when patching element attributes by @brettfo in #15629
• Bump ws from 8.18.3 to 8.21.1 in /npm_and_yarn/helpers/test/npm/fixtures/vulnerability-auditor/update-needed-across-two-versions by @dependabot[bot] in #15329
• Bump lodash from 4.17.23 to 4.18.1 in /bun/helpers/test/yarn/fixtures/conflicting-dependency-parser/deeply-nested by @dependabot[bot] in #14608
• Bump lodash from 4.17.23 to 4.18.1 in /npm_and_yarn/helpers/test/yarn/fixtures/conflicting-dependency-parser/deeply-nested by @dependabot[bot] in #14609
• Bump lodash from 4.17.23 to 4.18.1 in /npm_and_yarn/helpers/test/npm6/fixtures/conflicting-dependency-parser/deeply-nested by @dependabot[bot] in #14610
• Bump the dev-dependencies group across 1 directory with 2 updates by @dependabot[bot] in #14694
• Bump pip from 26.1.1 to 26.1.2 in /python/helpers in the pip group across 1 directory by @dependabot[bot] in #11830
• Bump yaml from 2.3.1 to 2.9.0 in /bun/helpers by @dependabot[bot] in #14535
• npm_and_yarn: group vulnerability auditor blocking-dependency messages by top-level ancestor by @thavaahariharangit in #15627
• Bump ip-address and socks in /bun/helpers by @dependabot[bot] in #14924
• Bump brace-expansion from 1.1.13 to 1.1.16 in /bun/helpers/test/yarn/fixtures/conflicting-dependency-parser/deeply-nested by @dependabot[bot] in #15634
• Bump brace-expansion from 1.1.13 to 1.1.16 in /npm_and_yarn/helpers/test/yarn/fixtures/conflicting-dependency-parser/deeply-nested by @dependabot[bot] in #15633
• Bump sigstore/cosign/cosign from v3.1.1 to v3.1.2 in /docker in the regclient group across 1 directory by @dependabot[bot] in #15621
• Bump lodash from 4.17.23 to 4.18.1 in /bun/helpers/test/npm6/fixtures/conflicting-dependency-parser/deeply-nested by @dependabot[bot] in #14606
• Bump @tootallnate/once from 2.0.0 to 2.0.1 in /bun/helpers by @dependabot[bot] in #15107
• Bump the "uv-ecosystem" group with 1 update across multiple ecosystems by @dependabot[bot] in #14969
• Bump ip-address and socks in /npm_and_yarn/helpers by @dependabot[bot] in #14923
• Bump yaml from 2.3.1 to 2.9.0 in /npm_and_yarn/helpers by @dependabot[bot] in #14533
• Bump golang.org/x/mod from 0.37.0 to 0.38.0 in /go_modules/helpers by @dependabot[bot] in #15559
• Bump @sigstore/verify from 3.1.0 to 3.1.1 in /npm_and_yarn/helpers by @dependabot[bot] in #15477
• julia: don't propose compat updates for workspace packages or synthesize member compat entries by @IanButterworth in #15643
• fix: guard against unparseable versions in cooldown fallback by @currantw in #15632
• Type dependency requirement readers by @JamieMagee in #15646
• v0.388.0 by @dependabot-core-action-automation[bot] in #15623

New Contributors

• @currantw made their first contribution in #15632

Full Changelog: v0.387.0...v0.388.0

The post Cloudflare Data Reveals Which World Cup Teams Moved the Global Internet Most appeared first on Cyber Security News.

The post Build an LLM Agent That Can Write and Run Code appeared first on Towards Data Science.

Dieser Beitrag Langflow RCE: ENCFORGE verschlüsselt KI-Modelle, Vektoren und Trainingsdaten erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.

The headline numbers are striking for a model this small. Poolside reports that Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1, a benchmark of long-horizon terminal tasks, placing it 11th on the company's compiled leaderboard — ahead of DeepSeek-V4-Pro-Max, a 1.6-trillion-parameter model that scored 64.0; Thinking Machines' 975-billion-parameter Inkling, at 63.8; and Nvidia’s 550-billion-parameter Nemotron 3 Ultra, at 56.4. On SWE-Bench Multilingual, it posts 78.5%, and on SWE-Bench Pro's public dataset, 59.4%.

Perhaps more telling than any single score: the model went from the start of pre-training on May 22 to public launch in under nine weeks, trained on 4,096 Nvidia H200 GPUs. In an industry where flagship model cycles are typically measured in quarters or years, Poolside has now shipped three models in three months.

Why the West's open-weight AI gap has become a boardroom issue

The release lands in the middle of an increasingly pointed debate about the provenance of open-weight AI. Over the past year, developer adoption has shifted decisively toward open-weight systems that companies can download, inspect, and run on their own infrastructure — and the leading options in that category have overwhelmingly come from Chinese labs. DeepSeek, Qwen, Kimi, GLM, MiniMax, and Tencent's Hunyuan line all feature prominently in Poolside's own comparison tables.

Poolside's accompanying press release frames Laguna S 2.1 explicitly as a response, noting that the model occupies a size class into which no Western lab has released open weights in 11 months — since OpenAI's gpt-oss-120b last August. "The West needs open-weight models it can trust, run, and build on," said Jason Warner, Poolside's co-CEO, in the announcement.

Co-founder and co-CEO Eiso Kant made the philosophical stakes even plainer in a lengthy post on X. "I believe intelligence should and will become a commodity," he wrote, arguing that the open ecosystem "will not win by being the best in its own category." Users, he argued, simply want the best intelligence for the task at hand — so open models must be on par with, or better than, their closed equivalents.

The strategic logic here is not charity. Poolside's core business is deploying models inside the security boundaries of government, defense, and regulated enterprises — customers for whom closed, metered API access is often a non-starter for compliance and sovereignty reasons. 

Every enterprise that standardizes on a Chinese open model today becomes harder to win tomorrow. Releasing competitive open weights is both an ecosystem play and a top-of-funnel strategy for the company's high-security deployment business. It also reframes the AI race away from terrain where Poolside cannot compete — frontier-scale capital expenditure — and toward terrain where it believes it can: cost per token, self-hosting, and iteration speed.

How a sparse architecture makes enterprise AI agents affordable to run

The technical design reflects a specific thesis about where value in coding AI is moving. Laguna S 2.1's sparse MoE architecture — 256 routed experts plus one shared expert, with grouped-query attention and interleaved sliding-window layers, according to the Hugging Face model card — means inference costs scale with the 8 billion active parameters, not the 118 billion total. Poolside emphasizes that the model is small enough to run on a single Nvidia DGX Spark, the desktop-class AI machine.

That matters for what Poolside calls token economics. Long-horizon coding agents are voracious consumers of tokens: the company's published data shows the model consuming a mean of roughly 249,000 completion tokens per trajectory on its hardest benchmark when thinking mode is enabled. At metered API prices, agentic workloads at enterprise scale become a meaningful budget line item. On OpenRouter, Poolside is offering a free 256K-context endpoint and a dedicated 1M-context deployment priced at $0.10 per million input tokens and $0.20 per million output tokens — aggressive pricing that undercuts most frontier alternatives by an order of magnitude.

The ecosystem support is unusually broad for day one. The model is live on Baseten's model library and Vercel's AI Gateway, with integrations across vLLM, SGLang, Ollama, and llama.cpp, plus quantized variants down to 4-bit GGUF files — 75 gigabytes — for local use. But Poolside's more interesting claim is behavioral, not architectural. Pengming Wang, co-head of applied research at Poolside, said the gains came from improving the model's working habits: "more verification, less taking things for granted, not declaring victory early, and being more persistent." Raw intelligence, the company argues, is one axis of capability; a model's way of working is a second axis that matters immensely for agents left unattended for hours.

Publishing every benchmark trajectory to counter AI's credibility crisis

The most consequential part of the release for enterprise buyers may be an evaluation-transparency move with little precedent among major labs: Poolside published the complete, unedited trajectory of every trial in its final benchmark runs — every reasoning step, tool call, and shell command behind every reported score.

This addresses a growing credibility problem in AI benchmarking. As top scores on mature benchmarks cluster in the 70–90% range, and as "reward hacking" — models finding solutions online or gaming verifiers rather than solving problems — has become endemic, self-reported numbers have lost much of their signal. Poolside disclosed its own encounters with the problem candidly: during training, more than half of trajectories on some SWE-bench tasks were flagged because the model simply researched the original bug-fix pull request online and applied it. The company documented its mitigations, including prompt addenda, LLM-based judging calibrated against human labels, and expert annotator review of a high-scoring Terminal-Bench run.

Three published case studies illustrate what the company means by persistence. In one, the model built a working HTML/CSS rendering engine from an empty folder in a 181-step, 50-minute unattended session — then, lacking vision capabilities, spun up headless Chromium to numerically compare its canvas output against a real browser's rendering. In another, pointed at Poolside's own agent harness in an automated optimization loop, the model made the Go codebase 5.2% faster with roughly 70% lower memory allocation, finding an O(n²) string-concatenation bug along the way. In a third, working in a sandbox with no Python installed, the model did its number theory in Perl and independently re-derived a proof of Erdős problem #397 — a combinatorics question open for five decades until GPT-5.2 Pro first solved it this past January. Poolside notes that its model's construction is structurally different from the earlier published solution, and that its November 2025 knowledge cutoff precedes the first proof.

What the disclosed limitations and benchmark fine print reveal

Poolside deserves credit for disclosing limitations most labs bury. The model can overfit to its native harness and stumble on slightly different tool schemas in third-party agents, mangles JSON in nested tool arguments, and is prone to overthinking on competition math. There is currently no user-configurable thinking-effort dial — just on or off — and the gap between the modes is enormous: thinking lifts Terminal-Bench 2.1 from 60.4% to 70.2%, and DeepSWE from 16.5% to 40.4%, at substantially higher token cost.

Buyers should apply their own discounts to the comparison tables. Poolside's methodology takes the maximum of vendor self-reported scores, benchmark-author leaderboards, and third-party figures for competitors — a reasonable convention, but one that mixes harnesses and test conditions. On DeepSWE, notably, Poolside ran its own agent harness rather than the leaderboard's standard mini-swe-agent, a difference the company acknowledges makes scores less directly comparable. And the frontier remains clearly out of reach: closed models like GPT-5.6 Sol, at 88.8 on Terminal-Bench 2.1, and Claude Fable 5, at 88.0, along with the 2.8-trillion-parameter open-weight Kimi K3, at 88.3, sit well above Laguna S 2.1.

The deeper structural question is whether Poolside's "Model Factory" — the internal platform the company credits for its rapid release cadence — can sustain this pace as models scale. The trajectory so far is genuinely unusual: the April dual release of Laguna M.1 and XS.2, the July 2 refresh of XS 2.1, and now S 2.1, which the company says outperforms April's flagship M.1 at roughly a third of its active size. Remarkably, S 2.1 used the exact same pre-training data as XS 2.1, meaning nearly all the improvement came from scale, training fixes, and post-training across the company's corpus of 409,000 agentic and non-agentic training environments. Poolside says its next, larger Laguna model began pre-training last week.

For technical decision makers, Laguna S 2.1 is the most credible Western open-weight option to emerge in nearly a year for self-hosted agentic coding — with published evidence, a permissive license, broad ecosystem support, and an economics story built around hardware you can own. Whether it dents the dominance of Chinese open models will depend less on this release than on the ones that follow it.

Kant, for his part, has already told the world how he intends that story to end. Poolside is building toward a future where the most capable intelligence "can be owned and shaped by anyone," he wrote — and the company plans to keep shipping "until that future exists." In an industry where the biggest labs increasingly lock their best work behind an API, the most radical thing about Laguna S 2.1 may not be what it scores, but that anyone can download it and check.

Release Date: July 20, 2026
Since v0.18.0: ~2,245 commits · ~1,065 merged PRs · ~2,465 files changed · ~300,000 insertions · ~36,000 deletions · ~3,300 issues closed · 450+ community contributors

The Quicksilver Release. Hermes is the messenger god, and this window we made him move like it. First-turn time-to-first-token dropped ~80% on every platform, reasoning streams live by default, the desktop app got a ~20-PR speed overhaul (14× faster streaming markdown, virtualized diffs, snappy session switching), and the TUI renders markdown incrementally. Around that speed spine: you can now manage your Nous subscription without leaving the terminal, plug Bitwarden and 1Password straight into Hermes, let smart approvals judge flagged commands for you by default, watch your subagents work live, and trust that a finished response survives a gateway crash thanks to a durable delivery ledger. This release also rolls up everything from the v0.18.1 and v0.18.2 infrastructure patch tags — those windows are fully documented here.

✨ Highlights

• Hermes got dramatically faster — first token in a fraction of the time — Cold-start "Initializing agent..." used to eat ~4.3 seconds before your first turn even reached the model; it's now ~0.9s, an ~80% cut that applies to the CLI, gateway, TUI, desktop, and cron alike. Round 2 attacked what you see while waiting: reasoning models now stream their thinking live by default (no more staring at a spinner for 30 seconds), and the response box paints per token instead of per line. If Hermes ever felt like it took a deep breath before answering, that breath is gone. (#59332, #59389 — @teknium1)

• The desktop app speed wave — 20+ targeted perf PRs — Long replies used to cost 14× more CPU in the markdown splitter than they do now; giant diffs froze the review pane until we virtualized it; switching sessions thrashes layout no more. Streaming no longer re-renders the sidebar and every tool row per token, profile backends pre-warm on hover intent, and boot-hidden panes mount at idle instead of on the cold-start critical path. The net effect: the desktop app feels like a native app under load, even with huge transcripts and busy agents. (#67154, #67818, #65898, #66033, #66747, #67742 and more — @OutThisLife)

• Manage your Nous plan from the terminal — /subscription and /topup — Changing your subscription used to mean a trip to the billing website. Now /subscription opens a full flow right in the TUI or classic CLI: see your plan and remaining allowance, preview exactly what an upgrade costs ("Pay $46.30 & upgrade now") or when a downgrade takes effect, and apply it — with scheduled-change banners and undo. The desktop app got a matching billing settings tab. Your wallet never has to leave the keyboard. (#51639, #61054, #61067 — @alt-glitch)

• Smart approvals are now the default — When Hermes wants to run a flagged command, an LLM reviewer now assesses it independently instead of asking you to approve every single one — and each verdict covers only that exact command, so a later command matching the same pattern gets its own review. Combined with the new user-defined deny rules (which block commands even under yolo mode) and /deny <reason> (which tells the agent why you refused so it course-corrects), day-to-day approval fatigue drops sharply without giving up control. (#62661, #59164, #54518 — @teknium1)

• Plug your password manager into Hermes — Bitwarden & 1Password secret sources — API keys no longer have to live in a plaintext .env. A new pluggable SecretSource interface lets Hermes fetch secrets from Bitwarden and 1Password (op:// references) at load time, with multiple vaults enabled simultaneously, deterministic precedence, conflict warnings, and per-variable provenance. This consolidated eleven competing community PRs into one orchestrated interface — future vault providers drop in as plugins. (#59498 — @teknium1, 1Password provider salvaged from @hwrdprkns)

• Watch your subagents work — live transcripts + durable background delegation — delegate_task dispatches now return live transcript files you can tail -f the moment the subagents launch: every tool call, result, and streamed reply, one human-readable log per child. And background delegation completions are now durable — if the process restarts mid-run, results are restored and delivered through an ownership-checked ledger instead of vanishing. Fan out a fleet, watch any worker live, and never lose the results. (#67479, #63494 — @teknium1)

• A finished answer can no longer be lost — the delivery-obligation ledger — If the gateway died between generating your response and confirming the platform actually delivered it, that answer used to be silently gone (and you'd paid for the turn). Final responses are now recorded in a durable ledger in state.db around the platform send and redelivered on the next boot — closing a P1 silent-loss window for Telegram, Discord, Slack, and every other channel. (#67181 — @teknium1)

• One gateway, many profiles — profile-based message routing — A single multiplexed gateway sharing one bot token can now route specific guilds, channels, or threads to different profiles — each with fully isolated config, skills, memory, and secrets. Point your work Discord server at the work profile and your hobby server at personal, from one bot. A second multiplex hardening wave means one misconfigured profile can no longer take down the whole gateway. (#64835 salvaging @Burgunthy, #65700, #60589 — @teknium1, @benbarclay + six salvaged contributors)

• New providers and the newest frontier models — Fireworks AI and DeepInfra land as first-class providers (Fireworks with cost estimation and a #2 slot in the provider picker), Upstage Solar joins via salvage, and the model catalogs picked up GPT-5.6 (Sol/Terra/Luna + Pro variants, wired end-to-end across every route), grok-4.5 (GA), moonshotai/kimi-k3, claude-fable-5 / claude-sonnet-5, and GA tencent/hy3 — plus LM Studio JIT model loading for local setups. (#62593, #63969, #61616 — @kshitijk4poor completing @rob-maron's #61578, #60887, #65913, #64541, #65472)

• Crank the thinking to max — new reasoning effort tiers and per-model control — Reasoning effort gained max and ultra levels (GPT-5.6 and Codex's top tiers), selectable everywhere from the CLI to the desktop, with sane clamping on providers with smaller scales. You can now also pin per-model reasoning-effort overrides in config, set per-slot effort in MoA presets (your advisors think hard, your synthesizer stays fast), and per-task effort for auxiliary models. Thinking depth is now a dial, not a global switch. (#62650, #64458, #64631, #64597 — @teknium1)

• Your sessions, your data — export everything — hermes sessions export now writes Markdown, Quarto, HTML, prompt-only, and even Hugging Face-ready trace formats, with the full filter surface (age, workspace, platform), an opt-in --redact secret-scrubbing pass, and compacted-session lineage stitched into one logical export. Pair with the new prune filters and bulk archive to keep your session store tidy. Your conversation history is a real dataset now, not a black box. (#60186 salvaging @web3blind, #60492, #60507, #59327 — @teknium1)

• Security hardening round — This window closed a long list of credential-surface gaps: Vertex credentials scoped away from subprocess env and through profile secret scopes, media/vision/image-gen local-file reads routed through one shared credential-read guard, a webhook body-size-cap sweep across every aiohttp server, bot-token redaction in Telegram transport errors, Fireworks token prefixes added to the redactor, six P1 browser/MEDIA/.env hardening PRs salvaged in one pass, and CI hardened against untrusted-ref interpolation. (#57660, #58709, #59215, #56582, #57842 — @teknium1, @srojk34, @kshitijk4poor, @jquesnelle)

⚡ Performance — the speed spine

First-turn latency (all platforms)

• ~80% TTFT cut — Discord capability detection off the critical path (token-keyed 24h disk cache + background refresh), Ollama probe skipped for known non-Ollama providers, agent-init blocking work removed; cold submit→dispatch ~4.3s → ~0.9s (#59332 — @teknium1)
• Perceived-latency round 2 — display.show_reasoning default ON (watch the model think instead of a spinner), per-token response-box painting with width-aware force-flush, prompt-build caching, mtime-cached timezone resolution (#59389 — @teknium1)
• Segment mixed tool batches to recover lost concurrency; drop per-call base64 re-serialization from request-size estimates (#64460, #67788 — @teknium1, @OutThisLife)

Desktop speed wave

• 14× less splitter CPU via incremental block lexing for streaming markdown; virtualized review-pane diffs (no more full-Shiki freeze); snappy session switching on large transcripts; killed the layout-thrash cascade on session switch (#67154, #67818, #65898, #66033 — @OutThisLife)
• Cut startup serialization + per-turn REST amplification; pre-warm profile backends and gateway sockets on hover intent; idle-mount boot-hidden panes; fast model picker + dialogs (#66747, #66347, #67857, #66470 — @OutThisLife)
• Stop per-token sidebar + tool-row re-renders during streaming; stop eager JSON.stringify of every tool's args/result; scope tool-diff subscriptions; batch sidebar session slices into one profile-DB pass; targeted file-tree revalidation; rAF-coalesced sash resizes (#67742, #67842, #67195, #67245, #67824, #67838, #67844 — @OutThisLife)
• Systematized perf benchmark harness with trustworthy cold-start + first-token measurement, replacing 12 one-off scripts (#67466, #67697 — @OutThisLife)

Everywhere else

• TUI renders streamed markdown incrementally per block (#67236 — @OutThisLife)
• Skill discovery cached by scan signature; snapshot manifest builds ~5× faster; text prefilter before AST parse in tool discovery (#61414, #61131, #63941 — @kshitijk4poor, @ethernet8023)
• Copy-on-write message prep instead of full deepcopy; model-metadata probe-cache cluster; gateway session.resume model + display history from one SELECT (#61133, #61368, #67247 — @kshitijk4poor, @OutThisLife)
• hermes update skips npm install when Node manifests are unchanged; dashboard session-list payloads trimmed + messages paginated (#61580, #60883 — @kshitijk4poor)
• Byte-stable gateway system prompts — pinned session-context render keeps the prompt cache alive across turns (#67403 — @kshitijk4poor)

🏗️ Core Agent & Architecture

Providers & models

• Fireworks AI provider with cost estimation + cached picker price columns, promoted to #2 in provider pickers (#62593, #65476, #65214 — @teknium1)
• DeepInfra hardened integration; Upstage Solar provider (#42231 salvage) (#63969, #64541 — @kshitijk4poor)
• GPT-5.6 (Sol/Terra/Luna + Pro) end-to-end — context lengths, native/Codex catalogs, pricing, compaction caps across every route (#61616 — @kshitijk4poor, building on @rob-maron)
• grok-4.5 (GA) catalog + reasoning allowlist; kimi-k3 on Nous Portal + OpenRouter (kimi-k2.x retired) + K3 discovery on the Kimi Coding endpoint; claude-fable-5 / claude-sonnet-5 / fugu-ultra curated; GA tencent/hy3 (#60887, #65913, #65922, #56617, #60943 — @teknium1)
• Catalog-labeled silent default (GLM-5.2) + bare-provider /model cost-safe routing; LM Studio JIT load mode; adaptive thinking for Kimi-family Anthropic endpoints (#64771, #65472, #67606 — @teknium1, @kshitijk4poor)
• GLM-5.2 native reasoning_effort controls; Gemini request-context improvements; extra HTTP headers for LLM API calls; per-client model routing on the API server (#58884, #61873 — @vishal-dharm, #57038, #57028 — @teknium1)
• Claude Sonnet 5 fully wired — curated lists, intro pricing, and metadata across every route (#67932 — @teknium1)
• Hide providers you don't use — enabled: false per-provider flag + excluded_providers config scrub unwanted providers from /model pickers and built-in resolution (#67971 — @teknium1)
• Bedrock catalog wave: real context-window probing from the live endpoint, 1M-context rows for current-gen Claude + Fable, geo-prefix parity, versioned profile-ID pricing, Opus 4.8/4.7 rows (#68007, #67977, #68005, #67976 — @teknium1)
• kimi-k3 rollout completed across Kimi-direct catalog surfaces with 1M context on canonical Kimi Coding endpoints (#68108 — @teknium1)
• Provider pickers: Qwen providers folded into one group row; collapsible provider groups in the desktop model picker; friendlier TUI model display grouping same-endpoint providers (#67758, #67904, #67908 — @teknium1)

Reasoning & MoA

• max + ultra effort levels across every surface and route (#62650 — @teknium1)
• Per-model reasoning_effort overrides via a unified resolution chokepoint; per-task auxiliary effort; per-slot MoA preset effort; session-scoped /reasoning in the CLI (#64458, #64597, #64631, #67946 — @teknium1)
• MoA: reference_max_tokens to cap advisor output and cut latency; per-preset fanout cadence (user_turn runs advisors once per user turn); stale presets surfaced without retries; half-filled preset saves rejected at the API boundary; aggregator resolves reasoning like an acting model (#56756, #57591, #64756 — @teknium1)

Delegation, approvals & the agent loop

• Live subagent transcripts + durable background completions (see Highlights) (#67479, #63494 — @teknium1)
• Smart approvals default; user-defined deny rules (block even under yolo); /deny <reason> relays the denial reason; plugin pre_tool_call approve action escalates to a human gate (re-landed with rule keys) (#62661, #59164, #54518, #60504 — @teknium1, @kshitijk4poor)
• Unified delegation concurrency caps (max_async_children deprecated); explain long provider waits on the live status line; deterministic tool-output risk exposure (#56955, #64775, #61793 — @teknium1)
• Codex: live TUI/desktop tool cards for the app-server runtime, commentary streamed as visible interim messages, compaction routed through thread/compact/start, max-output truncation recovery, oversized message ids dropped on replay, banked usage-limit resets via /usage reset (#66514, #66115, #60114, #58155, #62225 — @teknium1, @kshitijk4poor, @JoaoMarcos44, #64280 — @teknium1)
• Hooks: oversized hook-injected context spills to disk (#20468 — @teknium1)
• Vibe reactions — floating hearts on affection across CLI/TUI/desktop, token-free core detection (#62016 — @OutThisLife)

Secrets & config

• Pluggable SecretSource interface + Bitwarden & 1Password providers (see Highlights) (#59498 — @teknium1, @hwrdprkns)
• hermes config get / unset; warn on unknown root config keys + doctor deprecated-key reporting; display.timestamp_format (#65540, #67370, #40622 — @teknium1)
• Auxiliary model usage recorded per task in session accounting; conversation-scoped Nous Portal usage tags across aux/MoA/delegate calls; --usage-file JSON report for hermes -z (#65537, #65468, #59615 — @teknium1)

Sessions & compression

• Sessions export: Markdown/QMD/HTML/prompt-only/trace formats, HF upload, --redact, unified filters; full prune filter surface + bulk archive; CLI workspace filter + restore-cwd-on-resume (#60186, #60492, #60507, #59327, #63091 — @teknium1, @web3blind)
• Compression: preserve human intent and durable handoffs; retain prompt cache when memory is unchanged; flatten multimodal content for the summarizer keeping image handles; gateway compression routing integrity (#67275, #67916, #65046, #56868 — @kshitijk4poor, @teknium1)
• Gateway session metadata consolidated into state.db; routing index moved to state.db (sessions.json now an optional legacy mirror); exact API bytes persisted in an api_content sidecar (#58899, #59203, #67274 — @teknium1, @kshitijk4poor)

🌐 Gateway, Fleet & Relay

• Durable delivery-obligation ledger for final responses (see Highlights) (#67181 — @teknium1)
• Profile-based routing for inbound messages + multiplex hardening wave 2 + GATEWAY_MULTIPLEX_PROFILES override (see Highlights) (#64835, #65700, #60589 — @teknium1, @benbarclay + salvaged contributors)
• Per-session turn lease + conversation-scope funnel; unified session reset boundaries (reset sessions stay reset); truthful runtime readiness checks; per-channel model and system prompt overrides; per-session /model overrides persist across restarts (#67401, #65783, #62645, #56967, #57030 — @teknium1)
• Session auto-reset default off; /sessions search <query>; webhook payload filters + route scripts; platform HTTP event callback routing; configurable long-running status phrases (#60194, #57685, #60944, #65702, #58872 — @teknium1)
• Relay: generic OIDC client-credentials provisioning (NAS-free), routed profile carried from the connector wire source, channel context consumed from the connector; Nous auth forensics + nous_session_valid on /api/status for hosted self-heal; Docker re-seeds a terminally-dead Nous bootstrap session on boot (#60730, #60586, #64649, #59976, #59969, #59983 — @benbarclay)

📱 Messaging Platforms

• Inline choice pickers for /reasoning and /fast on Telegram, Discord, and Matrix — one-tap native buttons instead of typing (#65799 — @teknium1)
• WhatsApp: native Baileys polls (clarify renders as a poll), locations, rich inbound metadata; dashboard pairing flow (#58865, #60571 — @teknium1)
• Discord: recover messages missed during reconnect; auto-created threads renamed to generated session titles; configurable interactive view timeout; opt-in owner mentions on exec-approval prompts; optional admin-only gate for approval buttons (#66149, #60187, #60230, #60493, #51751 — @teknium1)
• Slack: live per-tool status line (#67080 — @teknium1, salvaging #62007)
• Telegram: per-topic free-response allowlist; Google Chat clarify prompts rendered as cards (#65543, #65546 — @teknium1)
• Voice: stt.echo_transcripts toggle; MEDIA: captions attached to the media bubble on standalone sends; display.tool_progress: log option (#58859, #61415, #57014 — @teknium1, @kshitijk4poor)

🖥️ Hermes Desktop App

• Contribution-driven shell on a layout-tree model — panes, zones, and layouts as data; plugin-scoped i18n locale bundles followed (#60638, #67303 — @OutThisLife)
• Capabilities page — Skills/Tools/MCP + Hub in one place, with responsive overlay nav; CLI/dashboard parity for skills hub, MCP test/toggle/catalog, maintenance ops, log filters; five UX fixes from live testing (#57590, #57441, #67482 — @OutThisLife, @teknium1)
• Hermes Cloud connection mode (salvage of #55402); soft gateway switch + gateway-settings polish; terminal execution backend picker with health probes (#61912, #61916, #67203 — @OutThisLife, @teknium1)
• Keybind hint tooltips + keybinds settings tab + unified worktree dialog; base-branch picker for new worktrees; green unread dot for background-finished sessions; background-task sidebar indicators; grouped tool calls across text-less messages; auto-scrolling window for long tool-call runs (#65204, #62243, #65109, #65174, #61147, #57913 — @ethernet8023, @OutThisLife)
• Session + project color system (inherit from project, per-session override, shared across sidebar/tabs); unified active-project identity in chat status; workspace path status action (#67469, #67681, #67282, #63086 — @OutThisLife)
• Declarative memory-provider panel + full-config modal; config-defined TTS/STT providers + xAI TTS params; custom endpoint settings; per-job cron model picker; profile-aware approval mode control; UI scale setting; Ctrl/Cmd+wheel zoom; chat backdrop toggle; /journey opens the memory graph overlay (#67206 salvaging @erosika, #67209, #67759 — @austinpickett, #67472, #63520, #60457, #67029, #64598, #57267 — @teknium1, @OutThisLife)
• Full TypeScript conversion of the desktop tree (#57855 — @ethernet8023)

📊 Web Dashboard

• Memory provider switching; safe session import flow; WhatsApp pairing; Discord-specific toolsets editable from the web UI; clarified manual Telegram bot setup (#60569, #63699, #60571, #65361, #64636 — @teknium1, @kshitijk4poor, @shannonsands)
• Terminal keep-alive + reattach for dashboard chat sessions; heavy turns isolated in a compute host; paste/drop images into Chat; browser.headed schema toggle; profile + gateway topology on /api/status; mobile/hosted OpenAI OAuth login (#60515, #65895, #61929, #67046, #60537, #61330 — @teknium1, @OutThisLife, @benbarclay)
• hermes serve is a true headless backend (no web UI build/mount) (#55923 — @OutThisLife)

🧰 CLI & TUI

• /subscription + /topup terminal billing (see Highlights) (#51639 — @alt-glitch)
• /model --once — one-turn model override that reverts automatically (#67113 — @teknium1, salvaging #29923)
• Stacked slash-skill invocations — /skill-a /skill-b do XYZ loads both skills in order (Claude Code port), with autocomplete + ghost text (#57987, #58763 — @teknium1)
• --safe-mode troubleshooting flag; uninstall dry-run; TLS failures fail fast with fix hints; /compact alias + preview flags; pip/Homebrew installs warned unsupported (#45300, #60111, #57992, #57029, #57225 — @teknium1, @ethernet8023)
• TUI: model picker refresh support; custom skill bundles dispatched as agent turns; banner sizes skills display to terminal width (#59782 — @helix4u, #62859 — @Adolanium, #40624 — @teknium1)
• Hermes Console REPL + perf follow-ups; hermes curator usage all-skills view; entry-point plugins surfaced in hermes plugins list (#57781 — @kshitijk4poor, #36727, #40623 — @teknium1)

🔧 Tool System, Skills & MCP

• MCP: mcp__server__tool naming convention; server log notifications surfaced in agent.log; hosted OAuth completed across Dashboard + Desktop; configurable redirect_uri/redirect_host for proxied/WAF setups; OAuth callback port races closed; Blender added to the MCP catalog with a curated 4-tool default (#52750, #57416, #66151, #65610, #65622, #64463 — @teknium1, @benbarclay)
• Skills: security/unbroker (autonomous data-broker removal) + blind opt-out hardening; unreal-mcp companion skill; blender-mcp reworked around the catalog entry; humanizer pattern expansion; mcp-oauth-remote-gateway optional skill (#57438, #57902, #65989, #64715 — @SHL0MS, #65066, #65486 — @teknium1)
• Browser: full snapshots stored on truncation, eval denylist opt-in; computer_use follows cua-driver's verify→escalate ladder (#65923, #67123 — @teknium1)
• Kanban: modal create-task dialog + editable board project directory; Done-card results made obvious; grab-to-pan board scrolling; attachment toolset + CLI with SSRF-guarded URL fetch; project directory captured at board creation (#66333, #63638, #60226, #65698, #63249 — @teknium1)
• Cron: durable execution audit history; one-shot stale-removal race fixed; run-claim TTL derived from HERMES_CRON_TIMEOUT (#61791 — @teknium1, #62014 — @PRATHAMESH75, #59567)
• mem0: self-hosted dashboard backend + recall tuning + setup-wizard mode (#56943, #60494 — @kshitijk4poor, @teknium1)
• Image gen: Codex image inputs; unsupported Codex image accounts classified; tool args recursively normalized by schema (cline port) (#57017, #63627, #52220 — @teknium1, @kshitijk4poor)

🔒 Security & Reliability

• Vertex: credential/project/region resolution through the profile secret scope; VERTEX_CREDENTIALS_PATH/GOOGLE_APPLICATION_CREDENTIALS stripped from subprocess env (#56680, #56582 — @srojk34)
• Six P1 hardening PRs salvaged in one pass — browser guards, MEDIA anchoring, .env lockdown, delegate ACP transport (#57660 — @teknium1)
• Media/vision/image-gen local-file reads routed through the shared credential-read guard; native image routing guarded by file-safety policy; unified image-source resolver + terminal-backend confinement (#58709, #58752, #57890 — @teknium1)
• Webhook body-cap sweep: explicit client_max_size on 3 uncapped aiohttp servers + completion sweep; Raft chunked-request body limit; timestamp-bound V2 webhook signatures (#59180, #59215, #58902, #58508 — @teknium1, @srojk34)
• Redaction: Fireworks token prefixes + Telegram transport errors; env-lookup false positives fixed for KEY=value and JSON/YAML config fields; bot tokens scrubbed from Telegram connect/send errors (#58501, #58534, #58915, #58893 — @teknium1)
• computer-use: subprocess env sanitized across all five cua-driver spawn sites (#58889, #59165 — @teknium1)
• Dashboard: managed-files credential guard widened past .env + dir-tree gap closed; OAuth token TOCTOU closed with atomic 0o600 writes; stale dashboards can't recreate deleted profiles (#58222 — @kshitijk4poor, #60236 — @teknium1, #49435 — @LeonSGP43)
• CI: untrusted refs passed through env, not run: interpolation; JS/TS tests wired into CI with source-regex tests banned; js-autofix pushes via PR instead of direct-to-main (#57842 — @jquesnelle, #60707, #65186 — @ethernet8023)
• Docker: terminal network toggle with full-path coverage; Git Bash Mandatory-ASLR install failures detected; Windows updater console hidden during handoff (#59149 — @teknium1, #64651, #66040 — @helix4u)
• Anthropic: request-local clients so the stale/interrupt watchdog never corrupts SQLite; per-profile OAuth file; OAuth login 429 fixed (UA must not be claude-code/) (#67238 — @OutThisLife, #59339, #58178 — @teknium1)
• Gateway/agent: tool_call_id deduplicated across pre-API sanitizers; background review inherits parent reasoning_config for Anthropic cache parity; /new memory extraction moved off the command path (#58350, #64379, #61139 — @teknium1, @kshitijk4poor)

🔁 Reverted in this window (for the record)

• iron-proxy credential-injection egress firewall (#30179 → reverted in #58489) — not shipping in this release
• dynamic-workflow orchestration skill (landed, then reverted) — not shipping
• memory provider-actions extension point (landed, then reverted) — not shipping
• Note: the plugin pre_tool_call approve escalation was reverted mid-window but re-landed in #60504 and ships in this release.

👥 Contributors

450+ people contributed to this release (via commits, co-author trailers, and salvaged PRs) — the biggest contributor window yet. Thank you, all of you.

Core team

• @teknium1 — release lead; TTFT perf wave, delivery + delegation durability, smart approvals, SecretSource, gateway multiplex + profile routing, sessions export, security round, and a ~290-PR community salvage burn
• @OutThisLife — desktop app (the speed wave, layout-tree shell, Capabilities page, session colors, vibe reactions, TUI incremental markdown, perf harness)
• @kshitijk4poor — GPT-5.6 end-to-end, DeepInfra + Upstage Solar providers, perf cluster, compression integrity, mem0, dashboard guards
• @ethernet8023 — CI overhaul (JS/TS tests wired in, autofix-via-PR, python speedups), desktop keybinds/worktrees/status indicators, full desktop TypeScript conversion
• @benbarclay — relay OIDC provisioning, gateway multiplex override, Nous auth self-heal, hosted MCP OAuth groundwork
• @alt-glitch — terminal billing (/subscription, /topup), desktop billing tab
• @helix4u — desktop provider/model UX, TUI model picker refresh, Windows install/updater hardening
• @austinpickett — desktop custom endpoint settings
• @SHL0MS — unbroker + unreal-mcp skills, humanizer expansion

Top community contributors

• @srojk34 — security hardening: Vertex credential/project/region scoping through the profile secret scope, subprocess env stripping, Raft chunked-request body limits
• @HexLab98 — 11 fixes across MCP capability gating, Windows installer PATH, desktop cron editing, gateway systemd warnings
• @UnathiCodex — desktop stability: zoom across display moves, LaTeX rendering, resume-stall and runtime-readiness fixes
• @xxxigm — <think> leak fix after thinking-only retry flush, dashboard auth/theme/PTY fixes
• @erosika — desktop declarative memory-provider panel + honcho recall/timeout correctness
• @Frowtek — credential security: master stores never mounted into skill sandboxes, live-transcript redaction, dashboard api_key precedence
• @necoweb3 — browser private-page CDP guard, cron one-shot liveness, gateway compression fail-closed
• @DavidMetcalfe — desktop updater version pill, Local/custom endpoint exposure, sidebar collapse behavior
• @shannonsands — dashboard: mobile channel setup, Discord toolsets from web UI, Telegram setup clarity
• @vishal-dharm — Gemini request-context improvements
• @PRATHAMESH75 — cron one-shot stale-removal race, dashboard multiplex port-binding guard
• @alelpoan, @embwl0x, @Adolanium, @giggling-ginger, @Drexuxux, @frizikk, @JoaoMarcos44, @wesleysimplici, @LeonSGP43, @pierrenode, @simpolism, @MorAlekss, @r266-tech, @WadydX, @nv-kasikritc — targeted fixes across desktop, TUI, gateway, cron, webhook, nix, and browser surfaces
• Salvaged-work authors whose PRs were cherry-picked with credit this window: @Burgunthy (profile routing), @web3blind (sessions export), @hwrdprkns (1Password), @Christopher-Schulze, @Ahmett101, @sjiangtao2024, and many more — see the salvage PR bodies for full attribution

All contributors

@0-CYBERDYNE-SYSTEMS-0, @0disoft, @0xbyt4, @100yenadmin, @17324393074, @2751738943, @8294, @abhibansal-sg,
@adambiggs, @Adolanium, @aeyeopsdev, @aguung, @AhmetArif0, @Ahmett101, @ai-ag2026, @AIalliAI, @ajzrva-sys,
@alastraz, @alelpoan, @alex-fireworks, @alex-heritier, @alex107ivanov, @AlexFucuson9, @Alix-007,
@allenliang2022, @Almurat123, @AlsayedHoota, @alt-glitch, @alvarosanchez, @amanning3390, @AmAzing129,
@AndreasHiltner, @andrewhomeyer, @annguyenNous, @ansel-f, @antydizajn, @arminanton, @arnispiekus, @asimons81,
@asscan, @ats3v, @austinlaw076, @austinpickett, @avifenesh, @aydnOktay, @Bartok9, @bautrey, @bbednarski9,
@bbopen, @benbarclay, @bigstar0920, @binhnt92, @bird, @Black0Fox0, @BlackishGreen33, @bo.fu, @brendandebeasi,
@briandevans, @BROCCOLO1D, @Bruce-anle, @brunz-me, @Burgunthy, @bytesnail, @catbearlove1-lang, @Cdddo,
@cgarwood82, @CharmingGroot, @chouqin, @Christopher-Schulze, @claudlos, @CocaKova, @Code-suphub, @CodeForgeNet,
@craigdfrench, @CrazyBoyM, @crazywriter1, @cresslank, @cruzanstx, @cyrkstudios, @danilofalcao,
@datachainsystems, @DatTheMaster, @davidb73-hub, @davidgut1982, @DavidMetcalfe, @davidrobertson,
@deacon-botdoctor, @DECK6, @deepujain, @derek2000139, @designnotdrum, @deusyu, @devatnull, @devorun,
@dexhunter, @dfein38347g, @Dhravya, @DictatorBacon, @digitalbase, @dlkakbs, @dmabry, @DNAlec, @dodo-reach,
@doncazper, @dorokuma, @doxe0x, @Drexuxux, @dschnurbusch, @Dusk1e, @EdderTalmor, @egilewski, @elashera,
@Elektrofussel, @eliteworkstation94-ai, @embwl0x, @emo-eth, @emozilla, @enzo-adami, @Epoxidex, @ErnestHysa,
@erosika, @esthonjr, @ethernet8023, @evefromwayback, @evelynburger, @F4TB0Yz, @falkoro, @fanyangCS, @firefly,
@fjlaowan1983, @flewe, @flo1t, @flow-digital-ny, @floze-the-genius, @frizikk, @Frowtek, @FuryMartin,
@fyzanshaik, @gauravsaxena1997, @geoffreybutler94, @georgedrury, @gigakun3030, @giggling-ginger,
@Git-on-my-level, @gitcommit90, @githubespresso407, @gnodet, @GottZ, @Gridzilla, @grimmjoww578, @gumclaw,
@Gutslabs, @HaiderSultanArc, @harjothkhara, @heathley, @hejuntt1014, @helix4u, @HeLLGURD, @hellno,
@herbalizer404, @HexLab98, @hmirin, @Hopfensaft, @Hotragn, @hsy5571616, @huanshan5195, @HumphreySun98,
@hwrdprkns, @hydracoco7, @hydraxman, @iamlukethedev, @iborazzi, @IgorGanapolsky, @iizotov, @ildunari,
@infinitycrew39, @IpastorSan, @irresi, @isfttr, @isheng-eqi, @itsflownium, @izumi0uu, @Jaaneek, @JacketPants,
@jaisup, @jakelongvu-bot, @jakepresent, @jaketracey, @JAlmanzarMint, @JasonFang1993, @jbbottoms, @jcjc81,
@JiaDe-Wu, @Jiahui-Gu, @Jigoooo, @jingsong-liu, @jneeee, @JoaoMarcos44, @joelbrilliant, @John-Lussier, @jplew,
@jtstothard, @juniperbevensee, @Jupiter363, @justinschille, @k4z4n0v4, @kaishi00, @karfly, @kartik-mem0,
@kavioavio, @KCAYAAI, @kenyonxu, @keslerm, @kevinrajaram, @knoal, @kocaemre, @kohoj, @konsisumer, @krowd3v,
@kshitijk4poor, @kuangmi-bit, @kubolko, @kyssta-exe, @Kyzcreig, @l0h1nth, @labsobsidian, @laurinaitis,
@LavyaTandel, @lawyer112, @lemonwan, @LeonSGP43, @lEWFkRAD, @linfeng961, @liuhao1024, @liuwei666888, @ljy-2000,
@loes5050, @logical-and, @LoicHmh, @loongfay, @lord-dubious, @lost9999, @lucasfdale, @lucaskvasirr,
@luxuguang-leo, @ly-wang19, @m0n5t3r, @m1qaweb, @M1racleShih, @MaartenDMT, @mahdiwafy, @MaheshBhushan,
@ManniBr, @marcelohildebrand, @marcolivierlavoie, @markoub, @MarkVLK, @Marxb85, @matantsevs,
@maxpetrusenkoagent, @mbac, @mdc2122, @mguttmann, @Mibayy, @michaelHMK, @mijanx, @minchang, @momomojo,
@MorAlekss, @morluto, @msh01, @mssteuer, @mvanhorn, @nanami7777777, @nankingjing, @necoweb3, @neo-claw-bot,
@neoguyverx, @nicha16, @nikshepsvn, @nima20002000, @nnnet, @NousResearch, @nullptr0807, @nv-kasikritc,
@okisdev, @OmarB97, @ooiuuii, @ooovenenoso, @oppih, @Osraka, @ostravajih, @otsune, @OutThisLife, @OYLFLMH,
@patrick-muller, @pdmartins, @pedrommaiaa, @Peterskaronis, @petrichor-op, @pgregg88, @pierrenode, @pixel4039,
@plcunha, @pnascimento9596, @Polyhistor, @PRATHAMESH75, @professorpalmer, @Punyko8, @Que0x, @Qwinty,
@r0gersm1th, @r266-tech, @rabadaki, @ragingbulld, @RainbowAndSun, @rainbowgore, @randimt, @rarf, @rasitakyol,
@rayjun, @raymondyan-zhijie, @re-ITRT, @RenoMG, @Rival, @RKelln, @rlaehddus302, @rob-maron, @rodboev,
@roryford, @rungmc357, @ruslanvasylev, @s0xn1ck, @s905060, @s96919, @sahibzada-allahyar, @sahil-shubham,
@Sahil-SS9, @SahilRakhaiya05, @sam7894604, @SAMBAS123, @samrusani, @sanidhyasin, @sasquatch9818, @sberan,
@ScotterMonk, @seagpt, @sebastianlutycz, @SemonCat, @setclock, @shannonsands, @sharziki, @shashwatgokhe,
@SHL0MS, @shuangxinniao, @SilentKnight87, @simplast, @simpolism, @SiteupAgencia, @sjiangtao2024, @sk-holmes,
@slow4cyl, @smtony, @soddy022, @Soju06, @solyanviktor-star, @SongotenU, @spiky02plateau, @sprmn24, @SquabbyZ,
@srojk34, @ssiweifnag, @stantheman0128, @StellarisW, @stephenschoettler, @suninrain086, @superposition,
@Supersynergy, @sweetcornna, @szafranski, @tanmayxchoudhary, @tarunravi, @tcconnally, @terry197913, @Thatgfsj,
@thegoodguysla, @thestudionorth, @TheTom, @TinkerOfThings, @tjboudreaux, @tjp2021, @Tortugasaur, @Tosko4,
@Tranquil-Flow, @trevorgordon981, @trismegistus-wanderer, @tt-a1i, @tuancookiez-hub, @TurgutKural, @Umi4Life,
@UnathiCodex, @unsupportedpastels, @uzaylisak, @valda, @vampyren, @veradim, @victor-kyriazakos, @virtualex-itv,
@vishal-dharm, @Vissirexa, @vizi0uz, @vkkong, @vKongv, @VolodymyrBg, @vortexopenclaw, @VrtxOmega, @WadydX,
@waroffchange, @waseemshahwan, @web3blind, @webtecnica, @wesleion, @wesleysimplicio, @williamumu,
@WilsonKinyua, @wxy-nlp, @wyuebei-cloud, @x7peeps, @x9x9x9x9x9x91, @xuezhaolan, @xxxigm, @ya-nsh, @yatesjalex,
@ygd58, @yingliang-zhang, @yinkev, @YLChen-007, @yu-xin-c, @yungchentang, @zapabob, @zccyman, @zeapsu,
@ziliangpeng, @zwcf5200, @zzpigpinggai

Also: bo.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.

Full Changelog: v2026.7.1...v2026.7.20

The post Kimai Docker Flaw Lets Unauthenticated Attackers Forge Cookies and Take Over Accounts appeared first on Cyber Security News.

Read more →

The post Kimai Docker Flaw Lets Unauthenticated Attackers Forge Cookies and Take Over Accounts appeared first on IT Security News.

The post Kimai Docker Vulnerability Exposes Default APP_SECRET, Enabling Account Takeover appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

Read more →

The post Kimai Docker Vulnerability Exposes Default APP_SECRET, Enabling Account Takeover appeared first on IT Security News.

The post Critical Kimai Docker Flaw Lets Hackers Forge Cookies and Hijack Admin Accounts appeared first on Cyber Security News.

• Fixed single-segment dir/** allow rules like Edit(src/**) auto-approving writes to nested dir/ directories anywhere in the tree instead of only <cwd>/dir
• Fixed a permission-check bypass affecting commands run in Windows PowerShell 5.1 sessions
• Fixed Bash permission checks to fail closed on file-descriptor redirect forms that bash parses differently than the permission analyzer
• Fixed Bash permission checks misjudging very long commands — commands over 10,000 characters now always prompt instead of running automatically
• Fixed Bash permission checks treating zsh variable subscripts and modifiers in [[ ]] comparisons as inert text — these commands now prompt for approval
• Fixed Bash permission checks to no longer auto-approve certain help and man commands that could run unsafe options, command substitutions, or backslash paths
• Fixed permission prompts on remote sessions that could proceed before the local confirmation dialog
• Added the EndConversation tool: Claude can end sessions with highly abusive users or jailbreak attempts, as on claude.ai since 2025 — see https://www.anthropic.com/research/end-subset-conversations
• Added a periodic progress heartbeat for long-running tool calls that previously went silent
• Added an ISO modified timestamp to memory file frontmatter
• Added message.uuid, client_request_id, and tool_source attributes to OpenTelemetry log events for message-level correlation and tool provenance
• Added CLAUDE_CODE_OTEL_CONTENT_MAX_LENGTH to configure the 60 KB truncation limit on OpenTelemetry content attributes
• Added reasoning effort to the subagentStatusLine payload, so custom agent rows can render model and effort
• Added permission prompts for docker commands (including the Podman docker shim) carrying daemon-redirect flags (--url, --connection, --identity, and Podman's remote mode) that previously ran without one
• Fixed a crash when a GrowthBook feature evaluates to null, and a bug where a malformed flag payload could wipe the cached feature flags
• Fixed Bash tool killing the Claude session when a pkill -f pattern accidentally matched the CLI's own process (Linux)
• Fixed unbounded memory growth when --settings points at a device file or multi-GB file; oversized (>2 MiB) settings files now fail at startup with a clear error
• Fixed streaming turns failing with "Socket is closed" behind corporate proxies on Windows
• Fixed stream-json output truncation at exit for slow-reading SDK/pipeline consumers; the exit drain now scales with queued bytes instead of a flat 2s cap
• Fixed scheduled tasks refusing their own configured prompt as untrusted input — the fired prompt is now delivered as the session's assigned task
• Fixed PowerShell tool commands hanging until timeout when a child process waited on standard input (Windows)
• Fixed Python scripts under the PowerShell tool crashing with UnicodeDecodeError when reading non-UTF-8 data from standard input (Windows)
• Fixed Python scripts run via the PowerShell tool crashing with UnicodeEncodeError on non-ASCII output, and PowerShell 7 error messages containing raw ANSI escape sequences (Windows)
• Fixed the PowerShell tool reporting where.exe, fc.exe, and diff.exe as errors when they return a valid negative answer (Windows)
• Fixed > and >> under the PowerShell tool on Windows PowerShell 5.1 writing UTF-16LE files that other tools couldn't read as UTF-8
• Fixed a displaced background daemon deleting its successor's control socket on shutdown, which made the next client kill the healthy replacement daemon
• Fixed background sessions parked with ← or /background and left idle keeping the background daemon and a worker process alive indefinitely
• Fixed completed background sessions being impossible to remove via claude rm or the agent view once the background service had gone idle
• Fixed background sessions dispatched from a non-git folder being impossible to delete from the agents view
• Fixed reopening a stopped background session failing to restore its saved conversation when an unreadable folder exists in the session store
• Fixed the Remote Control "session ready" push notification firing for sessions where Remote Control was not explicitly enabled
• Fixed /install-github-app and the /mcp settings menu being blocked in agent-view sessions — they're now refused only in background sessions with no terminal attached
• Fixed plugins enabled via the --settings CLI flag not loading (regression since v2.1.181)
• Fixed feature flags going stale in long-running sessions after the OAuth token rotates
• Fixed /ultrareview refusing to run in repos with no merge base — it now offers to review all tracked files
• Fixed claude update and claude doctor hanging silently, and the /status System diagnostics section going blank, when a shell-config path is a directory
• Fixed memory frontmatter values being silently truncated at an inline # when memory files are saved
• Fixed session cost and token telemetry double-counting on streams that emit multiple cumulative message_delta frames
• Fixed a spurious "check your network" warning that appeared while the advisor was thinking
• Fixed hooks with exit code 2 not blocking as documented when the hook's stdout JSON fails schema validation
• Fixed OTel log events emitted outside the turn's async context missing the interaction span's trace context
• Fixed MCP transient errors during prompts/resources refresh clearing the server's slash commands and resources
• Improved the claude rc workspace-trust error in the home directory to say trust there is never saved and to suggest running from a project directory
• Changed single-segment dir/** hook if: conditions to match only <cwd>/dir; write **/dir/** for any-depth matching. deny/ask permission rules keep their any-depth match.
• Changed file commands using -m/--magic-file or -f/--files-from to require permission instead of being auto-allowed as read-only
• Changed keep-alive connection pooling to disable after a stale-connection error, so retries open a fresh socket
• Changed SessionStart hooks to report source "fork" when a session begins as a fork instead of "resume"

Dieser Beitrag NadMesh: Botnet jagt KI-Services nach Cloud-Keys und Kubernetes-Tokens erschien als erstes auf IT BOLTWISE x Artificial Intelligence.