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.

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The Cyber Express Weekly Roundup 

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Craneware Data Breach Exposes Employee and Customer Information 

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Chick-fil-A Customer Accounts Targeted in Credential Attack 

South Korea Diplomatic System Breach Lasted Nearly 10 Months 

Weekly Cybersecurity Takeaway 

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The post Russian Hackers Exploiting Zimbra Zero-Day to Steal 90 Days of Emails appeared first on Cyber Security News.

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

The post Russian LAUNDRY BEAR Hackers Exploit Zimbra Zero-Day to Steal 90 Days of Emails appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

The company announced that GPT-Live now powers the ChatGPT desktop application on macOS and Windows, integrating directly with agentic systems like Codex and ChatGPT Work (which are separate experiences available in the ChatGPT desktop app).

When OpenAI initially launched GPT-Live on July 8, 2026, it introduced a continuous audio model capable of listening and speaking simultaneously—eliminating rigid turn-taking while delegating complex reasoning to background models like GPT-5.5.

Today's release expands that conversational layer to technical tasks, enabling software engineers to orchestrate multi-threaded coding jobs, review pull requests, and debug applications using natural voice commands.

As such, it could usher in a new era of "hands free" software development and even live, in-person group coding parties for Codex's more than 5 million weekly active users. Codex, of course, is the name given to OpenAI's models and harness focused on coding, but which the company has this year expanded into a more general productivity platform. An OpenAI spokesperson told VentureBeat this is the first time voice activation has been included natively with Codex on the desktop.

OpenAI posted a promotional video showing some of its employees, Codex developer experience engineer Jason Liu and Codex technical staffer Guinness Chen, speaking to the same ChatGPT desktop app session in the same room, each issuing different instructions and conversing with the same model.

New capabilities unlocked

At its core, this integration relies on decoupling the real-time voice layer from the underlying execution engines.

While GPT-Live maintains fluid conversation—inserting natural verbal acknowledgments like "got it" without interrupting the user—it passes heavy computational workloads to background reasoning models.

On macOS, the desktop application incorporates "Appshots" and screen context features, allowing ChatGPT Voice to analyze the frontmost window alongside local files, codebase structures, and active plugins.

This architecture creates a pair-programming dynamic where developers talk through problems conversationally while agents execute tasks asynchronously.

Rather than manually stopping coding sessions to type detailed instructions or switch windows, developers direct the system hands-free.

The full-duplex engine dynamically decides when to speak, pause, or invoke tools, maintaining conversational state even as background agents process complex code modifications.

Directing coding and complex builds with your voice alone

The central operational capability in this update centers on multi-task execution across Codex and ChatGPT Work environments.

Software engineers can initiate multiple concurrent task threads from a single spoken prompt. For instance, a developer preparing to ship a feature can instruct the system to investigate an open authentication bug, review a pending API migration pull request, and generate missing unit tests simultaneously.

The desktop application coordinates these actions across disparate contexts, tracing issues through Slack conversations, GitHub repositories, and local codebases.

Developers can also verbally convert design mockups into working code, splitting tasks across frontend, backend, and testing layers.

With support for multi-folder projects (build 26.715) and remote execution via iOS, engineers can check task progress, answer agent prompts, and redirect active jobs without switching applications or managing individual processes line by line.

Proprietary license

OpenAI’s voice-enabled desktop release operates under a proprietary, commercial enterprise model. Access is restricted to paid subscribers across Plus, Pro, Business, Enterprise, and Education plans.

For individual developers and corporate engineering departments, this commercial structure means the model weights, voice processing pipelines, and agent state architectures remain fully closed.

Organizations cannot modify or self-host the underlying systems. Furthermore, tasks initiated via ChatGPT Voice consume standard usage allocations directly from existing Codex and ChatGPT Work plan quotas, treating voice-triggered actions identically to standard agentic workloads.

Community reactions

Developer communities immediately noted the implications of bringing continuous full-duplex voice to autonomous coding workflows.

Reacting to the build 26.715 release announcement—which details voice integration and multi-folder project support—AI Insider journalist @ChrisGPT noted on X: "Today OpenAI will release voice and remote guidance for codex ! One step closer to personal AGI".

Early technical feedback highlights widespread enthusiasm for orchestrating complex agentic tasks hands-free, particularly when stepping away from the workstation or managing build pipelines remotely.

This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them.

The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production.

Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education.

At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators.

Finding 1: Confident and wrong

More than half have traced agent errors to bad context

We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had.

This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know.

The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem.

Finding 2: RAG is the default context source

Retrieval feeds more agents than any other method

We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin.

Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface.

One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business.

Finding 3: Provider-native retrieval already leads the vector databases

OpenAI file search and vertex AI search top the dedicated tools

We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists.

The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy.

The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from.

Finding 4: But they say they want to keep best-of-breed

A plurality resist consolidating onto a provider’s native stack

We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage.

Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool.

Finding 5: Hybrid retrieval is the consensus bet

Vector-only retrieval is already seen as insufficient

We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure.

The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed.

Finding 6: The governed context layer is being built now

Most run or are building a semantic layer — few in production

We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived.

The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production.

Finding 7: Bought on ingestion and simplicity, watched for correctness

Selection favors operability; monitoring favors correctness and security

We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical.

Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%).

Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted.

Finding 8: A retrieval reshuffle is coming

A majority plan to change providers — and the vector specialists are gaining interest

We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack.

The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle.

The bottom line: A context gap that more retrieval alone won’t close

Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation.

The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.

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Executive summary 

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

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

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

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

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

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

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

For a downloadable list of IOCs, see:

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

Cybersecurity industry tracking

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

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

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

Background

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

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

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

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

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

Targeting details

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

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

Technical details

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

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

Reconnaissance

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

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

Resource development

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

Flowerbed framework

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

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

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

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

Initial access

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

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

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

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

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

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

Persistence and credential access

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

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

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

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

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

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

Collection

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

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

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

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

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

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

Exfiltration

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

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

DNS exfiltration

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

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

HTTPS exfiltration

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

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

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

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

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

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

Catcher

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

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

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

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

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

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

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

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

Response strategies

Mitigations

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

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

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

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

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

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

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

Indicators of compromise (IOCs)

Flowerbed infrastructure

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

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

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

Phishing distribution

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

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

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

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

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

Post-compromise artifacts

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

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

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

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

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

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

Remediation

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

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

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

Works cited

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

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

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

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

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

Footnotes

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

Acknowledgements

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

Disclaimer of endorsement

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

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

Purpose

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

Contact

Appendix A: MITRE ATT&CK tactics and techniques

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

Appendix B: MITRE D3FEND countermeasures

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

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The post Hackers Breach South Korea’s Diplomatic Academy and Expose Foreign Ministry Staff Data appeared first on IT Security News.

The post Hackers Breach South Korea’s Diplomatic Academy and Expose Foreign Ministry Staff Data appeared first on Cyber Security News.

National Diplomatic Academy Data Breach Remained Active for Nearly 10 Months 

Thousands Impacted in South Korea Foreign Ministry Data Breach 

What Information was Exposed? 

• User IDs 
• Names 
• Email addresses 
• Encrypted passwords 

India online safety rules are being strengthened as the government steps up measures to protect children and other users from harmful digital content, cyber risks and emerging threats linked to artificial intelligence. The government said its policies are aimed at ensuring an open, safe, trusted and accountable internet, with recent measures focusing on child safety, privacy protection, faster content removal and stronger platform responsibilities.

The Information Technology Act, 2000, and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, form the core legal framework governing online safety and intermediary responsibilities in India. The government has also highlighted the Digital Personal Data Protection Act, 2023, and recent amendments to the IT Rules as part of its broader approach to digital safety.

India Online Safety Rules Tighten Platform Responsibilities

Under the IT Rules, intermediaries are required to observe due diligence and inform users that they must not host, display, upload, modify, publish, transmit, update or share content that is harmful to children or violates applicable laws.

Recent amendments require social media platforms and other intermediaries to remove unlawful content within three hours of receiving an order from a competent court or a reasoned intimation from the appropriate government or its agency.

The government has also outlined specific obligations related to content involving nudity, impersonation and other sensitive material. In cases involving certain complaints about content featuring full or partial nudity, exposed private areas or artificially morphed images, intermediaries must take reasonable and practicable measures to remove or disable access within two hours.

Government Targets Harmful Content and OTT Platforms

The government said it has taken action against online platforms and OTT services over unlawful and obscene content. In the last two years, 50 OTT platforms have been disabled for public access in India for displaying obscene content and violating provisions including Sections 67 and 67A of the IT Act, Section 294 of the Bharatiya Nyaya Sanhita and the Indecent Representation of Women (Prohibition) Act, 1986.

The government also said it has taken note of reports alleging the dissemination of advertisements linked to child sexual abuse material (CSAM) on social media platforms and sought a detailed report from the concerned intermediary. The National Commission for Protection of Child Rights has also issued notices to the concerned platforms.

India Strengthens AI-Generated Content Safeguards

The government has also expanded its regulatory focus to address risks associated with AI-generated content and synthetically generated information. Amendments to the IT Rules introduce requirements for clear labelling and traceable metadata for permissible AI-generated content, allowing users to identify synthetically generated material.

The framework also strengthens platform accountability and requires greater user awareness about the legal consequences of unlawful AI-generated content. The rules specifically cover harmful material including CSAM, non-consensual intimate imagery and impersonation.

Platforms are required to deploy reasonable and appropriate technical measures, including automated tools or other mechanisms, to prevent users from creating, modifying, publishing or sharing synthetically generated information that violates applicable laws.

Significant Social Media Intermediaries are also required to make reasonable efforts to deploy technical measures to proactively identify content depicting rape, child sexual abuse or conduct, as well as content identical to information previously removed.

Child Privacy and Digital Addiction Remain Key Concerns

The Digital Personal Data Protection Act, 2023, provides a framework for protecting children's privacy online. It mandates parental consent for processing children's personal data and prohibits practices considered detrimental to children's well-being, including tracking, behavioural monitoring and targeted advertising directed at children.

The government has also highlighted digital addiction as a serious challenge affecting children and young people. The Economic Survey 2025-26 noted potential impacts on cognitive development, academic performance, workplace productivity, social connectedness and mental health, alongside risks linked to cyberbullying, compulsive gaming, social media and online gambling.

Cyber Awareness Reaches 11.37 Lakh Participants

Alongside regulatory measures, the government is expanding cyber awareness initiatives through the Information Security Education and Awareness project. So far, 6,650 awareness workshops have been conducted nationwide, reaching more than 11.37 lakh participants, including students, teachers, law enforcement officials, government personnel and members of the public.

The government has also highlighted digital safety initiatives in education. The PRAGYATA Guidelines provide a framework for safe online learning and responsible use of social media and electronic devices. CBSE has introduced digital etiquette and cybersecurity training initiatives, while NCERT has incorporated cyber safety into its curriculum.

The measures were outlined by Union Minister for Electronics and Information Technology Ashwini Vaishnaw in the Lok Sabha on July 22, 2026, as the government continues to strengthen its approach to c child protection and accountability across India's digital ecosystem.

The post Hackers Exploit Security Weaknesses to Compromise South Korean Diplomats’ Personal Data appeared first on Cyber Security News.

Rapid advances in AI are forcing organizations to rethink how people learn new skills. The question isn't only how employees adapt, but whether K–12 education, universities, and professional development can keep pace.

Waiting until workforce shortages appear could mean reacting too late. Updating curricula before major disruptions occur may help prepare students and workers for jobs that increasingly involve AI and automation.

If you could change one thing about today's education system to prepare people for an AI-driven workforce, what would it be?

Subscribe to our podcasts: https://securityweekly.com/subscribe

#Education #FutureOfWork #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

As a reminder, we’ll continue to announce new Gemini Beta features in the Google Developer Program (GDP) forum and the Gemini Beta Help Center. You must be an active Workspace customer and register to access GDP content; follow these instructions to sign up.

Getting started

• Admins: There is no change to existing admin controls. You will begin to see the "Gemini Beta" label replace "Gemini Alpha" in the Admin console and Help Center articles over the next several weeks.
• End users: There is no end user setting for this change.

Rollout pace

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 22, 2026

Availability

• Business: Business Starter, Standard, and Plus
• Enterprise: Enterprise Starter, Standard, and Plus
• Education Add-ons: Google AI Pro for Education

Resources

• Google Workspace Admin Help: Turn access to Google Workspace with Gemini Beta on or off
• Google Developer Program: Workspace Alpha Forum

Anil Seth is right to point out that to overestimate artificial intelligence is to underestimate ourselves (Once again we are told AI may be conscious – I study consciousness, and I have my doubts, 15 July). But he is wrong only to have doubts about whether AI systems like Claude may become conscious. He should be certain, and should say so more forcefully.

It’s not like anything to be Claude, just as it’s not like anything to be a washing machine. An academic education is not required to realise that, common sense will do. Artificial intelligence systems won’t become conscious for the same reason they won’t become pregnant; they’re not that sort of thing.

The new experience, which will begin rolling out on July 27, 2026, is personalized based on a user's role and available features:

• For teachers, a new dashboard gives actionable insights, highlights student classwork interactions, tracks assignment completion, and surfaces a feature spotlight to help discover instructional tools and resources.

• For students, a dedicated ‘Enrolled’ view reminds learners of coursework that is due soon and helps them manage their deadlines.

• For school leaders and IT administrators, the homepage provides a centralized view to monitor high-level performance analytics, access shortcuts for backend administrative settings, and discover relevant tools to support educators and staff.

Users who have multiple roles (such as a teacher taking a professional development class) can easily change their view dashboard by clicking into another role (for example, Teaching, Enrolled, or Admin). When a user loads the homepage, it returns to the previous role view.

To help users control their view and focus on what matters most to them, all new homepage modules are collapsible.

Please note that not all features and views will be available to all users. Eligibility is determined by the user’s role, feature access, account type, and settings.

Getting started

• Admins: There is no admin control for the new Classroom homepage. Gemini and Gemini Notebook features will only appear if the user is in an OU with Gemini in Classroom, Gemini app, and/or Gemini Notebook enabled. Visit the Help Center to learn about managing access to Gemini in Classroom, Gemini app, Gemini Notebook, and the option to turn these services on or off for users in the Admin console.
• End users: There is no end user setting for the new Classroom homepage. Visit the Help Center to learn more about the new Classroom homepage.

Rollout pace

• Rapid Release and Scheduled Release domains: Full rollout (1-3 days for visibility) starting July 27, 2026

Availability

• Available to all Google Workspace customers, Workspace Individual subscribers, and users with personal Google accounts

Resources

• Google Classroom Help: Navigate your Classroom Homepage
• 2026: What’s New in Google for Education: Overview of Classroom Homepage

The post Where’s the Trump administration line on AI regulation? appeared first on CyberScoop.

The post Download YouTube App For Windows 11 PC (Offline Installer) appeared first on iTech Hacks.

There’s a moment in every appsec learner’s journey where a vulnerability stops being a bullet point on the OWASP API Top 10 and starts being something you actually did. For me, that moment was watching one user’s card get funded by another user’s session — no exploit chain, no payload, just a number in a URL that should never have worked.

This is the walkthrough of how I found (and rigorously confirmed) a Broken Object Level Authorization vulnerability in VulnBank, an intentionally vulnerable banking application built for security training.

What Is BOLA, Actually?

Broken Object Level Authorization sits at #1 on the OWASP API Security Top 10 (API 1: 2023), and for good reason — it’s common, trivial to exploit, and quietly devastating.

The core idea in one sentence: the server correctly checks who you are, but never checks what you’re allowed to touch.

Any API endpoint that takes an object identifier — a card_id, account_number, order_id — needs to answer two separate questions:

1. Authentication: is this a valid, logged-in user?
2. Authorization: should this specific user be allowed to access this specific object?

BOLA is what happens when an API nails question one and skips question two entirely. Usually it’s one missing clause in a query.

The vulnerable version:

SELECT * FROM cards WHERE id = :card_id

The fixed version:

SELECT * FROM cards WHERE id = :card_id AND user_id = :authenticated_user_id

That’s genuinely the whole difference and because it never breaks anything during normal use (your own IDs always belong to you), it hides in plain sight until someone deliberately tries an ID that isn’t theirs.

So that’s exactly what I did — with two accounts, on purpose, so I could prove it beyond doubt rather than just suspect it.

Setting the Stage: Two Users, Two Cards

Testing BOLA against yourself proves nothing — you always have legitimate access to your own resources. So I set up two separate accounts to simulate a real attacker/victim scenario.

User 1 — Jhonny

• I created a virtual card with a $2,500 limit.

• I then funded it with $80 from the main balance.

With the funding request captured in Burp Suite, I sent it to Repeater for closer inspection, this is the request whose card_id parameter would become the centerpiece of the whole test.

User 2 — Alex

Same setup:

• A fresh virtual card of $2,500 limit.

• Funded with $100.

• And the same treatment — captured the request and sent it to Repeater.

Two accounts, two cards, two independent funding requests sitting side by side. Now the real test could begin.

Step One: Does the App Even Check Who You Are?

Before hunting for authorization flaws, I checked the basics. I stripped the session cookie and Authorization header from a funding request entirely and sent it.

401 Unauthorized: "Token is missing."

Good! The server clearly enforces authentication. That ruled out the simplest failure mode and pointed straight at the real question: does it check which authenticated user is making the request, or just that one is?

Step Two: The Swap

This is the actual test, and it’s almost anticlimactic in how simple it is.

I took Jhonny’s valid token and used it to fund Alex’s card:

200 OK. The card funded successfully with Jhonny's session authorizing a change to Alex's card.

Then I reversed it, Alex’s token, aimed at Jhonny’s card:

200 OK again. Same result, opposite direction.

Neither request was rejected. The server verified that a valid token was present but never verified that the token holder actually owned the card they were funding. It simply processed whatever card_id showed up in the URL, against whichever authenticated user happened to be making the call.

Why This Isn’t “Just a Feature”

The first pushback any BOLA finding gets is: “Couldn’t this just be an intentional transfer feature?”

It’s a fair question, and worth addressing directly.

The answer is NO, for a few concrete reasons:

• There was no recipient search, no username/email lookup, no way to intentionally select another user through the interface.
• Neither Jhonny nor Alex received any notification or gave any consent.
• The card IDs used were never exposed to either user by the application itself, they were reached only by directly editing a request in Burp, not by anything the UI ever presented as selectable.
• Both requests used each user’s own main balance and own token throughout, nothing about the flow resembled a designed transfer mechanism.

A designed feature has guardrails: consent steps, recipient verification, fraud checks. This had none of that, because it was never meant to be reachable in the first place.

The Fix

The remediation here is almost anticlimactic given the impact. This isn’t a hard problem to solve, just an easy one to forget:

• Every object-level query needs an explicit ownership check tied to the authenticated session: WHERE card_id = ? AND user_id = ?
• Better yet, enforce this centrally, an authorization layer or middleware that every object-fetching endpoint routes through, rather than relying on each developer to remember it per-endpoint
• Make cross-account testing a standard part of QA and code review: test with two different authenticated accounts against each other’s objects, not just each account against its own.

The Takeaway

BOLA doesn’t require exotic tooling or deep exploit development. It requires one thing: noticing that an ID in a URL is just a number, and asking whether the server actually checked if you were allowed to use it.

In this case, it hadn’t. Two independent accounts, each fully authenticated, could reach into each other’s resources without so much as a warning.

Authentication tells a server who is asking. Authorization is the separate and often forgotten question of what they’re allowed to ask for?. Every API needs both, and it’s worth checking, endpoint by endpoint, that yours actually has them.

This testing was performed against VulnBank, an intentionally vulnerable application built for security education and training purposes.

I Funded a Stranger’s Bank Card With My Own Money; and That’s Exactly the Problem was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

Read more of this story at Slashdot.

Read more: 5 of the hottest STEM camps for young people this summer

Young people today have unprecedented opportunities to learn, connect, and explore — not just the web and the world, but also themselves. With the increased ubiquity of digital technologies and devices, worries around the relationship between these technologies and young people’s well-being have grown, too. While concerns about the societal implications of new technologies is not a new phenomenon, experts argue that the accelerating speed of deployment of new technologies has outpaced scientists’ capacity to feed into policy recommendations addressing risks. A growing body of research documents the harms experienced by young people online and the challenges reported by parents attempting to mediate their kids’ technology use. At the same time, experts highlight the importance of contextual factors like existing mental health conditions, socio-economic circumstances and parental mediation to understand the real-world effects of digital technologies.

Faced with this complexity, and mounting public pressure, policymakers around the world are urgently seeking ways to improve child safety online. Driven by a sense of time running out and promises of new technical solutions to difficult questions, this has led, across jurisdictions, to proposals to restrict young people’s access to certain technologies or platforms by introducing age assurance mandates.

Privacy and user empowerment have always formed a core part of Mozilla’s mission. As we have said before, we support safer spaces for minors, but we caution against approaches that rely on identity checks, surveillance-based enforcement, or exclusionary defaults. Such interventions rely on the collection of personal and sensitive data and, thus, introduce major new privacy and security risks.

While many technologies exist to verify, estimate, or infer users’ ages, fundamental tensions around accessibility, their effectiveness and effects on user’s privacy, security and free expression remain. Technological approaches must be part of wider efforts to address the root causes of online harms. However, the deployment of age assurance technologies will not solve the complex challenge of preparing young people to navigate an increasingly online world and ensure their wellbeing. That will require more holistic approaches: offering education and support to navigate the web safely, addressing harmful business practices and acknowledging the offline factors shaping children’s lives including social inequality, poverty or disparate access to (mental) health care services.

Ineffective age-gating mandates and the dangerous shift toward VPN restrictions

As jurisdictions around the world gain experience with government-mandated age gates for certain services, evidence is mounting that age restrictions are not an effective policy tool. Avoiding age gates is widespread and trivially easy: In Australia, where minors under 16 year of age have been banned from certain social media platforms since December 2025, the government’s Compliance Update reports that seven out of ten young Australians remain online, often skirting age checks by simply entering a fake birthdate. A recent study on the implementation of the UK’s Online Safety Act found that a third of children have bypassed age gates with fairly trivial steps like faking their birthdate, borrowing someone else’s login credentials, or even drawing on facial hair, and that a quarter of parents have helped their children to bypass age assurance systems. In the US, studies indicate that as far back as 2011, 64% of parents who were aware their child under 13 had a social media account were also ones who helped them create that account.

Confronted with the apparent ineffectiveness of age gates, policymakers around the world seem to be shifting their attention to alleged circumvention tools. While research shows that many young people bypass age barriers by using other people’s devices and accounts or tricking age estimation tools by making themselves look older, virtual private networks (VPNs) are increasingly framed as primarily a “loophole” to age gates. VPNs create encrypted “tunnels” between a user’s device and the internet, protecting all internet traffic from that device and concealing users’ IP addresses. VPNs are an essential privacy and security resource for millions of users worldwide, including young people.

Utah’s recent age verification law holds websites hosting age-restricted content liable for verifying the age of anyone physically located in Utah, including individuals using VPNs or proxies. While the law does not ban VPNs outright, it forces websites to either block known VPN IP addresses or verify the age of every visitor globally. In the UK, policymakers debated age gates for VPNs extensively, but stopped short of restricting VPNs after new evidence confirmed that VPNs are not a relevant pathway for children seeking to bypass age checks. In Brazil, the ECA Digital law empowers the regulatory authority to order technical countermeasures against circumvention tools such as VPNs. These developments suggest a worrying trend: well-meaning but ineffective attempts to protect children risk undermining the fundamental rights to privacy, security, and free expression of all users, as well as the health and openness of the web itself.

We are convinced, however, that there are rights-respecting alternatives policymakers can pursue to empower young people online and improve their safety and well-being.

Moving beyond access bans

We strongly believe that online safety frameworks should be grounded in children’s rights, striking a balance between their right to protection and their right to participate in society, express themselves freely, and access media and information. Such frameworks must also be proportionate and should not undermine the fundamental rights and access to tools like VPNs for all users.

Rather than focusing on limiting access, we believe that policymakers should prioritize interventions that tackle the root causes of online harm. Before considering new instruments, this work starts with ensuring that independent regulatory authorities have the necessary resources to enforce existing online safety frameworks. In Europe, preliminary findings against Meta and TikTok find these companies’ addictive design features to be in breach of the Digital Services Act, underlining the potential of frameworks like the DSA to address key concerns.

The design of online interfaces, and the affordances and constraints they offer, significantly influences users’ interactions, decisions and overall wellbeing. ‘Dark patterns’ or deceptive interfaces are key drivers of harms experienced by users, and especially young people: they can compel people to consent to extensive data collection and processing, resulting in hyper-personalized feeds, personalized ads that may exploit cognitive vulnerabilities and promote unhealthy or excessive consumer choices, and an overall erosion of privacy.

This is why we support proposals like EU Digital Fairness Act (DFA) and the American Innovation and Choice Online Act (AICOA) that could fill regulatory gaps. Specifically, we advocate for the prohibition of harmful design, guided by harmonized definitions of core concepts like “dark patterns”, “deceptive design,” and “addictive design” and anti-circumvention clauses to prevent companies from avoiding regulation through small tweaks. Platforms should be responsible for demonstrating that their design choices are fair, non-manipulative and non-exploitative. And services that are likely to be accessed by children should be required to refrain from enabling certain design features, including excessive notifications, endless feeds and gambling-like features by default, and only with parental consent.

Further, we urge policymakers to adopt a privacy-first approach to online harms. Many of the risks encountered by young people online are related to the collection and processing of personal data. Platforms collect enormous amounts of personal data, including sensitive data, to personalize and target services, ranging from algorithmic recommender systems to online ads. While the systems that target and display ads and curate online content are distinct, both are based on the surveillance and profiling of users.

Such profiling is the basis for young people being targeted with personalized ads and content recommendations, which can segment, exclude, or steer people into inequitable options and towards harmful content. Providers should thus be prohibited from using sensitive personal data (e.g. ethnicity, religious belief, health status, sexual orientation, political affiliation) to personalize content recommendations or ads, and they should be mandated to enable privacy-protective settings by default, including restricting access to users’ location, camera, microphone, contacts, and camera roll. Policymakers should also extend the fairness and transparency obligations to personalization systems and advertising actors, including intermediaries and data brokers.

Additionally, everyone online, including families and young people, should be fully in control of their online experiences and navigate the web according to their preferences and needs. There is a significant opportunity to empower users with easy, effective opt-out rights and granular user controls. In practice, users should have the right to opt out of personalized content and targeting without being penalized with a downgraded version of the service. Some frameworks already strengthen choice – in those cases, we advocate for their robust enforcement.

Across jurisdictions, choice can be strengthened by ensuring that preferences explicitly expressed (e.g. settings selected, feedback signals, customization choices made, survey responses) are respected and “sticky”, so do not get reset without being explicitly requested by the user. Interoperability mandates should let people integrate third-party content moderation systems or recommendation algorithms that better match their preferences and help them break out of the walled gardens of a few dominant companies. Parental controls are another important lever to operationalize user controls: Providers should deploy easy-to-use and effective parental controls that allow families to tailor online experiences to their preferences, across platforms.

We appreciate that this is a long list of complex policy recommendations which are also impacted by broader (geo)political developments. The fact remains that current age assurance approaches are not a silver bullet, and will create more, rather than solve, problems in the long term.

Where policymakers consider age signals as necessary to ensure age-appropriate online experiences, we believe that there are technical approaches better suited to balance users’ rights than those currently pursued. We will explore these developments and approaches in the second part of this series.

The post Beyond technical fixes: Protecting kids online without breaking the internet  appeared first on Open Policy & Advocacy.

Personal avatar user experience

• Secure verification: Verify and manage your personal avatar directly within your Google Account.
• Seamless integration: Easily add your personal avatar from the selector in Vids.
• Administrative oversight: Admins can manage or disable this feature through the Admin console.

A note on language and region availability

At launch, personal avatars are available in English only for users 18 years and older. They’re not available in the European Economic Area, Switzerland, or the United Kingdom. Learn more about personal avatar privacy settings.

Getting started

• Admins: This feature will be ON by default and can be disabled or enabled at the domain level. Visit the Help Center to learn more about managing personal avatars in Vids.
• End users: Visit the Help Center to learn more about using personal avatars in Vids.

Rollout pace

• Rapid Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 16, 2026 
• Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on August 5, 2026 

Availability

• Business: Business Starter, Standard, and Plus
• Enterprise: Enterprise Starter, Standard, and Plus
• Education: Education Plus
• Consumer: Google AI Pro and Ultra
• Other Editions: Enterprise Essentials and Enterprise Essentials Plus; Nonprofits
• Education Add-ons: Google AI Pro for Education; Teaching and Learning
• Other Add-ons: AI Expanded Access*

*Users with AI Expanded Access add-on licenses have higher limits on usage of Omni in Vids.

Resources

• Google Docs Editors Help: Create, use & manage your personal avatar with Gemini in Google Vids
• Google Docs Editors Help: Use Omni in Google Vids

Admin settings

Previously, admins could only enable or disable automatic note-taking for all meetings. Today, we’re starting to roll out a third option, which will allow admins to enable automatic note-taking only for meetings with three or more people.

Customers on Business Standard and Business Plus plans will soon see this setting turned ON by default; the setting will be OFF by default for customers on Enterprise Standard, Enterprise Plus, and Frontline Plus plans, as well as those with the Google AI Pro for Education add-on. There will be no impact to the end user experience on any plan before September 21, 2026. If you want to change your settings, you can do so in the Admin console.

If you currently participate in the Gemini Alpha program, and previously tested this setting, it may already be ON. Please review the current state of the settings for your organization in the Admin console before September 21, 2026.

End user settings

We’re also introducing a way for end users to enable note-taking only for meetings with three or more participants. Once the “For all meetings I host with 3+ guests” option rolls out, users should revisit their settings to confirm they’re configured as desired. This option will become available no sooner than September 21; stay tuned to the Workspace Updates blog to be notified when that rollout starts.

Getting started

• Admins for Business Standard and Business Plus organizations: This new setting is ON by default. If you wish to change your settings, you should do so before September 21, 2026, to avoid any impact for end users. Visit the Help Center for more information on how to adjust these settings.
• Admins for Enterprise Standard, Enterprise Plus, Frontline Plus, and Google AI Pro for Education organizations: This new setting is OFF by default. You can adjust this setting in the Admin console once it appears, but it will not affect the end user experience before September 21, 2026. Visit the Help Center for more information on how to adjust these settings.
• Admins for organizations participating in Gemini Alpha program: Your settings may be impacted by previous configurations. Please review your settings before September 21, 2026, to ensure they’re configured correctly.
• End users on all plans: The default set by admins will go into effect for end users no sooner than September 21, 2026. Users can override this default in the Meet user settings after that date. Visit the Help Center to learn more about Meeting settings for “take notes for me”.

Rollout pace

Admin setting

• Rapid Release and Scheduled Release domains: Rolling out now, with expected completion by August 3, 2026

End user setting

• Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting no sooner than September 21, 2026*

*We’ll post an update on the Workspace Updates blog when the end user setting rollout begins.

Availability

• Business: Business Standard and Plus
• Enterprise: Enterprise Standard and Plus
• Other Editions: Frontline Plus
• Education Add-ons: Google AI Pro for Education

Resources

• Google Meet Help: Take notes for me in Google Meet
• Google Workspace Updates  Blog: New user controls for Take notes for me

Omni in Vids user experience

• Generate clips with higher quality: Generate higher quality videos with improved text rendering, physics, and realism using Google’s latest Omni Flash model.
• Edit videos by typing changes: For example, fix the color-grading, restyle the visuals in anime, or remove that New York siren in the background with a simple text instruction in Vids.

A note on language and region availability

At launch, editing non-AI videos with Omni is not available in the European Economic Area, Switzerland, United Kingdom, Texas, or Illinois.

Getting started

• Admins: This feature does not have an admin control.
• End users: Visit the Help Center to learn more about using Omni in Vids.

Rollout pace

• Rapid Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 16, 2026 
• Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on August 5, 2026 

Availability

• Business: Business Starter, Standard, and Plus
• Enterprise: Enterprise Starter, Standard, and Plus
• Education: Education Plus
• Consumer: Google AI Pro and Ultra
• Other Editions: Enterprise Essentials and Enterprise Essentials Plus; Nonprofits
• Education Add-ons: Google AI Pro for Education; Teaching and Learning
• Other Add-ons: AI Expanded Access*

*Users with AI Expanded Access add-on licenses have higher limits on usage of Omni in Vids.

Resources

• Google Vids Editors Help: Use AI to generate video clips

This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them.

The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production.

Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education.

At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators.

Finding 1: Confident and wrong

More than half have traced agent errors to bad context

We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had.

This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know.

The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem.

Finding 2: RAG is the default context source

Retrieval feeds more agents than any other method

We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin.

Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface.

One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business.

Finding 3: Provider-native retrieval already leads the vector databases

OpenAI file search and vertex AI search top the dedicated tools

We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists.

The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy.

The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from.

Finding 4: But they say they want to keep best-of-breed

A plurality resist consolidating onto a provider’s native stack

We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage.

Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool.

Finding 5: Hybrid retrieval is the consensus bet

Vector-only retrieval is already seen as insufficient

We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure.

The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed.

Finding 6: The governed context layer is being built now

Most run or are building a semantic layer — few in production

We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived.

The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production.

Finding 7: Bought on ingestion and simplicity, watched for correctness

Selection favors operability; monitoring favors correctness and security

We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical.

Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%).

Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted.

Finding 8: A retrieval reshuffle is coming

A majority plan to change providers — and the vector specialists are gaining interest

We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack.

The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle.

The bottom line: A context gap that more retrieval alone won’t close

Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation.

The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.

Steering suggestions in Vids scripts

Getting started

Rollout pace

Availability

Resources

We are now expanding support for these features to 11 more languages, including Mandarin, Dutch, Malay, Hebrew, Polish, Turkish, Czech, Indonesian, Swedish, Danish, and Norwegian. These new additions join our previously supported languages: English, Spanish, Portuguese, Japanese, French, Korean, German, and Italian.

Reimagined Gemini experience in Docs

With this update, Google Docs offers a centralized place to generate, write, and refine your documents with Gemini. Powered by Workspace Intelligence, Gemini leverages data across Drive, Gmail, Chat, and the web to provide personalized, context-aware assistance.

• The upgraded Help me create experience enables you to generate relevant, fully formatted first drafts that synthesize information from your files, emails, chat, and the web.
• With Help me write, simply prompt Gemini from the bottom bar or side panel to make edits across your doc, or select text to focus Gemini’s attention. Gemini’s suggested edits are only visible to you until you approve them.
• Match writing style helps maintain a consistent tone and style across your entire doc, no matter how many people are working on it.
• With Match doc format, Gemini can mirror a source document to generate content that adheres to the original's formatting (e.g., fonts and colors) and structural elements (e.g., headings and table columns).

To generate new docs from scratch, open a new doc, enter your prompt, and click submit. To edit existing docs, simply hover over the spark near the bottom of your doc and type a prompt in the bottom bar.

Getting started

• Admins: These features are available by default if Gemini for Workspace in Drive is enabled. Note that enabling Workspace Intelligence expands the range of supported use cases.
• End users: You must have Workspace smart features enabled to use these features. Visit the Help Center to learn more about creating personalized documents with Gemini in Google Docs.

Rollout pace

• Rapid Release domains: Gradual rollout (up to 15 days for feature visibility) starting on July 15, 2026 
• Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on August 1, 2026 

Availability

• Business: Business Standard and Plus
• Enterprise: Enterprise Standard and Plus
• Education: Education Plus
• Consumer: Google AI Pro and Ultra
• Education Add-ons: Teaching and Learning
• Other Add-ons: AI Expanded Access*; Google AI Pro for Education*

*Users with AI Expanded Access and Google AI Pro for Education add-on licenses will have higher limits on usage of Match writing style and Match document format tools.

Resources

• Google Docs Editors Help: Write & edit with Gemini in Docs
• Google Docs Editors Help: Learn how Gemini in Gmail, Calendar, Chat, Docs, Drive, Sheets, Slides, Meet & Vids protects your data

Whether you need to add a missing detail to the second line or include a deadline for your request, simply type the instruction and Gmail will instantly update the draft for you.

Getting started

• Admins: This feature is available by default if both Gemini for Workspace in Gmail is enabled and Workspace Intelligence access to Gmail is enabled.
• End users: This feature is available by default. Visit the Help Center article to learn more about drafting emails with Gemini in Gmail.

Rollout pace

• Rapid Release and Scheduled Release domains: Rolling out now, with expected completion by July 20, 2026

Availability

• Business: Business Starter, Standard, and Plus
• Enterprise: Enterprise Starter, Standard, and Plus
• Consumer: Google AI Plus, Pro, and Ultra
• Other Editions: Frontline Plus
• Education Add-ons: Google AI Pro for Education
• Other Add-ons: AI Expanded Access

Resources

• Gmail Help: Draft emails with Gemini in Gmail

The move is Canva's most aggressive push yet into the fast-growing "vibe coding" market, a category that barely existed 18 months ago but has already minted billion-dollar startups and reshaped how non-developers think about building software. But where rivals like Lovable, Replit, and Bolt.new have focused primarily on generating functional code from text prompts, Canva is making a different bet: that the real bottleneck isn't creating the code — it's making the output actually look good.

"Most vibe coding tools stop at functional — generating output that looks the same as everyone else's," Canva states in its announcement. "You might get a working prototype, but making it actually look like yours requires a complex editing surface, a separate design tool, a developer, or endless back-and-forth prompting that rarely lands where you want it.”

Danny Wu, Canva's Head of AI Products, framed the product's positioning in stark terms during an exclusive interview with VentureBeat ahead of the launch.

"We are deliberately targeting non-technical users," Wu said. "Canva Code isn't a tool we're building for developers. What we're trying to do is bring the power of AI coding — and really lightweight coding — into the Canva platform, while answering our users' requests for more interactivity, more customization, and more flexibility, from websites to interactive presentations."

Canva Code 2.0 brings drag-and-drop editing, HTML import, and 75% faster generation to AI-built websites

The update introduces several capabilities designed to collapse the distance between generating code and publishing a polished interactive experience. Users can now create Canva Code projects directly inside other design projects — embedding interactive elements within a whiteboard, presentation deck, or standalone page. Canva has also added more than 50 new templates specifically designed for interactive designs, along with the ability to import raw HTML files from other AI coding tools and convert them into editable Canva designs.

The performance improvements are significant. Canva says it has reduced average code generation time by 75 percent and cut the median time from initial prompt to a published site by 30 percent. The company also reports that integrating Canva Code into the broader Canva editor — allowing users to treat coded outputs like any other design element — has increased active Code users by 25 percent.

Perhaps the most distinctive feature is the editing experience itself. Unlike most AI coding platforms, which require users to re-prompt or modify raw code to make visual changes, Canva Code 2.0 lets users click directly into generated elements to change text, drag and drop images from Canva's built-in library of over 120 million templates and assets, update colors and fonts through a familiar toolbar, or select a specific element and refine it through conversational AI. Every output is fully interactive and automatically adapts to different screen sizes, with a built-in mobile preview.

Wu demonstrated the drag-and-drop editing during the interview, showing how a generated conference website could be modified in real time — swapping in photos, changing fonts to branded alternatives, and editing text directly on the canvas. "The key differentiator with Canva Code is the editability and the kindness of the outputs it generates," he said, though he noted one current limitation: "We don't support moving elements around. You still have to re-prompt for that."

How Canva plans to compete with Lovable, Replit, and Bolt in the booming AI app builder market

Canva's entry into vibe coding at this scale arrives at a pivotal moment for the category. According to market research published by Luminix AI in May 2026, the vibe coding and AI app builder market has reached an estimated $4.7 billion in 2026, with projections pointing toward $12.3 billion by 2027 at roughly 38 percent compound annual growth. The research also estimates that AI-generated code now comprises approximately 41 percent of all code written globally — a figure that would have seemed inconceivable even two years ago.

The competitive landscape has grown ferocious. Lovable, which focuses on conversational, design-forward app generation for non-technical founders, has achieved what may be the fastest revenue ramp in the category's history — reportedly reaching approximately $400 million in annual recurring revenue by early 2026, according to Luminix's analysis. Replit, which transformed its browser-based IDE into a full vibe-coding engine through successive AI agent releases, has tripled its valuation to $9 billion and is targeting $1 billion in run-rate revenue by the end of 2026, per the same report. Bolt.new, which runs a full Node.js environment entirely in the browser, scaled from $4 million to $40 million in ARR within months of launching.

And then there is Canva, which brings something none of those platforms possess: a quarter-billion-user design ecosystem where brands, teams, and individuals already store their visual identities, collaborate on projects, and publish content.

Wu positioned Canva Code not as a direct competitor to these developer-focused tools but as something that fills a gap none of them have addressed. "A lot of the requests that we have been getting and the usage we're seeing is actually with using Canva Code not necessarily as just one artifact, but as part of an overall design, the visual communication they're trying to tell," Wu said. "Like when you have a sales deck, you're able to add a calculator, you're able to add a visualizer of what exactly your product does. That's something where an interactive slide can be worth a thousand pictures."

Why Canva's HTML import feature could turn it into a 'finishing layer' for every AI coding tool

One of the most strategically interesting features in Canva Code 2.0 is its HTML import capability, which allows users to take code generated by any AI tool — including ChatGPT, Claude, Lovable, or Bolt — and bring it into Canva as a fully editable design. The implication is unmistakable: Canva is positioning itself as the place where AI-generated code gets its finishing touches, regardless of where it was originally created.

When asked directly whether this amounts to positioning Canva as a "finishing layer on top of vibe coding," Wu offered a diplomatic but revealing response. "It's really a continuation of our goal to make all design as easy as possible," he said. "We've supported importing PDFs and translating them into docs, importing PowerPoint files — so in one way, it's an expansion of that. But in another way, it's really just listening to what our users want and making Canva both the most useful and the most compatible platform.”

He paused, then added: "It's not that we're deliberately positioning ourselves as a specific layer, say like a finishing layer after vibe coding. We just really want to make our platform the most accessible and the most pluggable."

That language — "most pluggable" — suggests a platform strategy that doesn't require Canva to win the AI code generation race outright. If Canva becomes the default destination for making AI-generated code look professional and on-brand, it captures value from the entire category regardless of which code generation engine users prefer. The strategy also echoes the broader import capabilities that already allow Canva to ingest PowerPoint decks and PDFs from competing platforms, gradually pulling users deeper into the Canva ecosystem without demanding they abandon existing workflows.

What Canva Code can build — and where Danny Wu says it hits its limits

Wu was notably candid about the product's boundaries — a refreshing departure from the typical Silicon Valley product launch. "Canva Code is great for anything that works as a front-end app, and it's especially good when you want to leverage data, data submissions, and interactivity at small to medium scale," he said. "I'll be honest about the limitations. Canva Code is probably not going to be suitable if you're trying to build a website with complex backends, or if you're handling hundreds of thousands of visitors per day."

This candor effectively draws a line between Canva Code and the more ambitious platforms in the space. While Lovable and Replit are pushing toward full-stack application development — complete with databases, authentication, and production-grade hosting — Canva is deliberately limiting its scope to interactive front-end experiences at modest scale. The question is whether that's a strategic weakness or a disciplined focus. For the teachers, small business owners, and marketing teams that make up the bulk of Canva's user base, complex backends and high-traffic scalability are irrelevant concerns. What matters is whether they can create an interactive event page, a property listing website, or a classroom hub that looks professional and works on mobile — without hiring a developer or learning a new tool.

When asked about the AI models powering Canva Code, Wu confirmed the company uses a combination of proprietary and third-party models, including those from OpenAI and Anthropic, but declined to specify the exact mix. "We don't share the exact mix, and it does change over time," he said. "We also route differently depending on what you're asking for and which model family we think is best for handling certain requests."

Canva's AI acquisition spree — from Affinity to Leonardo.ai — now powers its vibe coding push

Canva's broader AI infrastructure has been significantly bolstered by an acquisition strategy that has accelerated over the past two years. In March 2024, the company acquired Affinity, the British creative software suite popular with Mac users, in a deal that Bloomberg reported was valued at "several hundred million pounds." Canva at the time positioned the deal as a way to compete with Adobe's flagship products — Illustrator, Photoshop, and InDesign — by gaining ownership of Affinity's Designer, Photo, and Publisher applications.

Just four months later, Canva acquired Leonardo.ai, an Australian generative AI startup with over 19 million registered users and more than a billion images generated. Canva co-founder Cameron Adams said at the time that Leonardo.ai's technology would be integrated into Canva's Magic Studio generative AI suite.

Together with these acquisitions, Canva Code is the company's attempt to layer interactive, code-driven capabilities on top of a visual design platform that has already been enhanced by professional-grade design tools and generative AI models. The company reports over 32 billion uses of its AI products to date — a staggering figure that underscores how deeply AI is now woven into everyday Canva workflows, even for users who may not think of themselves as using artificial intelligence.

Six million sites published, but Canva's retention data remains an open question

Canva's announcement highlights an impressive traction metric: users have created and published more than six million websites using Canva Code since the feature was first introduced a year ago. But the number deserves scrutiny.

Wu clarified in the interview that the six million figure represents published websites over the past year — meaning sites that were either made public or shared via password-protected or private links. "They may have published publicly, or behind a password, or as a private link. But that's the number of published websites," he said.

When asked about active retention — how many of those sites are still live and being maintained — Wu acknowledged the gap in his data. This is a meaningful distinction. In the vibe coding market, raw creation numbers can be misleading because the barrier to generating a site is so low. The more telling metric — which Canva does not yet provide — would be how many of those six million sites receive regular traffic or have been updated after initial publication.

The early use cases, however, suggest genuine utility beyond novelty. Educators and school administrators are using Canva Code to build classroom hubs, with one teacher creating bespoke webpages for each of their classrooms to keep students and parents updated on announcements. Small businesses, like Alt Marketing School, have built mini apps for fundraising training and interactive roadmaps for their members. For World Book Day, 50 readers created educational games across different subjects, complete with pedagogical guides for classroom use.

Canva Code pricing, data governance, and what enterprise customers need to know

Canva Code 2.0 is available across all of Canva's pricing tiers, including its free plan — a notable decision given that competitors like Lovable, Bolt, and Replit reserve their most capable features for paid subscribers. "As you go from, say, free to pro to business to enterprise, you would get more AI credits and be able to have higher usage of Canva Code," Wu said. "But it is available and it is usable — even free Canva accounts as well as education and not-for-profit accounts."

This credit-based approach mirrors the pricing evolution happening across the entire vibe coding category, where platforms have converged on token or credit systems that meter AI generation capacity rather than gating features behind subscription tiers. The difference is that Canva's free tier serves as an acquisition funnel for a much larger design platform, not just for the coding feature itself.

For the institutional customers Canva increasingly courts — school districts, real estate brokerages, enterprise marketing teams — data governance is a threshold concern. Wu addressed this directly. "All users and customers have full control over how their data is used," he said. "They can choose whether their prompts and data are used for AI training in the settings. For businesses and enterprises, team admins can manage this at the organizational level and guarantee that their inputs, content, and outputs won't be used for training." This opt-out approach reflects a lesson the broader industry has learned the hard way. As The Verge reported when Canva acquired Leonardo.ai, Adobe suffered significant backlash over a policy update regarding user data and AI model training — a controversy Canva appears keen to avoid.

Canva's long-term vision: closing the gap between imagination and what non-technical users can actually build

When asked where Canva Code fits into the company's long-term trajectory — and whether Canva is building toward a full-stack app development platform — Wu steered the conversation back to the company's core audience.

"A huge part of it is reducing the gap between your imagination and what's possible, especially for everyday users — people who don't have a lot of time," he said. "They don't have time to figure out deploys or MCPs or APIs. They just want to design more interactive and more dynamic communication."

He pointed to the rapid improvement in AI model capabilities as a key accelerant. "The kind of things you can create today in one shot — like a 3D visualization of a solar system — you really couldn't have trusted the output a year ago. But today, you have a really high success rate."

Whether Canva Code becomes a durable product category or a feature that gets absorbed into the platform's broader AI workflow will depend on how quickly the company can close the gap between its current front-end focus and the full-stack capabilities that increasingly define the competition. Lovable is shipping Supabase-backed apps with authentication and databases built in. Replit's agents can execute autonomous long-running builds. Bolt.new runs entire Node.js environments in a browser tab. These are fundamentally different ambitions than making a conference landing page look good.

But Canva has never won by matching the technical depth of its competitors. A decade ago, it didn't try to out-feature Adobe — it made design accessible to the 99 percent of people who would never open Photoshop. Now, in a vibe coding market where every tool can generate a working prototype from a prompt, Canva is making the same wager it made in 2012: that for most people, the hardest part was never the building. It was making it look like it came from you.

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Am 12. Oktober 2027 beendet Microsoft für Privatanwender den erweiterten Support (den man als Extended Security Updates, ESU, bezeichnet) für Windows 10 (Version 22H2 Home, Professional, Pro Education oder Workstations Edition). Nur Unternehmenskunden bekommen gegen Bezahlung noch länger Sicherheits-Updates, und zwar bis 2028.

Das bedeutet: Nach dem 13. Oktober 2027 (nach der ursprünglichen Planung sollte bereits am 12.10.2026 Schluss sein, doch Microsoft verlängerte den Supportzeitraum für Windows 10 noch einmal) erhalten Sie als Privatanwender für Ihren Windows-10-Rechner keine Sicherheits-Updates mehr. Neu entdeckte Sicherheitslücken in Windows 10 schließt Microsoft dann grundsätzlich nicht mehr, stattdessen bleiben diese offen und können von Angreifern ausgenutzt werden.

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Bei besonders schwerwiegenden Sicherheitslücken sind aber weiterhin Ausnahmen möglich. Denn Microsoft hatte auch schon bei älteren, eingestellten Versionen wie Windows XP und Windows 7 in seltenen Fällen noch Patches nach Supportende veröffentlicht. Doch darauf dürfen Sie sich nicht verlassen.

Sie stehen also spätestens am 13. Oktober nächsten Jahres vor der Entscheidung, ob Sie Ihren Windows-10-Rechner noch weiternutzen wollen. Diese Möglichkeiten haben Sie:

Upgrade auf Windows 11: Sicher, gratis, empfehlenswert

Für diese Zielgruppe: Ihr alter PC/Laptop läuft stabil und ist für Ihre Bedürfnisse ausreichend schnell. Sie wollen möglichst kein Geld ausgeben und trotzdem relativ sicher vor Hackern und Viren sein. Zudem möchten Sie ein aktuelles Betriebssystem nutzen.

Der einfachste Weg besteht darin, dass Sie Ihr Windows 10 auf Windows 11 upgraden. Das ist für Sie kostenlos. Einzige Hürde: Ihre Hardware muss für Windows 11 geeignet sein. Das bedeutet: TPM 2.0, Secure Boot und kompatible Prozessoren ab der 8. Intel-Generation oder vergleichbare AMD-Modelle sind in Ihrem Rechner vorhanden.

Microsoft stellt die kostenlose PC-Integritätsprüfungs-App zur Verfügung, mit der Sie unter Windows 10 testen können, ob die Aktualisierung möglich ist. Klicken Sie dazu nach dem Start des Tools auf „Jetzt überprüfen“.

Tipp: Mit einigen Tricks können Sie Windows 11 auch auf Rechnern installieren, die für Windows 11 wegen veralteter Hardware nicht geeignet sind.

Lösung: Wir erklären beide Upgrade-Wege – also für kompatible und für nichtkompatible Windows-10-Rechner – in dem Ratgeber “Windows-10-Update auf Windows 11 24H2: Wie gehts? Was kostet es?“.

Zur Installation auf Hardware, die eigentlich nicht für Windows 10 geeignet ist, können Sie zudem “Windows 11 auf jeder Hardware installieren – so geht´s” lesen. Einen umfassenden Überblick zur Upgrade-Thematik bietet zudem unser Ratgeber “Windows-10-Nutzer aufgepasst: Das müssen Sie jetzt tun“.

Übrigens: Sollten Sie Windows 11 Home im Einsatz haben, dann entgehen Ihnen die vielen Vorteile der Pro-Version, die wir Ihnen hier vorstellen. Im PC-WELT Software-Shop ist das Windows-11-Upgrade für 59,99 Euro statt 145 Euro erhältlich.

Neuen Windows-11-Rechner kaufen: Sicher, teuer, empfehlenswert

Für diese Zielgruppe: Ihr alter PC/Laptop hat bereits Macken, stürzt ab oder ist zu langsam. Sie benötigen ohnehin neue Hardware.

In diesem Fall ist der Kauf eines neuen Rechners oder Laptops ganz klar die beste Wahl für Sie. Damit machen Sie nichts falsch, allerdings müssen Sie dafür Geld in die Hand nehmen.

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Wechsel zu Linux: Sicher, kostenlos, aufwendig

Für diese Zielgruppe: Ihr alter PC/Laptop läuft stabil und ist für Ihre Bedürfnisse ausreichend schnell. Sie wollen möglichst kein Geld ausgeben und trotzdem relativ sicher vor Hackern und Viren sein. Und Sie benötigen Windows nicht zwingend für bestimmte Anwendungen oder Spiele.

Sie müssen sich in das neue Betriebssystem allerdings einarbeiten und neue Programme kennenlernen. Das kostet Zeit und vermutlich auch etwas Nerven. Der Lohn der Mühe: Sie sind endlich frei von Microsoft. So, wie es unser Kollege in “Nie mehr Windows: Dieses Tool macht Sie jetzt Microsoft-frei” beschreibt.

Lösung: In “Linux wie Windows: Welche Distribution ist am ähnlichsten?” stellen wir Ihnen zudem geeignete Linux-Distributionen vor. Außerdem empfehlen wir Ihnen den Artikel “Linux für Windows-Umsteiger: 10 Fragen & Antworten“.

Wechsel zu einem Mac: Sicher, teuer, aufwendig

Für diese Zielgruppe: Ihr alter PC/Laptop hat bereits Macken, stürzt ab oder ist zu langsam. Sie benötigen ohnehin neue Hardware und sind bereit, viel Geld auszugeben und sich in ein neues Betriebssystem einzuarbeiten.

Lösung: iMacs und Macbooks sind leistungsfähig und sicher, bekommen lange Updates und sind langlebig. Sie sind aber auch teuer, wobei das Macbook Neo jetzt einen vergleichsweise preiswerten Einstieg ermöglicht, siehe “Das Macbook Neo für 700 Euro ist Microsofts schlimmster Albtraum“.

Wechsel zu einem Chromebook oder Googlebook: Sicher, günstig, bedingt empfehlenswert

Für diese Zielgruppe: Ihre alte Hardware läuft nicht mehr rund und Sie benötigen einen Laptop nur für wenig rechenintensive Aufgaben wie Surfen, Social Media, Streaming oder Office-Arbeiten. Sie wollen wenig Geld ausgeben und vergleichsweise sicher unterwegs sein. Sie arbeiten ohnehin immer schon durchgehend online.

Lösung: In diesem Fall müssen Sie Ihren alten Windows-10-Laptop nicht zwingend durch ein teures Windows-11-Notebook oder ein Macbook ersetzen. Sondern können stattdessen auch ein vergleichsweise preiswertes Chromebook kaufen. Oder künftig ein Googlebook.

Chromebooks eignen sich als günstige Notebooks gut für alltägliche Aufgaben und Büroarbeiten. Dabei müssen Sie ganz auf das Ökosystem von Google vertrauen, im Gegenzug bekommen Sie viel Sicherheit vor Schadsoftware. Hier finden Sie passende Geräte: Die besten Chromebooks im Test.

Die Googlebooks sind die neueste Laptop-Familie von Google. Standardmäßig mit Gemini Intelligence und dem Magic Pointer an Bord. In “Mit den Googlebooks will Google den Laptop-Markt aufmischen: Das steckt dahinter” stellen wir Ihnen diese Geräte vor. Als Betriebssystem dient hier genauso wie bei den Chromebooks Chrome OS. Verkaufsstart soll im Herbst 2026 sein. Preise nennt Google noch keine, ebenso fehlen alle Informationen zur Hardware.

Die letzte Chance

Was aber tun, wenn man kein Geld für einen neuen Rechner hat, der alte PC aber das Upgrade auf Windows 11 wegen seiner schwachen Hardware nicht zulässt? Nun, dann bleibt theoretisch die Möglichkeit, Windows 10 weiter zu verwenden.

Windows 10 nach Oktober 2027 weiternutzen: Nicht empfehlenswert

Für diese Zielgruppe: Ihr alter PC/Laptop läuft stabil und ist für Ihre Bedürfnisse ausreichend schnell. Sie wollen oder können kein Geld ausgeben und/oder benötigen weiter Windows 10 für bestimmte Anwendungen oder Spiele. Oder Sie wollen sich nicht mehr an ein neues Betriebssystem gewöhnen.

Lösung: Beachten Sie die folgenden Hinweise

Virenscanner und Anwendungen aktuell halten

Einen Windows-10-Rechner nach dem 12.10.2027 mit dem Internet zu verbinden, ist sehr gefährlich. Falls Sie das doch tun wollen und sich der Gefahr bewusst sind, dann halten Sie unbedingt den Virenscanner und die Firewall auf dem PC immer aktuell. Aktualisieren Sie zudem immer alle Anwendungen auf dem Rechner, also beispielsweise die Browser.

Defender bleibt aktuell

Immerhin: Die vorhandenen Sicherheitsfunktionen des Betriebssystems bleiben aktiv, das gilt auch für den Malwareschutz. Sie veralten aber mit zunehmender Dauer. Der in Windows integrierte Microsoft Defender Antivirus wird aber weiterhin aktualisiert. Microsoft stellt hierfür die sogenannten „Security Intelligence Updates“ (die Datenbanken zur Erkennung neuer Viren und Malware) für alle Windows 10-Nutzer mindestens bis Oktober 2028 bereit. Dies garantiert einen grundlegenden und aktuellen Schutz vor Schadsoftware, auch wenn das Betriebssystem selbst nicht mehr gegen Schwachstellen im Code gepatcht wird.

Meiden Sie unbekannte Webseiten und Downloads

Die Firewall Ihres Routers schützt Ihren Windows-10-Rechner auch weiterhin. Gefährlich wird es aber, wenn Sie Webseiten im Browser aufrufen. Vermeiden Sie deshalb unbedingt den Besuch unbekannter Webseiten. Klicken Sie keine unbekannten Links an und seien Sie besonders vorsichtig bei Downloads – das gilt auch für Links und Dateianhänge in Mails.

Kein Online-Banking

Vermeiden Sie Einkäufe, Bezahlvorgänge und Online-Banking auf diesem Rechner.

2FA besonders wichtig

Schützen Sie alle Ihre Benutzerkonten durch die Zweifaktorauthentifizierung oder durch Passkeys. Melden Sie sich bei all Ihren wichtigen Websites mit einem zusätzlichen Code (oder Schlüssel/Passwort) an, den Sie auf Ihrem Smartphone und nicht auf Ihrem jetzt anfälligen Windows-Computer speichern. Auf diese Weise können Malware oder Hacker Ihre Konten nicht über Ihren Computer übernehmen.

Vom Internet trennen

Falls Sie Ihren Windows-10-Rechner nach Oktober 2027 weiter nutzen wollen, um darauf beispielsweise ein fest installiertes Spiel zu spielen, für das Sie keine Internetverbindung benötigen, dann trennen Sie den Rechner am besten dauerhaft vom Internet. Und stecken Sie nur solche externen Datenträger wie USB-Sticks oder Festplatten an, die Sie mit einem aktuellen Virenscanner überprüft haben.

Weitere Ratschläge für die Weiternutzung von Windows 10 lesen Sie in “Ab heute bekommt Windows 10 keine Sicherheits-Updates mehr – das müssen Sie jetzt tun“. In diesem Zusammenhang sollten Sie auch die Windows-10-ISO-Datei herunterladen.

Nutzen Sie Windows 10 in einer virtuellen Maschine: Sicher und gratis

Falls Sie Windows 10 nur gelegentlich und nur für bestimmte Zwecke benötigen, können Sie das Betriebssystem auch in einer virtuellen Maschine installieren. Auf Ihrem Rechner läuft dann beispielsweise das aktuelle Windows 11 und Windows 10 starten Sie, wenn Sie es benötigen, als Gastsystem in der virtuellen Maschine.

Updates für Windows 10 bis 2023: Sonderweg

Eine Alternative, die Sie bereits jetzt nutzen können, ist die Sicherheitslösung 0Patch. Dabei handelt es sich um ein Unternehmen, das Sicherheitsupdates für Windows 10 bis zum Jahr 2030 bereitstellt. Allerdings aktualisiert die cloudbasierte Software des Unternehmens nicht die Systemdateien von Windows 10, sondern aktiviert die Patches im Arbeitsspeicher des Rechners. Dadurch müssen diese bei jedem Start neu geladen werden.

Der Einstieg in die Software ist sogar kostenlos möglich. Wer umfassender geschützt sein will, kann die kostenpflichtige Version für 25 Euro pro Jahr zuzüglich Steuer buchen.

Windows-10-Variante mit Updates bis 2032: Nicht legal

Im Internet finden sich immer wieder Tipps, auf das Betriebssystem Windows 10 IoT Enterprise LTSC 2021 zu setzen. Dieses entspricht im Grunde genommen Windows 10 Enterprise mit allen Funktionen und erhält Updates bis 2032. Lizenzrechtlich ist der Einsatz als Büro-PC aber nicht erlaubt.

Technisch gesehen können Sie das Betriebssystem nach dem Kauf aber bis 2032 sicher einsetzen. Wie das geht, erklären wir in “Kostenlos von Windows 11 zu Windows 10 wechseln und Updates bis 2032 nutzen – so geht’s mit UpDownTool“.

Wichtig: Wie auch immer Ihre Entscheidung ausfällt, sollten Sie ein Backup Ihrer Daten auf dem alten Rechner machen. Mit O&O DiskImage ist das kein Problem.

Microsoft stellt 2026 den Support für zahlreiche Produkte ein, die noch auf Millionen von Rechnern laufen. Das bedeutet: Alle diese Programme werden unsicher, weil Microsoft darin keine Sicherheitslücken mehr schließt. Ein Programm stirbt sogar komplett. Und nein: Es handelt sich dabei nicht um Windows 10.

Windows 11 24H2 Home und Pro

So stellt Microsoft in diesem Jahr den Support für Windows 11 24H2 Pro und Home ein, wie Sie hier bei Microsoft nachlesen können. Am 13. Oktober 2026 wird das der Fall sein. Dann erscheint nämlich das nächste große Windows-11-Update 26H2 – das übrigens nur einen winzigen Download erfordert – und dementsprechend beendet Microsoft die Unterstützung für die vorletzte Windows-11-Version. Das war auch 2025 schon der Fall, damals endete im Herbst der Support für Windows 11 23H2.

Am 13. Oktober 2026, dem Microsoft-Patchday, erhalten Sie also das letzte Mal Updates für 24H2. Spätestens an diesem Tag sollten Sie auf eine neuere Windowsversion wie 25H2 updaten.

Übrigens: Sollten Sie Windows 11 Home im Einsatz haben, dann entgehen Ihnen die vielen Vorteile der Pro-Version, die wir Ihnen hier vorstellen. Im PC-WELT Software-Shop ist das Windows-11-Upgrade für günstige 59,99 Euro statt 145 Euro erhältlich.

Windows 11 24H2, das diese neuen Funktionen auf den Rechner brachte, hatte nach seinem Erscheinen bei vielen Nutzern für Kopfzerbrechen gesorgt. Bei einigen Nutzern scheiterte bereits die Installation, bei anderen Nutzern hagelte es Fehlermeldungen und die Rechner machten urplötzlich Neustarts. Die Taskleiste konnte auf den Windows-PCs einfrieren. Bei einigen Spielen sank die Framerate.

Office-Programme ließen sich teilweise nicht mehr unter Windows 11 24H2 verwenden. Microsoft stoppte zwischenzeitlich sogar die Auslieferung von 24H2.

Ebenfalls vom Supportende betroffen sind Windows Server 2012/Windows Server 2012 R2. Für diese gibt es ab 13.10.2026 keine Extended Security Updates mehr.

Windows 11 SE stirbt ebenfalls

Das Supportende für Windows 11 24H2 hat eine spezielle Nebenwirkung: Damit stirbt endgültig auch Windows 11 SE.

Dieses Windows 11 SE hatte Microsoft im November 2021 als Alternative zu Googles Chrome OS angepriesen, doch Windows 11 SE konnte sich am Markt nicht durchsetzen. SE war vor allem für günstige Notebooks im Schuleinsatz gedacht. Wir schrieben im November 2021: