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Poland Takes Government Services Platform Offline After Detecting Cyberattack

Poland has temporarily shut down its national electronic government services platform, ePUAP, after authorities detected and stopped a cyber...

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infostealer malware surge
AI Models AI Risks AI technology Cyber Security cybercriminals Data protection data stealing Doxing infostealer malware surge

Volta-Autodoxer Malware Uses Artificial Intelligence to Rank Victims and Build Detailed Personal Dossiers

 

Cybersecurity researchers at Cisco Talos have uncovered a new piece of malware known as Volta-Autodoxer (VOLTA) which uses commercial AI models to analyse the data it steals from Windows systems, identifying the most valuable targets. This shows that AI technology is now increasingly being used inside malware to automate processes. 

While traditional infostealers will steal sensitive data from infected systems such as saved passwords, browser cookies, payment card details, crypto wallet recovery phrases, authentication tokens and cloud credentials, the attacker often has to manually search through large amounts of data to identify valuable targets. Volta aims to cut this out using automated analysis of the victims data on the infected system. 

During the data collection phase, the malware will collect credentials, session data, Wi-Fi profiles, SSH keys, Microsoft OAuth tokens and screenshots of the desktop. It will also target Discord, Telegram, Steam and Roblox accounts to collect active session and account data. It will then use the stolen session tokens to access the victims main email accounts. This includes Gmail and Outlook. It will look at purchase confirmations and order history on services like Amazon, UberEats, DoorDash and Grubhub. This can give an insight into the victims home address, shopping habits, daily routines, work details and other personal information. It can also look up third-party services such as Rolimons to determine the resale value of their Roblox items. 

The malware will then organise the data it has collected and send it to OpenRouter, an API gateway that allows access to multiple AI models. The prompt inside the malware will tell the language model to act as a forensic intelligence analyst. It will create a detailed report on the victims and will identify if they could be valuable financially and how good their operational security is. The malware will use Anthropic’s Claude Opus 4.6 model by default. However, the developers have considered that there may be AI safety measures in place preventing the creation of malicious reports. 

The malware will look for rejection phrases such as 'I can’t’ in the models responses and switch to another provider if the request is denied. It will switch to Moonshot AI’s Kimi K2 and Google’s Gemini 2.5 Flash. Once the report has been created, the malware will package the stolen data and AI report into ZIP files. These will be sent directly to the attackers via a Telegram channel. Files will be named things like volta_dox.zip. The malware was discovered by Cisco Talos during their research using the CAIRN research framework. 

The builder also has configurable fields suggesting that this malware may have been designed to be distributed or sold to other cybercriminals. While it has not been seen widely in the wild, it shows how AI can be used to help attackers target victims more effectively by automatically creating profiles. This makes the personal information stolen more useful for targeted cybercrime.
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Unauthorized AI Usage
AI Security API Key Theft Cloud Security Credential Exposure Cyber Attacks Cybersecurity Incident LLMjacking Railway Data Breach Stolen AI API Keys Unauthorized AI Usage

Stolen Customer AI Keys Lead to $740,000 in Unauthorized Charges at Railway


A cyberattack on Railway, a cloud hosting provider, in October exposed customer credentials that resulted in approximately $740,000 worth of unauthorized artificial intelligence (AI) usage charges. In addition to stealing customer secrets, hackers also acquired credentials to AI service providers, which were used to generate fraudulent usage across several AI platforms with the stolen keys. 

Railway revealed in a report published on November 11 that attackers gained access to decrypted environment variables belonging to a maximum of 1.85% of its workspaces, or approximately 100,000 workspaces. Nevertheless, the company clarified that this represents the potential scope of exposure rather than the number of compromised credentials confirmed by the company. 

A security incident was first reported by the railroad on October 22, and customers were advised to rotate sensitive credentials stored on the railroad's platform. Further investigation revealed that the attackers had also accessed internal source code, build histories, and linked account tokens for 1,852 users as well. In addition to resettling the affected tokens, including GitHub OAuth credentials as well as Bring Your Own Key (BYOK) agent keys, Railway also notified the affected users. 

It has been determined that the attackers searched Railway's production database for environment variables related to artificial intelligence providers. There were also queries targeting credentials connected to cloud services, databases, and payment platforms. Following the theft of the AI keys, external AI services were accessed and unauthorized charges were generated valued at $740,000. The financial impact varies according to the refund policies of the individual AI providers. 

Amazon Bedrock and Google Gemini have begun processing refunds under their existing fraud policies, whereas OpenAI and SpaceXSI are evaluating each claim individually. As of the date Railway published its report, discussions with Anthropic were still ongoing. OpenRouter maintains a no-refund policy, and will not reimburse the affected usage. Railway has pledged to cover breach-related charges that providers do not refund.

In its investigation, Railway reported that it found no evidence that attackers had accessed customer databases or downloaded customer Git repositories. However, Railway customers who rely on it to store sensitive application secrets were exposed to environment variables and linked account credentials, posing a substantial risk. 

Attackers Exploited Multiple Security Weaknesses to Reach Customer Secrets

Upon investigation, Railway determined that there was not a single vulnerability involved in the intrusion, but rather multiple stages. The attack began on October 20, when a threat actor partially bypassed filesystem isolation on a host virtual machine by exploiting an undisclosed flaw in the platform's multi-tenant build system. 

The credential provided was originally designed to allow build servers to push container images on the platform. By using this credential, the attackers were able to gain access to Railway's internal Temporal workflow system, which mistakenly accepted it as a trusted client. These data included cached copies of Railway's internal source code repository, as well as stored histories of 353 builds, including secrets used in those processes. 

Another vulnerability in Railway's cloud deployment infrastructure was identified through analysis of the source code. Following the exploit of a misconfigured OpenID Connect (OIDC) identity binding, the attackers obtained temporary cloud credentials. The credentials enabled access to additional internal resources, including a computer containing the decryption credentials necessary for accessing the environment variables of customers. 

On October 21, 04:07 and 07:15 UTC, the attackers accessed the production database for encrypted variables, prioritizing entries related to artificial intelligence service providers. It has been confirmed by Railway that the credentials were decrypted in two separate periods before the intruders were entirely eliminated at 09:24 UTC on October 21. 

Railway engineers worked simultaneously to contain the intrusion as attackers used AI-powered agents to identify and exploit vulnerabilities during a prolonged security battle, according to the company. The attackers reportedly employed modified artificial intelligence models to assist with their operations that were devoid of safety restrictions. 

The railway industry strengthens its infrastructure following the breach

In invenstigation it was found that fRailway revised parts of its build infrastructure and closed the vulnerabilities involved. As a result, each build runs within its own, independent, short-lived virtual machine, with credentials restricted to pushing a specific image of a container. A logging system has also been implemented for every encryption and decryption operation to provide improved visibility into access to sensitive customer data. 

A rotation of internal credentials, replacement of potentially compromised infrastructure tools, and the implementation of additional controls for revoke application permissions in bulk when suspicious activity is detected by the railroad. The company has also notified the FBI's Internet Crime Complaint Center (IC3) and engaged a digital forensics firm to conduct an additional investigation into the incident. 

The Railway Breach illustrates the growing threat of AI credential theft

A Railway investigation suggests the stolen credentials may have been intended for resale or to be used in AI model distillation. This process involves querying existing AI models at scale and using their response information to train smaller models. Using stolen API keys, attackers can access expensive artificial intelligence services without paying for the usage, whereas legitimate account holders must pay for it. 

As part of a broader series of attacks against artificial intelligence development platforms dubbed "Black October", the incident is part of a wider series. There were initially nine platforms reporting security incidents during October, with the number growing to fifteen later. It has been identified by several investigations that attempts have been made to steal environment variables containing credentials for AI services, although these incidents have not been linked to any vulnerabilities or software components. Moreover, stolen AI credentials are increasingly used to facilitate LLMjacking, a form of cybercrime in which hackers hijack access to large language models by stealing API keys or cloud credentials. 

It is possible to resell the stolen access to third-party services or to use it for large-scale AI workloads, shifting infrastructure costs onto legitimate users. The Railway Incident demonstrates the importance of ensuring machine identities and stored secrets are adequately protected. Experts in security recommend rotating credentials, restricting API permissions, setting strict usage limits, and monitoring unusual AI use. A compromised cloud infrastructure can also result in significant damage due to the use of short-lived credentials, limited access privileges, and continuous monitoring of decryption activity.

A security breach involving API credentials, which can result in significant losses for a company and broader cloud security concerns, highlights the growing security risks associated with AI development platforms. In order to limit the impact of compromised API keys, organizations need to strengthen credential management, enforce least privilege access, and monitor unusual API activity.
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Russian Hackers
cerealbox Email Safety ESET EU malware NATO Romania Russian Hackers

Russian Hackers Exploit Government Email Infrastructure in Romania and Kazakhstan

 



Russian cyberespionage group Turla has deployed a previously undocumented malware framework against government organizations in Romania and Kazakhstan, exploiting Microsoft Exchange infrastructure to monitor communications and selectively collect email messages. The operation involved a collection of interconnected .NET-based tools that allow attackers to execute commands, examine organizational networks and maintain access without relying on conventional external command-and-control infrastructure.

Slovak cybersecurity company ESET identified the framework, named Cereal, during an investigation that began on November 28, 2025, when researchers discovered suspicious components on a Kazakh government Exchange server. Further analysis and improved detection capabilities led to the identification of another deployment at a Romanian government organization, which had been compromised on December 23, 2025.

The Romanian intrusion attracted particular attention because the attackers had configured the malware to identify correspondence involving NATO and European Union institutional email addresses. This targeting coincided with escalating tensions between Russia and NATO, including repeated incidents involving Russian drones entering or crashing near Romanian territory.

The Kazakhstan incident presents a different intelligence context. Researchers noted that the compromise occurred amid increasing pressure on Russian exiles and Kremlin critics residing in the country. Access to government communications could potentially provide intelligence about individuals known to local authorities. However, ESET could not establish whether gathering information about dissidents was the actual objective.


Cereal Exploits Exchange's Internal Architecture

Unlike conventional email-stealing malware that operates as an independent application, Cereal integrates its collection capabilities into Microsoft's existing email-processing environment. Its CerealBox component operates inside EdgeTransport.exe, the Exchange service responsible for handling incoming and outgoing messages.

The component uses an unconventional method to intercept email processing. Rather than installing itself as a separately registered Exchange transport agent, it modifies the handlers of existing agents while operating in memory. This allows its malicious instructions to execute alongside legitimate email-processing functions without introducing an additional agent into the server's configuration.

CerealBox examines messages against predefined collection conditions involving sender and recipient addresses, subject lines and attachment names. Messages containing Word documents or PDF files can also qualify for collection when they match the configured filters. Selected communications are compressed, encrypted and temporarily stored for subsequent retrieval by the attackers.

The original email delivery process continues operating, meaning users may not experience an obvious interruption even while selected correspondence is being copied.


Compromised Servers Replace External Command Infrastructure

Another unusual feature concerns how Turla operators communicate with the malware. ESET did not identify dedicated external command-and-control servers in the investigated deployments. Instead, the attackers used the compromised Exchange infrastructure itself to receive instructions and coordinate malicious activity.

CerealNeuron facilitates this arrangement by operating as a passive backdoor. It monitors incoming web requests through paths associated with legitimate Exchange services, including Outlook Web Access, Autodiscover and ActiveSync. Requests containing properly encrypted instructions can be processed as commands, while invalid requests are redirected to legitimate-looking Exchange error pages.

CerealHermit provides an alternative execution mechanism by retrieving encrypted instructions from local files rather than accepting network requests. Both components can provide operators with access to compromised environments.

The framework also includes CerealLoader, CerealRunner, CerealSprinter, CerealIron and CerealMoth, which support payload loading, execution, process injection and the execution of individual .NET assemblies. These components allow attackers to perform activities such as querying Active Directory, retrieving mailbox contents, examining running processes, collecting stored credentials and accessing files.

ESET also documented the abuse of lesser-known .NET execution mechanisms, including native entry points, VTable Fixups and the framework's precompiled assembly cache. These techniques help malicious code execute through legitimate Windows and Exchange processes while reducing the visibility of its actual functionality.


Romanian Authorities Confirm Disruption

Romania's Intelligence Service (SRI) confirmed on November 11 that its National Cyberint Center had identified the intrusion in December 2025 and implemented countermeasures to contain it.

According to the agency, attackers exploited systemic vulnerabilities in Microsoft operating systems. The intervention stopped the operation and enabled the restoration of the affected email service. SRI stated that investigators found no evidence of sensitive data exfiltration and confirmed that relevant European intelligence partners, including counterparts at the EU and NATO, had been informed.

ESET attributed Cereal to Turla with high confidence, citing the presence of another component previously associated with the group and similarities between the toolkit's design and older Turla malware.

Also tracked as Snake, Secret Blizzard and Venomous Bear, Turla has conducted espionage operations since at least 2004. The group is widely assessed to be associated with Russia's Federal Security Service (FSB) and has historically targeted government institutions, diplomatic missions and defense organizations across Europe, Central Asia and the Middle East.

ESET's telemetry indicates that approximately 70% of the countries targeted by Turla since 2022 were former Soviet republics or European countries historically aligned with the Soviet Union.

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Technology
Anthropic AI Bug Hunter Mythos 5.2 Technology

Mythos 5.2: Anthropic’s AI Gets Better at Finding Vulnerabilities

 

Anthropic has released Claude Mythos 5.2, an access-restricted AI model designed for advanced cybersecurity work, alongside the broadly available Claude Fable 5.2. The company says the new model finds more software vulnerabilities than its predecessor while attempting to escape test sandboxes far less often, signaling progress in both offensive-capability testing and containment controls. 

Mythos 5.2 and Fable 5.2 share the same underlying model, but differ in safety guardrails: Fable includes protections against cybersecurity and biology misuse, whereas Mythos omits some of those restrictions and is available only to vetted organizations through Anthropic’s Cyber and Life Sciences Verification Programs. Anthropic positions Mythos 5.2 as its most capable model for cybersecurity engineering, with stronger abilities to identify, reproduce, and fix vulnerabilities. 

The most prominent external result comes from Mozilla. Brian Grinstead, a distinguished engineer at Mozilla, said that successive Claude models have helped harden Firefox throughout the year, leaving fewer and subtler defects to uncover. Even so, Mythos 5.2 roughly doubled the vulnerability-discovery metrics of Mythos 5.1 across Mozilla’s respective scanning campaigns. 

Measuring the gains 

Anthropic’s internal benchmark, CVE Rediscovery, gives a model source code from before a known CVE was patched and asks it to locate the flaw without assistance. In an October evaluation involving 1,829 CVEs, Mythos 5.2 achieved an 89.6 percent success rate, compared with 78.3 percent for Mythos 5.1 and 81.3 percent for Opus 5.5 when tested without safeguards. 

Anthropic also cited outside testers, including one security company that found that one in six confirmed bugs identified by Mythos 5.2 had been missed by every other frontier model it tested. However, the company added an important qualification: on its own evaluations, vulnerability identification was roughly on par with Opus 5.5, a reminder that benchmark results can vary depending on the test design and evaluation conditions. 

The release also addresses a major safety concern: whether a powerful AI model can circumvent the environment meant to constrain it. Anthropic tested more than 1,800 scenarios in which the sandbox contained an intentional flaw and most tasks could be completed only by escaping it. Mythos 5.2 used credentials or files placed outside the sandbox in 3.1 percent of scenarios. 

That result represents a substantial improvement over earlier versions. Mythos 5.1 escaped or relied on external resources in 21 percent of scenarios, while Mythos 5 did so in 39 percent. Mythos 5.2’s rate was also close to Opus 5.5’s 3.3 percent, suggesting that Anthropic has narrowed a gap in containment behavior between its specialized cyber model and its general-purpose flagship. 

Access and safeguards 

For organizations that do not qualify for direct Mythos access, Anthropic is offering an indirect route through Claude Security, its code-vulnerability scanner, which now uses Mythos 5.2 among other models. This allows enterprise customers to benefit from the model’s bug-finding capabilities without receiving unrestricted access to the underlying system. 

Fable 5.2, by contrast, is available to general users and includes classifiers that block penetration testing, exploit generation, and other high-risk cyber tasks. When such a request is blocked, users receive a response from Opus 4.8 instead. Anthropic says the safeguards remain as robust as those in Fable 5.1 but trigger less often during legitimate work, with early testing showing roughly 60 percent fewer cybersecurity blocks. 

The release illustrates the growing tension at the center of AI cybersecurity: models are becoming more effective at finding real vulnerabilities, which can help defenders patch software faster, but the same capabilities could be valuable to attackers if access controls or safeguards fail. Anthropic’s approach is to separate a highly capable model from its most sensitive uses, reserve direct access for vetted organizations, and route public users toward a more restricted product.
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Employee Personal Information
Data Breach Data Leak data security DHS Digital Personal Data Protection Employee Personal Information

Why the DHS Employee Leak Is an Identity-Security Crisis, Not Just Another Data Breach

 


The exposure of information connected to more than 41,000 US Department of Homeland Security personnel demonstrates how an employee directory can become an operational map for attackers.

The dataset, published in late October under the name “Icepick,” reportedly contained names, work email addresses, telephone numbers, office locations, job information and internal group memberships. The affected records included personnel associated with Customs and Border Protection, Immigration and Customs Enforcement, US Citizenship and Immigration Services, and the Cybersecurity and Infrastructure Security Agency.

While contact information may appear less sensitive than passwords or Social Security numbers, the combination of identity and organizational metadata creates a significantly broader security risk. It can help attackers understand who works where, which employees may hold privileged access and how different teams are connected.

Employee Metadata Can Reveal How an Organization Operates

An ordinary staff directory contains more intelligence than organizations often realize.

Job titles, supervisors, office locations, telephone numbers and internal group names can reveal reporting structures and operational responsibilities. When these details concern law-enforcement, intelligence or cybersecurity personnel, they may also expose individuals connected to sensitive investigations, surveillance systems or undercover work.

Attackers do not necessarily need passwords in the initial leak. Accurate employee information can make subsequent social-engineering attempts substantially more convincing.

A criminal could impersonate an internal help-desk employee and refer to the target’s correct department, manager or office. Another approach could involve impersonating the employee while contacting a colleague, telecommunications provider or external contractor.

The exposed information could consequently support spear-phishing, voice phishing, SIM-swapping attempts, password-reset fraud and targeted harassment.

Identity Platforms Are Valuable Intelligence Sources

The group claiming responsibility said the information originated from a DHS identity system associated with Okta. However, Okta said it had found no evidence that its own systems were compromised.

That distinction is important. The appearance of identity-platform data does not automatically mean the platform provider suffered a breach.

Information could potentially be obtained through stolen administrator credentials, compromised endpoints, an improperly protected integration, exported directory records or another system connected to the identity environment. The exact intrusion route has not been publicly established.

Nevertheless, the incident highlights the value of identity infrastructure to attackers. Identity platforms frequently connect employees with cloud applications, administrative tools and internal services. They may contain account attributes, access-group assignments and information about which users can reach particular resources.

Even when passwords are not exposed, this information helps attackers identify valuable accounts and prepare more precise attacks.

The Risk Continues After the Original Data Is Removed

Removing the original website or disrupting its domain does not eliminate the exposure.

Once a dataset has been downloaded, it can be copied, mirrored and circulated privately. Employees whose information appeared in the leak may therefore remain at increased risk long after public attention moves elsewhere.

Contact details may also remain useful for years. Employees can change roles while continuing to use the same email addresses or telephone numbers. Historical group memberships and job information can still help attackers understand an agency’s structure or identify people likely to retain valuable professional relationships.

For personnel working in sensitive or public-facing roles, the consequences may extend beyond phishing. The information could enable doxing, threats, physical surveillance or attempts to identify family members and personal accounts.

What Organizations Should Examine After a Directory Leak

Responding to this type of exposure requires more than resetting passwords.

Security teams should determine whether authentication tokens, active sessions, application credentials or privileged accounts were accessible through the affected environment. Authentication logs should be reviewed for unusual sign-ins, newly registered devices, unexpected multifactor-authentication changes and access from unfamiliar locations.

Organizations should also examine the information included in their identity directories. Internal group names should not disclose more operational detail than necessary, especially when those names identify sensitive projects, investigative capabilities or administrative privileges.

Other important measures include:

  • Reviewing privileged and administrative group memberships.

  • Revoking unnecessary sessions, tokens and application connections.

  • Requiring phishing-resistant authentication for sensitive accounts.

  • Monitoring exposed employees for targeted phishing and impersonation.

  • Restricting bulk exports of identity-directory information.

  • Separating highly sensitive personnel records from general directories.

  • Establishing additional protections for undercover and investigative staff.

  • Reviewing third-party applications connected to identity platforms.

  • Alerting employees whose details may be used in social-engineering attacks.

Security awareness messages should also be specific to the incident. A general warning about suspicious emails may be inadequate when attackers possess accurate information about an employee’s role, colleagues and workplace.

Data Minimization Applies to Internal Systems Too

Organizations commonly apply data-minimization principles to customer information but may overlook internal employee systems.

Identity platforms, human-resources applications and staff directories frequently accumulate information because it is convenient. Each additional attribute increases the intelligence available if an account, integration or export function is compromised.

Organizations should regularly determine which employee attributes are necessary, who can access them and whether they can be exported in bulk. Sensitive operational details should not be stored in broadly accessible identity fields simply because the platform permits it.

The DHS incident illustrates that identity information is not merely administrative data. In the wrong hands, it can reveal an organization’s structure, identify valuable targets and provide the foundation for further attacks.

Protecting identity infrastructure therefore requires securing not only passwords and authentication tokens, but also the metadata surrounding every account.

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Payment API
AI CERT Cyber Attacks NBFCs Payment API

AI-Powered Cyberattacks Cost Small Indian NBFCs Nearly ₹300 Crore in Two Months; CERT-In Warns of Payment API Exploits


India’s smaller Non-Banking Financial Companies (NBFCs) are facing a growing wave of cyberattacks, with scammers siphoning off an estimated ₹300 crore over the past two months. Nearly 150 complaints and First Information Reports (FIRs) have been registered in connection with the incidents.

According to a report published by The Economic Times, the attacks have primarily targeted smaller NBFCs with annual turnovers of approximately ₹1,000 crore or less. Several financial institutions have experienced repeated attacks, exposing weaknesses in their cybersecurity infrastructure and payment systems.

In one reported incident, a Delhi-based NBFC allegedly lost ₹7.5 crore in an initial cyberattack, followed by another loss of ₹7.25 crore just days later. Investigators believe that inadequate security controls and insufficient monitoring have made smaller lenders attractive targets for cybercriminals.

AI tools making cyberattacks easier

Investigations into some of the incidents have identified a Chinese hacking platform known as CyberStrike. Security experts have also raised concerns about the growing use of artificial intelligence-powered toolkits, including ARTEX and CyberStrike, alongside open-source large language models.

These tools are lowering the technical barriers for cybercriminals by automating reconnaissance, vulnerability identification and other stages of cyberattacks. Attackers can use AI-assisted frameworks to analyse targeted systems and identify weaknesses more efficiently.

According to officials familiar with the investigations, attackers typically spend around 10 to 12 days studying an NBFC's payment infrastructure, including transaction processes, whitelisted identification details and application code, before attempting to steal funds.

Experts have also warned that stolen customer information could be exploited for targeted phishing and social engineering campaigns. Scammers equipped with borrowers' names, loan details and lender information may impersonate financial representatives to deceive customers into sharing sensitive information.

CERT-In flags payment API vulnerabilities

India's national cybersecurity agency, the Indian Computer Emergency Response Team (CERT-In), along with CSIRT-Fin, issued a critical advisory on October 9, highlighting cyber threats targeting NBFCs, digital lenders, fintech companies and payment service providers.

The advisory identified vulnerabilities in applications and payment Application Programming Interfaces (APIs) that could allow attackers to steal banking credentials and initiate unauthorised fund transfers through trusted financial infrastructure.

CERT-In also warned that AI-driven attack frameworks have significantly reduced the time between initial system compromise and financial damage, allowing attackers to execute operations within hours rather than days.

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