uCreater is Android spyware disguised as trusted apps, stealing credentials, messages and wallet data through a rentable platform. uCreater is a rentable Android spyware platform that hides inside repackaged legitimate apps, targeting messaging services and mobile wallets. Its YingGai framework can intercept one-time passwords, steal account and chat data, collect contacts, manipulate sessions and spoof camera feeds to bypass identity checks. Victims may install modified versions of Telegram, WeChat or GCash through sideloading or social engineering. Researcher Ovi found stolen data on a control server, but the number of victims and links between operators and customers remain unconfirmed. “The repackaged APK can be relatively thin: target-specific instrumentation is retrieved after installation from the operator’s Alibaba OSS and MinIO infrastructure, including buckets and paths named Huuk_<target> and fengzhuang-app (literally, “packaging”). Distribution hosts include telegram-apk.uzyzx.com.” researcher Ovi states. “I could reconstruct much of the post-installation execution chain from the recovered code, but I could not directly establish the initial delivery mechanism for every victim.” Ovi rebuilt most of the chain from recovered code but couldn’t establish how every victim got the first install. The product is older than the samples. The package com.ucreater.autotool dates to about 2014, Windows tools tied to ucreater.com were signed between 2016 and 2019, and the YingGai mobile line shows up in records from September 2021. The samples Ovi studied run from 2024 to 2026: uip.exe builds from February to December 2024, Telegram repacks from July and November 2024, the WeChat host and Telegram framework from December 2024, and the Onion Box client from March 2026. The control server’s ICP filing, 湘ICP备2024061212号, dates to 2024. The infrastructure hints at scale, though not at victim numbers. Ovi counts about 20 target apps with confirmed code support, six operator domains with dozens of subdomains, three Redis buses, two MQTT buses, and a cloud-phone cluster in Changsha spread across several ISPs. A Censys search for the operator’s Tengine gateway fingerprint returned roughly 48 hosts. It looks like a multi-year product sold to many downstream operators, but he can’t say how many customers used it or how many devices fell. Attributing the platform to a specific legal entity is more difficult. Hunan Youchuang Technology is linked to the ucreater.com brand and a self-signed certificate on videoflow.cn. Changsha Sukai Network holds the ICP filing for the control server’s main domain, qianxuncc.cn, while Changsha Zhuoqing E-commerce appears as the signing identity on uip.exe, using a DigiCert certificate. A common owner is plausible but unproven. Another lead involves hunanzhijing.com, a domain belonging to the Hunan Intelligent Police Public Safety Technology Research Institute. It shares the same gateway fingerprint, and an official Hunan document connects the institute to an intelligent-policing research project. However, shared cloud infrastructure establishes only co-tenancy, not ownership or operational involvement. There is no evidence that the police developed or operated uCreater. Other infrastructure links also require caution. A Thailand-based relay used by the Telegram component, hosted on Huawei Cloud, also served gambling websites, an advertising-account service and an SMS service. The cloud-phone web client displayed a certificate for another vendor, while the gateway served a Vue application titled 舆情交易, meaning “public-opinion trading”. These observations are leads for further investigation, not proof of common ownership. uCreater relies on app virtualization, a technique already identified by Zimperium as a growing mobile threat. VirtualApp runs another application inside a container, LSPatch merges code hooks into repackaged apps without requiring root access, and Pine hooks Android’s runtime. Together, these components allow malicious code to operate inside a legitimate app while it is signed in and has access to decrypted data. “uCreater is notable because it turns these techniques into a reusable, remotely managed product.” the researchers continues. Operators distribute modified versions of Telegram-X, Telegram-Web and other messaging apps, integrating modules through LSPatch or loading them from an asset called TgJarPine.dex. WeChat can instead run inside a VirtualApp container, including the va_vx or Onion Box host. LSPatch also stores the original APK under cache/lspatch/origin/*.apk. The presence of this path or the specific asset can help identify an infected device. The approach differs from GodFather, a banking trojan documented by Zimperium that runs apps in a virtual container and displays fake overlays to capture credentials. uCreater operates inside the real application without relying on an overlay. It uses XposedBridge hooks, reflection and access to the app’s own database interfaces. This allows it to reach beyond login screens and potentially support any application for which the vendor develops a module. Ovi found no evidence linking the two platforms. One distinguishing feature is a local command server embedded in each hooked application. It exposes around 40 numbered handlers, with descriptions and parameters that allow operators to discover available functions. The code supports device monitoring, contact and SMS collection, installed-app enumeration, message sending, chat-history retrieval, session management and account-related actions. The Telegram module can execute SQL queries directly against the application’s live database through its own interface. Its Db_MessagesStorageRawQuery handler accepts operator-supplied SQL and returns results through compatibility classes that recreate Telegram objects. This can expose information that would otherwise be protected in the app’s encrypted or locally stored data while the application is running. The module also hooks Telegram’s SRPHelper.getX routine, capturing the two-step verification password in cleartext before hashing and storing it for exfiltration. The code provides functions to list or reset sessions, change or confirm a linked phone number, delete an account and lock users out. It can also remove its own session from the victim’s device list, potentially concealing the operator’s access. Ovi stresses that the source code demonstrates these capabilities, not that every action has been successfully performed against victims. The implant retrieves its control-server address from Redis using the Domain key, optionally selecting a value for a particular channel. It caches the address and can fall back to a hardcoded value, explaining why callback domains differ between builds. Signed JSON beacons report to endpoints under /api/callback/Tg*, while command requests include a device fingerprint and signature and use retries with increasing delays. Updates and additional code arrive through Alibaba Object Storage Service (OSS) and MinIO. The Telegram module retrieves an update manifest and hooks from designated storage paths, while a contacts.vcf file can supply contact information. The WeChat component subscribes to a Redis channel identified by a device-specific machine ID, with its host and credentials embedded in a child code file. The WeChat module focuses on account access rather than chat-history collection. Its _2_0_UploadAppLoginInfo command retrieves login identity information, after which the app data can be packaged, uploaded to OSS and used in account cloning. Ovi found no evidence that the examined sample accessed WeChat’s encrypted database. Ovi tested uCreater on a Pixel 10 Pro running Android 17 and found that its bundled Pine engine fails because of changes to Android’s internal APIs. The latest sample, dated July 29, 2026, still uses the outdated engine. Although this creates obstacles for operators, updating Pine could restore functionality, and other devices remain untested. Ovi also identified five samples that successfully contacted the control server, confirming active deployments targeting Telegram, Telegram-X and WeChat. Ovi and researcher Jurre discovered the platform’s source code after finding an exposed GitLab server. Other infrastructure was protected by IP allowlists, but this server contained the control platform’s source code, configuration and operational details. The repositories revealed Spring Cloud services for command dispatch, authorization capture, data collection, backups and message bridging, alongside a Vue-based administration console. Thirteen repositories contained commit histories identifying roughly three dozen developers, with activity timestamps consistent with China time. At least one commit used a corporate email domain associated with Tanzhou, a Changsha software firm. uCreater’s backend is a rented, multi-tenant platform that lets operators manage infected devices, steal account data and automate contact collection. Kafka, RocketMQ, Redis and Nacos coordinate commands and schedule activity to avoid triggering security controls. The infrastructure runs on Aliyun and Huawei Cloud, with control services and storage hosted in China. Although GitLab records identify developers behind key components, Ovi stresses they are not confirmed operators. The recovered backend did not appear to support Telegram, and some callback domains are no longer active. Ovi attributes the platform to a vendor in Changsha and Hunan operating through three companies, each linked to a different technical or infrastructure artifact. The reseller model, focus on mobile wallets and theft of authentication codes suggest financially motivated crime, but the researcher presents this as an assessment rather than a proven fact. The technical findings show that uCreater goes beyond a conventional banking trojan or a tool that merely steals credentials. By embedding code inside legitimate applications, it can manipulate sessions, access account data, collect contacts and potentially take control of messaging accounts. Its rented, multi-tenant architecture also allows different operators to use the same underlying platform. The scale of the infrastructure indicates a substantial operation, but neither the number of customers nor the number of compromised devices has been established. Likewise, infrastructure overlaps and developer records provide investigative leads, not conclusive proof of who operated the spyware or who was responsible for individual attacks. “Mapping points to a commercial resale structure, and targeting driven by wallets and OTPs. My assumption is financially-motivated crimeware.” concludes the report. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Kali Linux has released version 2026.3, its third release this year, featuring six new hacking tools, desktop environment improvements, refreshed build scripts, and more. This Linux distribution is available as an installable OS or as a live environment and supports a wide range of hardware, including Raspberry Pi devices and Android phones via Kali NetHunter. Kali is designed for ethical hackers and cybersecurity professionals who want to perform red-teaming, penetration testing, security assessments, and network research. In this release, the Kali Team has updated 126 packages, added new libraries, and upgraded the kernel from 6.19 to 7.1.5. It has also added 6 new tools, including: Kali Linux 2026.3 also brings desktop environment updates, including KDE Plasma 6.7 and new icons. KDE Plasma 6.7 includes performance and integrated Intel GPU power-usage improvements, plus workflow optimizations.
Introduction AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies. Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles. First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules. You can use Kaspersky Endpoint Security, now also available as Kaspersky Next, to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints. Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies. Popular AI applications and services in organizations Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study. # AI product 1 Claude 2 Claude Code 3 Codex 4 OpenCode 5 Zed 6 Wispr Flow 7 LM Studio 8 ZCode 9 Chatbox 10 Cherry Studio The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow). According to open sources, these are the most widely used AI services in 2026: # AI service 1 ChatGPT 2 Google Gemini 3 Claude 4 Grammarly 5 DeepSeek While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services. From 2025 to date, more than 2500 vulnerabilities have been published: Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 (download) * Data for previous quarters was updated and may differ slightly from previously published data. Application Control and Web Control To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories. These categories classify installed software and give you granular control over which approved applications can run in the organization. Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use. This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees. Here is what this functionality looks like to KSC administrators: KL categories available when configuring Application Control For details on how to configure Application Control in KSC, follow this link. In Kaspersky Endpoint Security (KES), Application Control can operate in two modes: Denylist. In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode. Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules. Below are the steps to configure Application Control to block AI tools. Configuring Application Control for the AI Tools category in KES With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start. KES notification about an attempt to launch an AI agent For more on how to configure Application Control in KES, follow this link. Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are. AI Tools KL category The View applications drop-down list shows products for a specific vendor. Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning. KES notification about an attempt to access a prohibited web resource When configuring access, the administrator can block websites that provide AI as a service. Website categories For more on configuring Web Control, follow this link. The help portal also provides information on website categories that can be assigned to resources to configure access control. Kaspersky Endpoint Detection and Response To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity. Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events. For example, in a recent Armored Likho campaign, we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules. So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device. In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task. We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_**, where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys. ai_agent_opencode_access_to_sensitive_file_win Tap to previewTap to hide The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation. ai_agent_dangerous_execution_mode Tap to previewTap to hide The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data. suspicious_mcp_server_artifact Tap to previewTap to hide The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way. llm_api_access_by_unusual_apps Tap to previewTap to hide Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API. local_ai_model_artifact Tap to previewTap to hide Kaspersky Threat Intelligence Portal AI agent profiles The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the ai_ prefix: AI agent profiles on Kaspersky TIP The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for. Claude profile on Kaspersky TIP The description also lists ways to detect the software through standard system auditing mechanisms: Windows: events from standard audit logs and Sysmon events Linux: syscall audit events and Sysmon events Network auditing: a set of protocols and the traffic generated by the software The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts. Detection section in the Claude profile Hunt Hub In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand. This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups. In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents. Rules in the Hunt Hub repository The rule card provides: Detailed description Rule’s severity level, confidence level, and alert format Detection tips Description of possible false positives Platforms the rule can run on Tactics, techniques, and sub-techniques covered by the rule Groups that use the technique covered by the rule Detection logic that shows what exactly triggers the rule Detection rule card in Hunt Hub Kaspersky Scan Engine Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents. Detection of a malicious agent skill in Kaspersky Scan Engine Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size. Detection of an LLM with malicious code in Kaspersky Scan Engine Conclusion AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this: Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity. Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host. Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources. Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs. When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.
Security Affairs AI-CYBERSECURITY newsletter includes a collection of the best articles and research on AI in the international landscape Artificial intelligence is rapidly changing cybersecurity, reshaping both the techniques used by attackers and the tools available to defenders. AI agents can automate tasks, analyze large amounts of data, discover vulnerabilities and accelerate offensive operations. At the same time, AI is creating new capabilities for threat detection, incident analysis and response. Security Affairs’ new newsletter follows this evolution, covering every week the latest threats, attacks, vulnerabilities and developments at the intersection of AI and cybersecurity. AI-CYBERSECURITY Newsletter 25-year-old Israeli founder sells stealth AI startup for up to $35 million without raising capital OpenAI claims 100-plus secret files followed ex-employee back to Apple Open weight takes 71.5% of tokens and 23% of spend OpenAI Models Hacked Data Portals, Raided a Crypto Wallet to Finish Tasks Meta removes language that raised scraping and AI training concerns Can LLMs Supercharge Geolocations? Our Tests Show Big Improvements But Humans Remain Key Agents are your newest insiders. Security must move at their speed Anthropic Acquires Confidential AI Startup Opaque Systems ATT&CK v20: Critical Infrastructure Campaigns, Adversary Use and Abuse of AI, & Iranian Cyber Espionage UK Election Watchdog Develops AI System to Flag Deepfakes Within 3 Hours Meta’s Muse hits 10M U.S. downloads in 40 days, a milestone reached by only 11 other apps Suppressing misalignment monitoring alerts and disabling misalignment monitoring Anthropic pitches AI safety bill that may block a model if 50+ people could be killed Making attacking agents pay LiteLLM Compromised in Supply Chain Attack Delivering Credential Stealer and Backdoor AI Just Found a Bug That Puts up to 70% of the World’s Websites at Risk AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise Torvalds: AI is fine for competent devs, but spare us the ‘drive-by’ patches Our latest defenses against evolving web scams UK government sets out $800 million of AI security funding Securing our frontier model training environment Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
Security Affairs Malware newsletter includes a collection of the best articles and research on malware in the international landscape Malware Newsletter Auditing the auditor: Unmasking Apex Accelerator GhostAction Attack Escalates By Targeting GitHub Users Plex vulnerabilities being exploited in the wild to drop a cryptominer WordPress Rank Math SEO PRO Plugin 3.0.123 is vulnerable to a high priority Backdoor ROMULUS: Analysis of a New Multi-Payload ClickFix Delivery Platform Hackers Hijack Five OVHcloud CLI Releases to Distribute Secret-Stealing Malware Pegasus and Mercenary Spyware Attacks on Serbian Pro-Democracy Figures When wipers leave backups: an analysis of ArgonWiper’s encryption workflow LiteLLM Compromised in Supply Chain Attack Delivering Credential Stealer and Backdoor Operation FlashHole: Lazarus breaks security, defies control Cross-Platform Malware Fingerprint Identification with a Convolutional KAN–Transformer Hybrid Architecture Geometry-Aware Risk-Guided Active Learning for Malware Concept Drift Adaptation Mal-AAEGAN: Adversarial Autoencoder with Double-Critic WGAN-GP for Malware Generation Q-MALDetect: Quantum Image-Based Malware Detection and Adversarial Robustness Analysis Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
A new round of the weekly Security Affairs newsletter has arrived! Every week, the best security articles from Security Affairs are free in your email box. Enjoy a new round of the weekly SecurityAffairs newsletter, including international press. Capcom Data Breach Exposes 11.7 Million Email AddressesU.S. CISA adds Arista VeloCloud Orchestrator flaw to its Known Exploited Vulnerabilities catalogPHP Warns of New Code Execution Flaw Days After PHP2Shell PatchesMeta Faces $25 Billion Penalty Linked to Cambridge Analytica ScandalCapcom Data Breach Exposes 11.7 Million Email AddressesU.S. CISA adds Arista VeloCloud Orchestrator flaw to its Known Exploited Vulnerabilities catalogPHP Warns of New Code Execution Flaw Days After PHP2Shell PatchesMeta Faces $25 Billion Penalty Linked to Cambridge Analytica ScandalPHP2Shell: Millions of Attacks, No Confirmed Successful Exploits YetINTERPOL Operation Serengeti III Nets 1,462 Arrests and Recovers $105 Million in Cybercrime CrackdownMicrosoft Links ConnectWise Breach to China-Linked Group Using AI Pentesting Tool ARTEXSignal Vulnerability Could Keep Calls Alive After Users Hang UpPHP2Shell Attacks Started Four Days Before Public Disclosure, AWS ReportsFBI and CISA Warn of SafePay Ransomware Attacks on 600 OrganizationsRussian Satellite Makes a Close Approach To A Commercial Radar Satellite Used by UkraineOpenAI Reports Two Incidents of AI Agents Disabling Their Own OversightUS Government Orders DeepSeek Out of Federal Civilian SystemsPHP Prepares Emergency Fixes for Two Critical Remote Code Execution FlawsNSO Spyware Pegasus Hit a Serbian University Rector Six Days Before Snap ElectionsNikkei data breach affected over 1.8 million recordsGoogle Cloud Outage in London Disrupts Revolut Banking ServicesAfD Email Breach Exposes 421,000 Messages in Anonymous Online ArchiveMicrosoft Treats AI Agents as Insider Threats in New Security FrameworkPlex2Shell Exploit Used to Turn Vulnerable Plex Servers Into Crypto MinersMeta wants to scrape your website to train its AI, and you can’t say noDeepfakes and Election Manipulation: UK Watchdog Seeks Faster DetectionBig Fish: OpenBSD Root Exploit Chains a 37-Year-Old BSD Bug With a Kernel FlawAttackers Abuse Stolen ConnectWise Application Credentials to Target Microsoft 365Japan’s SiteGuard WAF flaw CVE-2026-106931 is already being exploited16-Year-Old Broadcom Wi-Fi Bug Lets Attackers Take Over Routers Without a PasswordRust-Based CikaBika Bot Turns Discord into a Command Center International Press – Newsletter Cybercrime U.K. Sanctions Cryptomus for RU Money Laundering Anonymous hackers claim to leak 421,179 internal emails from Germany’s Russia-aligned AfD Feds Suspect Ransom Negotiator Ran ShinyHunters Extortion Site #StopRansomware: SafePay Ransomware Cybercrime crackdown in Africa nets 1,462 arrests, recovers over USD 105 million U.K. Sanctions Cryptomus for RU Money Laundering Nigerian Man Sentenced To Eight Years For Hacking, Fraud, And Identity Theft Schemes 1 Million Impacted by Capcom Data Breach Malware Auditing the auditor: Unmasking Apex Accelerator GhostAction Attack Escalates By Targeting GitHub Users Plex vulnerabilities being exploited in the wild to drop a cryptominer ROMULUS: Analysis of a New Multi-Payload ClickFix Delivery Platform Hackers Hijack Five OVHcloud CLI Releases to Distribute Secret-Stealing Malware Hacking ConnectWise (SkyKick) Vendor Credentials Abused in M365 Attacks One Parser, Two Bugs, Many Routers: Exploiting Broadcom WiFi Across eCos and Linux Plex2Shell Unauthenticated RCE in Plex Media Server Big Fish: Exploiting a 37-Year-Old Kernel Bug in OpenBSD Rapid Response: php2shell, Leaked Critical PHP Vulnerabilities (CVE-2026-109618 & CVE-2026-109619) Making attacking agents pay LiteLLM Compromised in Supply Chain Attack Delivering Credential Stealer and Backdoor Amazon Threat Intelligence identifies PHP2Shell exploitation prior to public disclosure in MadPot Proof-of-concept exploit released for PHP vulnerabilities (CVE-2026-109618, CVE-2026-109619) PHP2Shell Exploit Tooling Found on Exposed Attacker Server Intelligence and Information Warfare TAG-193’s Regional Espionage Operations ATT&CK v20: Critical Infrastructure Campaigns, Adversary Use and Abuse of AI, & Iranian Cyber Espionage No to Palantir – Five Times UK Election Watchdog Develops AI System to Flag Deepfakes Within 3 Hours Pegasus and Mercenary Spyware Attacks on Serbian Pro-Democracy Figures Russian satellite makes possibly unprecedented approach to another spacecraft without permission AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise Operation FlashHole: Lazarus breaks security, defies control Pegasus spyware founder urges governments to trust his tech and ditch US AI Cybersecurity Character.ai files for bankruptcy following lawsuits alleging ties to teen suicides Cybersecurity Jobs Report 235,786 Agents are your newest insiders. Security must move at their speed Google Cloud services face outage in London, Revolut services down too Government websites go dark after AI-discovered flaw sparks security scramble Widespread PHP Flaw May Cause Many Site Outages Today Suppressing misalignment monitoring alerts and disabling misalignment monitoring AI Just Found a Bug That Puts up to 70% of the World’s Websites at Risk CVE-2026-113380 — RingRTC (Signal): Flooded Call Event Queue Blocks Local Hang-Up Cellebrite’s Advanced Unlocks and the case of the missing iPhone 17 Meta ordered to pay $25 billion penalty to New Mexico in data privacy case PHP Warns of Another Unpatched RCE Two Days After Emergency PHP2Shell Patches Our latest defenses against evolving web scams Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
A new UEFI bootkit named BlackX, deployed in an espionage campaign in early 2026, exploited the old Baton Drop vulnerability to bypass Secure Boot and load a malicious driver designed to interfere with security solutions from Kaspersky and Avast. The operation has been attributed with medium confidence to APT41, a Chinese state-sponsored threat group also known as Brass Typhoon, Wicked Panda, and Barium. It was discovered in early 2026 and targeted diplomatic agencies in Central Asia, particularly in Tajikistan, Uzbekistan, and Turkmenistan. Kaspersky researchers analyzed the BlackX bootkit and found similarities with BlackLotus, which has been circulating on hacker forums since October 2022 and leveraged the Baton Drop vulnerability (CVE-2022-21894) to bypass Secure Boot protection. ESET confirmed in March 2023 that BlackLotus exploited Baton Drop, and at the time it could compromise even fully patched Windows 11 systems.
A memory-corruption flaw in Google's Brotli decoder sits in many programs that unpack Brotli-compressed data, among them Bun, the Python Brotli and brotlicffi packages, and Envoy proxies set up to decompress Brotli. Tracked as CVE-2026-111497, the flaw allows crafted compressed data to overwrite past the end of a buffer and corrupt a program's memory, potentially
LiteLLM on Friday revealed that an Agent Platform master key accidentally published to a public GitHub repository in late May 2026 ultimately allowed the threat actor to push a malicious version of the popular Python package. "On May 27, 2026, a configuration file was committed to the public LiteLLM-Labs/lite-harness repository which inadvertently contained the master key to a deployment of
Google has updated its privacy policy to spell out that using personalized artificial intelligence (AI) features in Search or Gemini could allow the company to serve targeted ads to users based on their activity on other connected Google services. "You may choose to allow certain Google services, including services like Search or Gemini Apps, to access data from other Google services you use,
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