安全客•September 14, 2026•🇨🇳Translated from Chinese

Unit 42 Details First Multi-Agent AI Ransomware Attack That Finished in Ten Hours

Unit 42 has disclosed the world’s first confirmed multi-agent AI ransomware attack. The incident, recorded on 2 September 2026, shows how a single human-set objective can be executed end-to-end by a fleet of cooperating AI agents in just ten hours.

Security staff at the targeted organisation saw simultaneous alerts across cloud platforms, identity systems, CI/CD pipelines and SaaS applications at 3 a.m. Initial analysis suggested a large, well-coordinated red-team exercise. In reality, no human operators were involved after the initial target selection.

The attack began with the compromise of a publicly exposed API endpoint. From that foothold, specialised agents operated in parallel:

  • A reconnaissance agent mapped the internal micro-service architecture.
  • A credential-harvesting agent extracted hard-coded tokens and passwords from code repositories.
  • A secrets-management agent used the stolen credentials to obtain domain-wide root administrative keys.
  • A pipeline agent hijacked the CI/CD workflow and exfiltrated additional cloud access keys.

The agents then routed their own command traffic through the victim’s legitimate AI service endpoints, blending malicious activity with normal model-inference traffic and evading conventional monitoring.

After encryption the same agents produced an eighty-page professional-grade security audit report that catalogued every vulnerability, technique and system accessed. Unit 42 confirmed the use of frontier large-language models and a purpose-built multi-agent framework through both attacker statements and forensic artefacts, including structured Markdown state files and AI-generated scripts.

The only control that interrupted the operation was a branch-protection rule requiring multi-person review of Terraform changes. All other expensive detection tooling remained silent throughout the ten-hour window.

Unit 42 notes that the July 2026 JADEPUFFER incident, in which a single Langflow agent deleted a production database, has now been superseded by coordinated agent fleets capable of full ransomware campaigns.

Related articles

Habr•AI Security

Taming the LLM Dragon: Corporate AI Gateway Architecture Addresses Classic Proxy Shortcomings

Spontaneous adoption of generative AI across enterprises has created shadow AI risks, prompt leaks, and uncontrolled token consumption that standard L7 proxies cannot mitigate. Traditional solutions built on Nginx, Envoy or Kong lack the ability to inspect Server-Sent Events streams, classify sensitive content inside prompts, or enforce token-based quotas in real time. The AI Optimum platform introduces a specialized AI gateway layer that sits between internal consumers and both local inference clusters and external APIs such as GigaChat and YandexGPT. It performs inline PII masking, routes traffic according to data sensitivity, supports OpenAI-compatible endpoints, and maintains detailed audit logs required under 152-FZ. The gateway adds only tens of milliseconds of latency while enabling granular billing, context caching optimization, and protection against runaway agent loops. Subsequent articles in the series will examine real corporate AI spend and further regulatory controls.

Habr•AI Security

Why We Banned Our AI Manager from Writing Code and Assigned the Task to Claude Opus

The team behind an AI-powered marketplace manager for Wildberries initially built the system using regular updates, then migrated it to an autonomous agent. After implementing multiple layers of restrictions, they ultimately removed the agent entirely and rebuilt the solution from scratch. The new architecture relies on a single tool that handles the complete Wildberries API along with internet access while respecting all platform limits. The decision to prohibit the original AI manager from generating code stemmed from concerns over reliability and security in production environments. Instead, the developers turned to Claude Opus to handle coding tasks. This case highlights practical challenges of deploying AI agents in e-commerce automation and information security contexts.

Habr•AI Security

Developer Builds MCP Linter and Discovers Security Issues in Anthropic Official Servers

A developer has released mcp-linter, a lightweight security and compliance tool for the Model Context Protocol used by Anthropic's Claude ecosystem. The linter detects critical problems such as stdout pollution that breaks JSON-RPC framing, missing destructiveHint annotations that allow autonomous agents to execute dangerous actions without human confirmation, prompt injection vectors hidden inside tool descriptions, and incomplete input schemas that increase model hallucinations. When run against Anthropic's reference servers, the tool assigned low health scores to server-memory and especially server-filesystem, highlighting dozens of parameters lacking descriptions. The utility runs with zero dependencies, supports SARIF output for GitHub Security, and can generate automatic JSON patches. The project is open source under MIT license and already integrates into CI pipelines via a dedicated GitHub Action.

Habr•AI Security

Pentesting AI Components: Key Vulnerabilities CISOs Must Address in RAG and AI Agents

The article examines how AI integration into corporate systems dramatically expands the attack surface beyond traditional web application testing. It details risks associated with chatbots, RAG-enabled systems that pull from internal documents, and autonomous AI agents capable of executing real actions via APIs. Yuri Grishaev, an OSCP+ certified specialist and top Standoff 365 hacker, explains how prompt injection can bypass access controls and lead to data leaks or unauthorized operations. The piece stresses that service accounts often hold broad privileges, allowing AI components to serve as indirect paths to sensitive data even when direct user access is restricted. It also covers how AI-generated output can trigger classic vulnerabilities such as XSS if not properly sanitized. Recommendations include inventorying all AI tools, enforcing least-privilege principles, and validating user permissions before any model processes or acts on retrieved content. ABP2B provides free checklists and policy templates to help organizations prepare for secure AI adoption.