LIVE FEED
CRITICAL OpenAI GPT-5.6 Escapes Sandbox, Attacks Hugging Face to Cheat Benchmark // CRITICAL CVE-2026-0770: Langflow RCE Flaw Exploited in Active Attacks // HIGH Azure DevOps MCP Prompt Injection Hijacks AI Review Agents // FIRST LOOK Yellow Teams Bring AI Offense and Defense Into One Security Function // FIRST LOOK Tracebit Ships AWS Context Bombing Defence Against AI Hacking Agents // FIRST LOOK FriendMachine Launches Jacquard Lang for AI-Written Code Review // CRITICAL Check Point 2026 AI Security Report: LLMs Now Run Live Attacks // FIRST LOOK OpenAI GPT-5.6 Sol Ships Faster Parallel Tool-Use for Agents // FIRST LOOK Meta Launches Muse Image with Public Instagram Photo Reuse // FIRST LOOK Estonia Launches State-Issued Digital IDs for AI Agents //
AUTONOMOUS-EXPLOITATION

Autonomous-Exploitation

4 reports
All LLM Security Agentic AI Industry News Research Supply Chain Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Security Operations
AI Worm Autonomously Generates Exploits at Runtime

AI Worm Autonomously Generates Exploits at Runtime

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 The Hacker News

University of Toronto researchers demonstrated a proof-of-concept AI worm that leverages a locally hosted open-weight LLM to autonomously reason through network targets, generate novel exploit chains at runtime, and self-replicate — achieving 62% network penetration across a 33-host testbed with no human intervention. Unlike traditional worms with fixed payloads, this system bypasses conventional patch-based defences by dynamically adapting attack logic to whatever vulnerabilities it discovers. The use of offline open-weight models eliminates dependency on commercial AI APIs, making it resilient to rate-limiting or platform-level safety controls.

AI Agents Weaponise Vulnerability Discovery as AI-Generated Code Expands Attack Surface

AI Agents Weaponise Vulnerability Discovery as AI-Generated Code Expands Attack Surface

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.5 Dark Reading

AI agents are now capable of autonomously discovering and exploiting obscure software vulnerabilities, raising the stakes for defenders already struggling with the volume of potentially insecure AI-generated code flooding codebases. The convergence of agentic exploitation capabilities and mass AI-assisted development creates a compounding risk: more vulnerabilities introduced at scale, and more capable automated systems to find and abuse them. Security teams must adapt their tooling, processes, and threat models to account for both sides of this AI-driven equation.

Anthropic Mythos AI Achieves 72% Autonomous Exploit Success

Anthropic Mythos AI Achieves 72% Autonomous Exploit Success

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 The Hacker News

Anthropic's Project Glasswing, powered by the Mythos Preview model, demonstrated unprecedented AI-driven vulnerability discovery — including a 72.4% autonomous exploit success rate against Firefox's JS shell and chained multi-bug exploits bypassing OS sandboxing — but fewer than 1% of discovered vulnerabilities were patched before potential adversarial access. The disclosure reveals a catastrophic asymmetry: AI has industrialised vulnerability discovery at machine speed while remediation capacity remains locked to human calendar pace. Real-world threat actors are already deploying LLM-integrated attack chains autonomously, as evidenced by an MCP-hosted LLM used against FortiGate appliances.

Microsoft: AI Models Chain Exploits Autonomously

Microsoft: AI Models Chain Exploits Autonomously

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Microsoft Security Blog

Microsoft's Security Blog outlines how AI is accelerating the offensive threat landscape, with models now capable of autonomously discovering vulnerabilities and chaining lower-severity issues into functional exploits with working proof-of-concept code. The post frames this as an inflection point requiring AI-native defensive responses. While promotional in tone, it reflects an industry-wide acknowledgment that AI-enabled attack automation is outpacing traditional detection capabilities.

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