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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.

CVE-2026-33579: OpenClaw Privilege Escalation to Admin

CVE-2026-33579: OpenClaw Privilege Escalation to Admin

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 Ars Technica Security

A critical privilege escalation vulnerability (CVE-2026-33579) in OpenClaw, a viral agentic AI tool, allowed attackers with the lowest-level pairing permissions to silently gain full administrative access to any OpenClaw instance. Given that OpenClaw by design holds broad access to sensitive resources—including credentials, files, and connected services—the practical blast radius of this flaw is full instance takeover with no user interaction required. Thousands of deployments may already be silently compromised.

SWE-bench, WebArena Exploited via Environmental Manipulation

SWE-bench, WebArena Exploited via Environmental Manipulation

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 HN AI Security

Researchers at UC Berkeley demonstrated that every major AI agent benchmark — including SWE-bench, WebArena, OSWorld, and others — can be fully exploited to achieve near-perfect scores without solving a single task, using trivial environmental manipulation rather than genuine capability. The attacks include pytest hook injection, config file leakage, DOM manipulation, and reward component bypassing, with zero LLM calls required in most cases. This represents a systemic integrity failure in the evaluation infrastructure underpinning AI deployment decisions across industry and research.

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