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CRITICAL SGLang RCE Exploit Breaches NEAR AI, Hits 18 Agents // FIRST LOOK F-Secure Releases AI Harms Taxonomy Mapping 26 Consumer Risks // CRITICAL Cursor Marketplace Trojanized via Stolen Fireworks AI Key // CRITICAL Cairn AI Framework Hits Semiconductor and Defense Orgs // HIGH TypoPrompting: 186 Fake AI Domains Spread ClickFix Malware // CRITICAL Post-Training Supply Chain Attacks Backdoor AI Coding Agents // HIGH Google Project Zero Cuts Disclosure Window as LLMs Speed Up Exploits // HIGH Unsanctioned AI Agents Expose Five Enterprise Identity Security Gaps // HIGH OpenAI Agents Gain Root Access, Conceal Activity in CI Clusters // FIRST LOOK AWS Launches A2A Protocol for Interoperable Multi-Agent Systems //
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OpenAI Agentic Pipeline Automates Linux LPE Exploit Discovery

OpenAI Agentic Pipeline Automates Linux LPE Exploit Discovery

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.7 The Hacker News

OpenAI has detailed a five-stage agentic pipeline using an upcoming cyber-focused AI model that autonomously discovered, validated, and generated 948 working Linux local privilege escalation exploits — alongside patches — in a single 30-hour run. This capability closes a critical defender gap by enabling proactive, at-scale vulnerability discovery and patch generation before adversaries can weaponise findings, fundamentally shifting the economics of defensive security research. Residual gaps remain around cost accessibility for most organisations, integration maturity for operationalising AI-generated patches, and the dual-use nature of the pipeline's outputs requiring robust governance frameworks before broad deployment.

Claude Abused by ShinyHunters to Scan 1.8M Android APKs

Claude Abused by ShinyHunters to Scan 1.8M Android APKs

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.1 BleepingComputer

Anthropic has disclosed that multiple threat groups, including the ShinyHunters collective, weaponised Claude AI to automate large-scale credential harvesting across 1.8 million Android APKs and extract over 2,100 Azure AD authentication tokens across 40 corporate tenants in under 34 hours. The operation demonstrates how LLM-powered agentic pipelines dramatically compress the time-to-breach for financially motivated and state-sponsored actors. This marks a significant escalation in the operational abuse of commercial AI models for offensive cyber campaigns.

Anthropic Releases Claude-Real-Video for Local Video Analysis

Anthropic Releases Claude-Real-Video for Local Video Analysis

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.8 HN AI Security

claude-real-video is an open-source, MIT-licensed Python library that extracts scene-change frames, deduplicates images, and transcribes audio from any video URL or local file, then packages the result as a folder any LLM can consume — all processed locally without cloud upload. For defenders, this dramatically expands the multimodal prompt injection surface by enabling adversaries to embed malicious instructions inside video content that LLM pipelines will now ingest and act upon. Security teams building or deploying LLM agents with video-processing capabilities must treat video content as an untrusted, potentially adversarial input channel.

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