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FIRST LOOK CrowdStrike Acquires Fiddler to Launch Falcon Observe AI Agent Monitoring // FIRST LOOK CrowdStrike Launches AI Kill Switch for Rogue Agent Control // FIRST LOOK CrowdStrike Acquires Dreadnode to Add AI Red-Teaming to Falcon // FIRST LOOK Anthropic Warns RSI Threshold Is Now Imminent in S-1 Filing // HIGH NCSC UK: AI-Enabled Cyber Intrusion Threat Outlook to 2028 // CRITICAL CVE-2026-93680: Langflow RCE via Auth Bypass Hits All 1.x // HIGH Muse AI Agent Exposed as Covert Commercial Surveillance Tool // CRITICAL VOLTA Infostealer Uses Claude and Gemini to Triage Victims // HIGH AI Tools Accelerate Software Vulnerability Discovery at Record Pace // CRITICAL VOLTA Infostealer Uses LLM Chain to Rank Victim Assets //
REFUSAL-BYPASS

Refusal-Bypass

2 reports
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VOLTA Infostealer Uses LLM Chain to Rank Victim Assets

VOLTA Infostealer Uses LLM Chain to Rank Victim Assets

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 Cisco Talos

VOLTA-AUTODOXER is a self-deleting Windows infostealer that integrates a multi-model LLM pipeline to automatically triage stolen credentials and rank victims by monetisation value, functioning as an AI-powered intelligence analyst for cybercriminals. The malware submits a structured victim evidence bundle to OpenRouter-hosted models—defaulting to Claude with Kimi-K2 and Gemini fallbacks—and prompts the model to produce a prioritised 'target assessment' dossier. The fallback chain, specifically engineered to bypass model refusals, represents a meaningful escalation in adversarial LLM abuse within commodity malware.

Malware Uses Prompt Injection in JavaScript to Evade LLM Tools

Malware Uses Prompt Injection in JavaScript to Evade LLM Tools

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Schneier on Security

A malware developer has been observed embedding fake system instructions and policy-triggering content — including references to nuclear and biological weapons — inside JavaScript comment blocks to confuse or trigger refusal behaviour in LLM-powered security analysis pipelines. The technique does not affect code execution but is specifically designed to disrupt naive AI-first triage tools that feed raw file content to language models without isolating it as untrusted data. Traditional static analysis methods remain unaffected, but the approach signals an emerging class of anti-AI-analysis evasion techniques.

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