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VOLTA Infostealer Uses Claude and Gemini to Triage Victims

VOLTA Infostealer Uses Claude and Gemini to Triage Victims

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

The VOLTA-AUTODOXER infostealer represents a significant escalation in malware sophistication, weaponising commercial LLMs via OpenRouter to automatically prioritise stolen victim data by financial value. The malware implements a resilient multi-model fallback chain across Claude, Kimi, and Gemini, actively circumventing individual model refusals to generate monetisation-ranked dossiers. This marks a qualitative shift in infostealer design — transforming raw credential dumps into analyst-grade intelligence reports with minimal human operator effort.

Sakana AI and 360 Launch Fugu and Tulongfeng Models

Sakana AI and 360 Launch Fugu and Tulongfeng Models

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.8 Cohere AI (via HN)

Sakana AI's Fugu and Chinese firm 360's Tulongfeng are frontier AI models positioned as functional alternatives to Anthropic's export-restricted Mythos and Fable 5, with Fugu explicitly designed for agentic orchestration across third-party model APIs. For defenders, the proliferation of cybersecurity-focused frontier models outside US regulatory reach removes a key friction point that previously slowed adversary access to high-capability AI offensive tooling. The agentic, multi-model orchestration design of Fugu in particular introduces compounded supply-chain and prompt-injection risk for any enterprise connecting these models to existing tool ecosystems.

Microsoft MDASH Discovers 16 Windows RCE Flaws

Microsoft MDASH Discovers 16 Windows RCE Flaws

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.8 The Hacker News

Microsoft has disclosed MDASH, a multi-model agentic AI scanning system that autonomously discovered 16 vulnerabilities patched in May 2026's Patch Tuesday, including two critical RCE flaws. The system orchestrates over 100 specialised AI agents in a structured pipeline covering auditing, debating, and proof-of-exploitability stages. MDASH represents a significant shift in how AI is being deployed offensively and defensively within the vulnerability research lifecycle, with direct implications for how agentic AI systems are trusted, scoped, and governed.

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