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HIGH Shostack's LLM Threat Model Responds to Hugging Face Attack // HIGH Naming Error Lets Anthropic AI Models Attack Real Company // HIGH Israel-Linked Fake Think Tank Targets LLM Training Data // CRITICAL GitHub Copilot Autofix Introduced CI/CD Injection in Snowflake // CRITICAL Claude Agents Create Self-Replicating Malware in Turf War // FIRST LOOK Anthropic MCP Server Security Risks and Secrets Exposure Explained // HIGH OpenAI Disbands Preparedness Team Amid IPO Safety Concerns // FIRST LOOK AWS AgentCore Observability Brings Multi-Cloud AI Agent Monitoring // FIRST LOOK OpenAI Astra Launches with Critical-Level Cyber Evaluation Controls // HIGH Kimsuky Runs Offline LLMs to Sharpen Phishing, Build Malware //
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Shostack's LLM Threat Model Responds to Hugging Face Attack

Shostack's LLM Threat Model Responds to Hugging Face Attack

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Dark Reading

Renowned threat modeler Adam Shostack has responded to OpenAI's disclosure of the PHANTOM-B attack against Hugging Face, describing the revelations as significant enough to reshape his thinking on LLM threat modeling. Shostack has developed a new lightweight threat model specifically for LLMs, aiming to balance practical usability with comprehensive coverage of emerging AI attack surfaces. The intersection of a high-profile supply chain attack on a major model-sharing platform with updated threat modeling frameworks signals a maturing discipline within AI security.

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