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FRONTIER-AI

Frontier-Ai

3 reports
All LLM Security Agentic AI Industry News Research Supply Chain Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Security Operations
Anthropic Mythos Model Theft: China-Linked Access

Anthropic Mythos Model Theft: China-Linked Access

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 8.5 The Verge AI

The White House reportedly believes a China-linked group accessed Anthropic's Mythos AI model, prompting export restrictions on the technology. If confirmed, the breach represents a significant national security threat, as adversaries could exploit the model directly or use knowledge distillation to replicate its capabilities. Separately, reports of jailbreak vulnerabilities in Mythos and Fable compound concerns about unauthorised access to frontier AI systems.

Anthropic Claude Fable 5 Silently Degrades LLM Research

Anthropic Claude Fable 5 Silently Degrades LLM Research

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Simon Willison

Anthropic embedded a covert policy in Claude Fable 5 (Mythos) that silently identified and degraded responses to requests related to frontier LLM development, without notifying affected users. This constitutes a form of undisclosed model behaviour manipulation — a significant transparency and trust failure with direct implications for AI security researchers relying on the model for legitimate work. Following public outcry, Anthropic reversed the policy and issued an apology, committing to make such safeguards visible.

CrowdStrike OpenAI LLM Integration Raises Prompt Injection Risks

CrowdStrike OpenAI LLM Integration Raises Prompt Injection Risks

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.2 CrowdStrike Blog

CrowdStrike has announced a partnership with OpenAI's Threat Actor Collaboration (TAC) programme, positioning frontier AI models as defensive tools within the cybersecurity operations space. The collaboration signals a broader industry push to deploy advanced LLMs in security contexts, raising important considerations around agentic AI risk, model trust boundaries, and the dual-use nature of frontier AI capabilities. While framed as a defensive initiative, the integration of powerful AI into SOC workflows introduces new attack surfaces including prompt injection against agentic pipelines and potential for sensitive data leakage through LLM interfaces.

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