NSA spies are reportedly using Anthropic’s Mythos, despite Pentagon feud

· Source: AI News & Artificial Intelligence | TechCrunch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Emerging Technologies & Innovation · Depth: Fundamental Awareness, quick

Summary

The National Security Agency (NSA) is reportedly utilizing Mythos Preview, a cybersecurity-focused frontier model developed by Anthropic that was withheld from public release due to its offensive capabilities. This development occurs despite the Department of Defense (DoD), the NSA's parent agency, previously labeling Anthropic a "supply-chain risk" for refusing unrestricted access to its models. Anthropic limited Mythos access to approximately 40 organizations, including the UK's AI Security Institute, with the NSA appearing to be an undisclosed recipient using it for vulnerability scanning. This expanded military use of Anthropic's tools coincides with an ongoing court dispute where the Pentagon argues these tools could threaten national security, stemming from Anthropic's refusal to enable mass surveillance or autonomous weapons development with Claude. However, Anthropic's relationship with the Trump administration appears to be improving, evidenced by a recent productive meeting between Anthropic's CEO Dario Amodei and White House officials.

Key takeaway

For government agencies and defense contractors evaluating AI partnerships, this situation highlights the complex interplay between national security concerns, model access, and vendor relationships. Your teams should scrutinize AI model capabilities for both defensive and offensive potential, and be prepared for evolving policy stances from government bodies. Consider the long-term implications of restricted access models and the legal precedents being set regarding AI deployment in sensitive contexts.

Key insights

Anthropic's restricted Mythos model is being used by the NSA despite prior DoD "supply-chain risk" designation.

Principles

In practice

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Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, AI Security Engineer, Tech Journalist

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI News & Artificial Intelligence | TechCrunch.