Cybersecurity Looks Like Proof of Work Now

2026-04-14 · Source: Simon Willison's Weblog · Field: Technology & Digital — Cybersecurity & Data Privacy, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The UK's AI Safety Institute (AISI) recently published an independent evaluation of Anthropic's Claude Mythos Preview, confirming its exceptional effectiveness in identifying security vulnerabilities. This analysis, detailed in "Our evaluation of Claude Mythos Preview’s cyber capabilities," supports Anthropic's claims regarding the model's cyber capabilities. Drew Breunig observed that the AISI report indicates a direct correlation between the number of tokens (and associated cost) spent and the quality of the security review results. This creates a significant economic incentive for organizations to invest heavily in security reviews, framing cybersecurity as a "proof of work" problem where increased expenditure leads to better system hardening. This dynamic also enhances the value of open-source libraries, as the cost of securing them can be amortized across a wide user base.

Key takeaway

For CTOs and VPs of Engineering evaluating cybersecurity strategies, the emergence of AI models like Claude Mythos Preview fundamentally shifts the cost-benefit analysis of security. You should consider increasing budget allocations for AI-powered vulnerability assessments, understanding that greater investment in compute (tokens) directly translates to enhanced system hardening. This also reinforces the strategic value of integrating well-maintained open-source libraries, as their collective security investment offers a cost-effective defense.

Key insights

Cybersecurity is becoming a "proof of work" problem, where spending more tokens directly improves vulnerability detection.

Principles

• Increased token expenditure correlates with better security outcomes.
• Open-source libraries gain value through shared security investment.

In practice

• Allocate more compute resources for thorough security audits.
• Prioritize open-source components for shared security benefits.

Topics

• AI Cybersecurity
• Proof of Work Security Model
• UK AI Safety Institute
• Claude Mythos Preview
• Vulnerability Detection

Best for: CTO, VP of Engineering/Data, AI Security Engineer, Security Engineer, Director of AI/ML

Related on AIssential

• AI cybersecurity is not proof of work — AI cybersecurity success depends on model intelligence and understanding, not merely "proof of work" computational resources.
• Quoting Bobby Holley — AI models like Claude Mythos Preview can significantly enhance vulnerability detection and defensive security.
• Our latest investment in open source security for the AI era — Industry leaders are investing $12.5 million and AI tools to proactively secure open source against AI-driven threats.
• this is really bad... — AI significantly amplifies cyberattack capabilities, necessitating advanced AI-driven defenses and highlighting geopolitical risks.
• With $1 Cyberattacks on the Rise, Durable Defenses Pay Off — Generative AI accelerates both cyberattacks and defenses, necessitating a shift to foundational security rather than reactive patching.
• Mythos... I can't sleep — Anthropic's "Mythos" model poses a cybersecurity threat by autonomously discovering thousands of high-severity software vulnerabilities.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.