When Anthropic decided not to release Mythos publicly, citing safety concerns, the AI community buzzed with speculation. Daniel Stenberg, creator of the cURL tool, took a deep dive into Mythos's ability to find security flaws in source code—specifically in the cURL repository. His findings offer a balanced perspective on the hype versus reality of advanced AI in code analysis. Here are five critical takeaways from Stenberg's analysis, shedding light on what Mythos can—and cannot—do.

1. The Hype Around Mythos Is Largely Marketing-Driven

Stenberg’s personal conclusion is blunt: much of the excitement surrounding Mythos appears to be fueled by marketing rather than evidence. He found no proof that Mythos discovers vulnerabilities with significantly higher accuracy or efficiency than existing tools. While it may be slightly better, the improvement does not justify the dramatic claims. The lesson here is to critically evaluate AI breakthroughs, as early hype often outpaces actual performance.

2. Mythos Shows Marginal Improvement Over Other AI Code Analyzers

When comparing Mythos to other AI-powered code analyzers, Stenberg notes that it performs only marginally better—if at all. Even if Mythos edges ahead, the gain isn't transformative. This suggests that the current generation of AI models has reached a plateau in code analysis capabilities. Developers and security teams should temper expectations and continue using a mix of tools, rather than relying on any single model as a silver bullet.

3. Testing on a Single Repository (cURL) Limits Generalizability

Stenberg acknowledges that his analysis is confined to the cURL codebase. He cannot vouch for Mythos's performance on other projects. A single data point does not make a trend. While cURL is a mature, security-critical project, different repositories may present unique challenges where Mythos could excel—or fall short. This highlights the need for broader, multi-repo evaluations before drawing firm conclusions about any AI tool’s effectiveness.

4. AI Code Analyzers Are Genuinely Superior to Traditional Tools

Despite his skepticism about Mythos, Stenberg emphatically states that AI-powered code analyzers are significantly better at finding security flaws than traditional static analyzers. All modern AI models, including those predating Mythos, excel at this task. The real breakthrough is that anyone with time and curiosity can now use AI to uncover vulnerabilities. This democratization of security testing is a game-changer, even if no single model dominates.

5. The 'High Quality Chaos' Phenomenon Is Real

Stenberg describes the current state as “high quality chaos”—a phase where AI tools produce many false positives and scattered results, but also genuine, serious finds. This chaos is productive because it forces developers to review more code manually, catching bugs that older tools miss. The lesson for teams is to embrace this chaos with proper validation processes, rather than dismissing AI outputs as noise.

In summary, Daniel Stenberg’s review of Mythos offers a reality check amid the AI frenzy. While Mythos is not the revolutionary leap some claim, it is part of a larger wave of AI tools that have raised the bar for code security. The key is to harness this “high quality chaos” wisely, combining AI suggestions with human expertise. For now, no single model has cracked the code—but together, they make software safer.