When Anthropic announced Mythos, their latest AI model designed for code analysis, the tech world buzzed with excitement. Yet after a detailed examination by curl creator Daniel Stenberg, the narrative shifts from groundbreaking breakthrough to marketing hype. In an extensive blog post, Stenberg dissects what Mythos actually delivers—and the results are far less dramatic than many anticipated.
The Mythos Controversy
Anthropic deemed Mythos too dangerous for public release, citing its potential to discover vulnerabilities with unprecedented ease. This decision sparked debates about AI safety and the ethics of powerful code analysis tools. However, Stenberg's hands-on testing on the curl source code repository reveals a more grounded reality.
Stenberg's Assessment: Hype Meets Reality
In his analysis, Stenberg concludes that the massive excitement surrounding Mythos is primarily marketing. He states, "I see no evidence that this setup finds issues to any particular higher or more advanced degree than the other tools have done before Mythos." While conceding that Mythos might be slightly better than its predecessors, the improvement is marginal—not the leap forward that would make a significant dent in code analysis capabilities.
A Single Test Case
Stenberg emphasizes the limitation of his review: he only tested Mythos on the curl codebase. "This is just one source code repository and maybe it is much better on other things," he notes. "I can only tell and comment on what it found here." This honest caveat reminds readers that benchmark results can vary widely across different projects and languages.
The Real Value of AI Code Analyzers
Despite his skepticism about Mythos's unique abilities, Stenberg reiterates a broader truth: AI-powered code analyzers are significantly better at finding security flaws than traditional tools. He writes, "All modern AI models are good at this now. Anyone with time and some experimental spirits can find security problems now." The key insight is that the barrier to discovering vulnerabilities has been lowered dramatically—not by a single model, but by the collective advancement of AI in this domain.
This democratization of vulnerability discovery is what Stenberg calls "the high quality chaos." While it may not be as orderly or predictable as traditional analysis, the sheer volume of issues that can now be unearthed is unprecedented. The danger, then, is not that one AI is too powerful, but that any determined individual can now leverage AI to find exploits—making model-specific restrictions like those applied to Mythos somewhat moot.
Conclusion: A Tool Among Many
Stenberg's analysis serves as a healthy reality check. Mythos may be a capable AI code analyzer, but it is not the singular, game-changing force that its hype suggested. The real story is the broader capability of AI to enhance code security—a capability that is now widely accessible. As Stenberg puts it, "The high quality chaos is real."
For developers and security researchers, the takeaway is to focus on the tools that work best for their specific codebases, rather than chasing the latest headline. The above assessment shows that even the most talked-about models often deliver only incremental improvements.