AI Code Analysis Tools: Reality Check on Mythos vs. Curl

The AI code analysis tool Mythos was recently put to the test against the curl project, a highly scrutinized codebase. Daniel Stenberg, the lead developer behind curl, reviewed Mythos’s findings. Out of five reported vulnerabilities, only one was deemed valid, and it was rated low severity. Three were false positives, and one was simply a bug, not a security flaw. This evaluation underscores that while AI tools are evolving, they are not yet infallible, especially against mature, heavily audited projects.

Mythos’s performance against curl, a project with decades of development, countless hours of fuzzing, static analysis, and human review, provides a critical reality check. For highly hardened software, AI tools currently offer incremental improvements rather than revolutionary breakthroughs in vulnerability discovery. This doesn’t diminish the value of AI in security analysis; Stenberg himself notes these tools are already aiding in finding bugs and that teams not leveraging them leave a larger attack surface open.

This incident highlights the ongoing need for human oversight in security analysis. Defenders should view AI code scanners as valuable assistants, not replacements for expert human review. The calculus for attackers remains the same: target less-audited, less-hardened software where the probability of finding exploitable flaws is significantly higher. CISOs should continue investing in robust traditional security practices alongside exploring and integrating AI tools to augment their defensive capabilities.