Curl and Mythos (anderegg.ca)

In a recent update on the application of AI in security testing, Daniel Stenberg, the creator of the widely-used curl project, shared his experience with Anthropic's Claude Mythos model. While initially overwhelmed by an influx of security reports, Stenberg noted that Mythos only identified one low-impact issue in the curl codebase after filtering out false positives. This incident raises concerns about the uneven access to powerful AI tools, as the curl team was not given direct access to Mythos, relying instead on a third party to conduct the scanning. The significance of this development lies in the implications for the AI/ML community, especially regarding the effectiveness of new models in identifying vulnerabilities. Though Mythos was marketed as a potent tool for detecting dangerous flaws, the report generated primarily highlighted non-security bugs. This suggests that while Mythos may offer some improvements over previous models, its capabilities might be only marginally better. As the AI landscape continues to evolve, the experience underscores the need for equitable access to advanced tools and the ongoing challenges in leveraging AI for security, indicating that the journey to robust AI-assisted security solutions is still unfolding.