Project Glasswing: what Mythos showed us
Curated from Cloudflare Blog
Cloudflare's recent exploration with Mythos and similar security-focused LLMs offers a timely and practical look into how these tools can be applied in real-world infrastructure scenarios. For SREs and DevOps engineers, this kind of experimentation is crucial as we increasingly look to AI to augment our workflows, especially in security and code analysis. The article doesn't just highlight findings—it shows the messy, iterative process of trying to apply cutting-edge models to operational code. It's a candid look at what works, what doesn't, and what's required to make it work at scale. The takeaway is clear: when evaluating AI tools for infrastructure tasks, prioritize practical testing and understand their limitations before integration.
In recent weeks, we pointed Mythos and other security-focused LLMs at live code across critical parts of our infrastructure. We share what we observed, the models’ strengths and weaknesses, and what the work around them needs to look like before any of it can scale.
— Cloudflare Blog