Mythos vs. The World: Benchmarking AI in Security Bug Hunting

The author developed a benchmark to test if public AI models can match the security bug-hunting capabilities of Anthropic's private Mythos model. Results showed that Mythos remains uniquely capable of finding certain complex bugs, though models like Qwen and DeepSeek are surprisingly competitive for their price. The study suggests that while Mythos holds a current lead, public models are capable of understanding these vulnerabilities if discovery methods are improved.

Key Points

• Mythos successfully identified four complex vulnerabilities that every other tested public model failed to find blindly.
• Chinese models such as Qwen, DeepSeek, and MiMo offer security auditing performance comparable to Western frontier models at a fraction of the cost.
• Strict safety guardrails in models like Mistral and Gemini often hinder legitimate security research by refusing to analyze code for vulnerabilities.
• Model size is not a perfect predictor of success, as smaller variants of Nemotron and Laguna occasionally outperformed their larger counterparts.
• Top-tier public models like Opus can understand the bugs when pointed directly at them, suggesting the primary challenge is the initial discovery phase.

Sentiment

The overall sentiment is cautiously supportive of the article's direction and appreciative of its concrete testing, but not fully convinced by the strongest interpretation. Hacker News generally agrees that AI-assisted security research is becoming practically useful and that guardrails are a real obstacle, while remaining skeptical that this benchmark definitively ranks private and public models.

In Agreement

• Concrete benchmark results are more useful than anecdotal claims about model quality, and the article gives readers evidence to inspect rather than hype to accept.
• AI systems can be powerful vulnerability research assistants, especially for finding flaws in existing software when guided by a skilled operator.
• Guardrails that block legitimate security analysis make some mainstream products less useful for defensive auditing and can distort benchmark outcomes.
• Lower-cost public models showing credible performance supports the article's suggestion that public tooling may narrow the gap with better prompts and agent workflows.
• The author is right to treat harnesses, static analysis tools, and iterative workflows as important next variables rather than assuming raw model choice tells the whole story.

Opposed

• The benchmark does not directly establish Mythos superiority because the comparison corpus comes from Mythos-discovered bugs and the original Mythos workflow is unknown.
• Some users see Fable or related private-model advantages as mostly style, taste, or interface quality rather than a clear jump in technical capability.
• A minimal harness may understate what public frontier models can do, while also making it hard to compare against an internal Anthropic setup that may use richer tooling.
• Cost-limited runs leave uncertainty about expensive frontier models, since incomplete coverage can make their relative performance look worse than it would under a different budget model.
• Finding bugs in flawed existing systems is not the same as building secure systems, and current models still need expert oversight, formal methods, or specialized workflows for high assurance.