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Small models also found the vulnerabilities that Mythos found
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Anthropic Mythos's showcase vulnerabilities were tested on small, inexpensive, open-weight models, revealing similar analysis results. AI cybersecurity capability varies significantly with model size, indicating that the security moat relies on the system architecture rather than the model itself.
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Based on 5 sampled comments from this thread. Analyzed 2026-04-11.
- Praise: Small, inexpensive models demonstrated the ability to detect vulnerabilities effectively, suggesting that advanced AI capabilities can be accessible to a broader audience.
- Praise: The ability to use smaller models for initial vulnerability detection, followed by more powerful models for verification, could streamline the security analysis process and reduce costs.
- Praise: The findings indicate that with proper tooling and context, smaller models can perform competitively in vulnerability detection, which may democratize security analysis.
- Concern: Isolating code for testing significantly alters the use case, raising concerns about the applicability of results from small models to real-world scenarios.
- Concern: Smaller models may struggle with the complexity of large codebases, as they often require contextual hints to identify vulnerabilities effectively.
- Concern: The reliance on harnesses and tooling suggests that the model's inherent capabilities may not be sufficient for autonomous vulnerability discovery.