Reddit Tests the Claim That Mythos-Style Security Work Needs Frontier Models

What happened

A r/LocalLLaMA post with 587 upvotes and 120 comments pushed readers toward AISLE's AI Cybersecurity After Mythos: The Jagged Frontier. The article argues that some of the vulnerability analysis showcased in Anthropic's Mythos and Project Glasswing announcement can be reproduced by much smaller and cheaper models, including open-weights systems. From that, AISLE makes a bigger claim: the moat in AI cybersecurity may live more in the system, the scaffold, and the human security expertise around the model than in one frontier model alone.

The evidence presented is specific enough to matter. AISLE says eight of eight tested models detected Anthropic's headline FreeBSD NFS bug once the relevant function was isolated. It also reports that smaller or cheaper models recovered key parts of the reasoning behind the older OpenBSD SACK vulnerability, while some tasks showed almost inverse scaling behavior. On a false-positive discrimination test based on an OWASP snippet, several smaller models reportedly outperformed more expensive frontier systems. That is why the article describes AI cybersecurity capability as "jagged" rather than smoothly scaling with model size.

Why Reddit argued over it

The most important Reddit response did not deny the experiments. It challenged the setup. One of the top comments bluntly noted that the hard part is finding the vulnerable code in the first place. That captures the real split in interpretation: reasoning over a pre-isolated function is not the same thing as autonomously navigating a large repository, identifying the right code path, validating exploitability, and turning the result into a patch or exploit chain. Another highly upvoted reply questioned model selection, arguing that the comparison set may have favored older baselines in some cases.

Even with that pushback, the thread mattered because it shifted the conversation away from a simple "which lab has the smartest model" framing. Security work is modular. Broad scanning, vulnerability detection, false-positive triage, patch generation, and exploit construction do not scale the same way. If smaller models are already adequate for part of that pipeline, then the competitive edge starts moving toward orchestration, cost structure, tooling, and evaluation design.

For Insights readers, that is the useful takeaway. The thread does not prove that frontier models are irrelevant, and it does not show that small models can replace end-to-end autonomous security systems. It does suggest that the economics and architecture of AI security products may be more open than the Mythos launch narrative implied. Original sources: r/LocalLLaMA and AISLE blog.