Hacker News debates whether small open models can already reproduce parts of Mythos-style AI security work

The Hacker News thread on AISLE's post-Mythos analysis focused on a sharper question: how much of today's AI security work actually requires a frontier model. AISLE does not dismiss Anthropic's Mythos program. Instead, it argues that some of the headline security reasoning can already be reproduced by much smaller and cheaper open-weight models when the vulnerable code has been isolated and the task is framed tightly.

The evidence in the post is specific. AISLE says eight out of eight tested models detected Mythos's flagship FreeBSD exploit in a scoped review, including a 3.6B-active model that identified the overflow and rated it as critical. A 5.1B-active open model reportedly recovered the core reasoning behind an old OpenBSD bug. From there AISLE makes a broader claim: cyber capability is jagged, and no single model family is best across every security task.

• AISLE says all eight tested models found the scoped FreeBSD exploit.
• The article argues that small open models may already be useful for targeted security reasoning.
• The HN dispute centered on whether scoped context is comparable to full codebase discovery.

HN commenters were much less willing to generalize. Several top replies argued that giving a model the vulnerable function strips away the hardest part of vulnerability research, which is finding the relevant code path inside a large codebase and establishing exploitability under realistic constraints. Others pushed back that modern agent scaffolds already narrow attention file by file, so scoped context is not an artificial edge case but part of how real systems operate.

The practical takeaway is balanced. Small models may already be strong enough for targeted triage, exploit review, or second-pass analysis, which matters for cost-sensitive security pipelines. But the thread stopped well short of concluding that small open models can replace frontier systems in fully autonomous discovery loops. HN's underlying message was that methodology matters at least as much as model size.