As a scientist who repeatedly ran into the classifier-based denials: it appears Anthropic’s strategy to make denials more robust, at the cost of many false positives, was to have a separate classifier processing both input and output tokens, at an extremely simple, almost keyword-search level. One weakness of this approach is that it only catches things that use the right keywords: it is in some sense weak exactly where an LLM-based classifier would be stronger.
Work on abstract, closer-to-CS algorithms that used chemistry terminology were blocked immediately, while work directly relevant to chemistry/biology experiments, writing code to process images from a very specific microscopy setup relevant primarily to biological samples, was never blocked at all, because it happened to never use relevant keywords.
That’s consistent with this situation: finding and fixing bugs in the context of looking for bugs perhaps happened to never use words like ‘exploit’ or ‘cybersecurity’.