Training Frontier Models to Out-Think Hackers — Uri Rolls, Arithmetic & Thom Wolf, Hugging Face
Jul 24, 2026 · 17:28
Uri Rolls of Arithmetic and Thom Wolf of Hugging Face argue that frontier models can be trained to out-think hackers, not just pattern-match vulnerabilities, by focusing on logic leaps in access control. Their benchmark, Mask Off, builds blackbox environments from real zero days found by human researchers, testing whether models can chain reconnaissance into exploitation. A live example: a Keycloak check validates admin by name while another checks by ID, so renaming oneself to the admin inherits privilege—GPT-5.5 and Opus probe everything but never make that logical leap. Results are brutal: only one solve at K1, with GPT-5.5 alone succeeding at K5. Rolls and Wolf argue this mirrors ARC-AGI’s challenge—models struggle to build dynamic world models—and that high-quality data and open source models can shift the economics of cyber defense, giving defenders a lasting speed advantage.
