GPT-5.5 Matches Claude Mythos on Advanced Cybersecurity Benchmarks
Key Takeaways
- ▸GPT-5.5 achieves 71.4% pass rate on expert-level cybersecurity tasks, slightly exceeding Claude Mythos Preview (68.6%)
- ▸Multiple frontier models are converging on similar advanced capabilities for reverse engineering, exploit development, and vulnerability research
- ▸AI models can solve multi-step cybersecurity challenges in minutes that would take human experts 10-20 hours to complete
Summary
A new evaluation shows that OpenAI's GPT-5.5 achieves comparable performance to Anthropic's Claude Mythos on advanced cybersecurity tasks, suggesting that frontier AI models are converging on similar capabilities for complex security challenges. The evaluation used a suite of 95 cybersecurity tasks in capture-the-flag (CTF) format, with expert-level challenges requiring sophisticated skills including reverse engineering stripped binaries, exploit development against modern mitigations, cryptographic attacks, and vulnerability research.
On the expert-level tasks, GPT-5.5 achieved a 71.4% pass rate, slightly exceeding Claude Mythos at 68.6%, with substantial improvements over earlier models like GPT-5.4 (52.4%) and Opus 4.7 (48.6%). A standout achievement was GPT-5.5's completion of a complex custom virtual machine reverse-engineering challenge in 10 minutes and 22 seconds—a task that took human cybersecurity experts roughly 12 hours of specialized work using Binary Ninja, gdb, Python, and SMT solvers.
The results indicate that multiple AI developers have now produced models capable of handling sophisticated, multi-step cybersecurity challenges. This convergence suggests advanced cyberattack capabilities are becoming a standard feature of frontier AI systems, raising important implications for both cybersecurity offense and defense.
- Advanced benchmark covers complex domains including binary analysis, cryptographic attacks, heap exploitation, and firmware reverse engineering
Editorial Opinion
The convergence of multiple frontier models on sophisticated cyberattack capabilities marks both a remarkable technical achievement and a sobering inflection point. While rigorous benchmarking is essential for understanding AI security risks and driving defensive improvements, the accelerating pace at which language models acquire advanced cyberattack capabilities demands careful consideration of access controls and deployment safeguards. The ability to autonomously solve complex exploitation challenges in minutes rather than expert-hours should inform policy discussions around AI model distribution and cybersecurity governance.
