Since ChatGPT arrived in late 2022, we have been bullish on what large language models could do for reverse engineering and malware analysis. The early models were useful for teaching but too rudimentary for production work; that changed with the advent of reasoning models. OpenAI’s o1-preview, in September 2024, was the first to show the kind of sustained problem-solving the work demands, and within months Sean Heelan had used o3 to find a net-new vulnerability in the Linux kernel. In cybersecurity, though, our understanding of what these models can do remains stovepiped to vulnerability discovery. The frontier labs took on vulnerability discovery deliberately, because that competency keeps agentic code generation from quietly shipping vulnerable code at scale.
OpenAI built Aardvark, since folded into Codex; Google DeepMind announced Big Sleep, available internally to its Project Zero researchers; and Anthropic followed with selective access to Mythos Preview. Concerns that these capabilities could be misused have led the labs to stricter guardrails and ‘know your customer’ style controls that limit access to specific capabilities, or to entire model variants. OpenAI’s Daybreak initiative and its Trusted Access Program opened a dedicated variant, GPT-5. *-cyber-preview, with guardrails relaxed for cybersecurity use cases, while Anthropic’s Glasswing initiative and its Cyber Verification Program provided early access to Mythos Preview and the promise of lesser guardrails respectively. For a short period in mid-June 2026 access to the highest-end flagship models from both providers required some form of U.
S. government clearance. At the time of writing, GPT 5. 6 Sol is widely available, while Mythos 5 still requires clearance and access as a Glasswing partner. The existence of this new class of models left us with an unusual task: benchmarking what these models can actually do on the work defenders care about, and assessing whether they live up to the surrounding hype.