The 'Mythos Moment' - prof serious
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The 'Mythos Moment'<br>... a guide to its consequences for security practice and policy
prof serious<br>May 17, 2026
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TL;DR: Recent results show that AI can identify vulnerabilities across critical systems in practice and at scale. This shifts the constraint in cybersecurity from discovery to remediation, as detection begins to outpace the capacity to fix. Most organisations are not yet structured for the consequences, nor for the speed at which this capability is diffusing.
There is something both profoundly exciting and deeply unsettling in seeing so much of what I have spent a professional career researching and practising upended. AI, and specifically the rapid development of Large Language Models (LLMs), has had that effect. Most recently AI agents – which combine a language model with tools, memory and structured reasoning – have started to find vulnerabilities in widely used, well-audited, critical software that decades of automated testing and human review have missed.<br>A flurry of high-profile announcements associated with the limited release of Anthropic’s Claude Mythos Preview has excited attention and concern, at times bordering on panic. The question is how much to make of this. Marketing-induced urgency is not a reliable guide to underlying capability, and moments of heightened attention are best treated as prompts for analysis.<br>@profserious attempts to provide a balanced assessment of the situation for the broader reader and, for those in technical and policy leadership roles, to point to some steps that might reasonably be taken in response to recent developments. There is a useful rule of thumb when evaluating claims about AI and security: if it comes from a vendor, halve it; if it comes from government, double it; if it comes from an academic paper using a synthetic benchmark, hold it pending real-world results. We are increasingly at the point when those real-world results are arriving and thus we can form a grounded view. So, what has actually happened in the last approximately 18 months?<br>What Has Changed<br>Google’s Project Zero team, working with DeepMind, built a system called Big Sleep. In November 2024 it found an exploitable memory corruption bug in SQLite – a database engine embedded in a vast number of devices and so thoroughly tested that new bugs in it are surprising. By July 2025 it had identified a further vulnerability in the same codebase. A startup called AISLE went further. Using frontier models with their own analysis scaffolding, it found 12 zero-day vulnerabilities in the January 2026 OpenSSL release. OpenSSL is the cryptographic library that secures the majority of encrypted internet traffic. The findings included a critical flaw rated 9.8 out of 10 on the standard severity scale, and bugs traceable to 1990s code that had survived years of continuous automated testing. Across 30+ established projects – Linux kernel, Chromium, Firefox, Apache, OpenVPN, Samba – AISLE has reported around 180 externally-validated CVEs (formally registered, independently verified security flaws) since early 2025. Most of these are now patched.<br>DARPA (the Defense Advanced Research Projects Agency) ran a competition, the AI Cyber Challenge (AIxCC), the lineal descendant of DARPA’s 2016 Cyber Grand Challenge. The competition concluded at DEF CON (the security conference) in August 2025. In it 7 AI systems worked autonomously across 54 million lines of code, found the majority of the seeded vulnerabilities, patched most of them, and surfaced 18 previously-unknown bugs that were subsequently disclosed to the relevant maintainers. The winning team took home $4 million.<br>Microsoft’s Security Copilot, applied to bootloader code (the low-level software that initialises a computer prior to the operating system loading) in March 2025, found vulnerabilities across GRUB2 (the bootloader used by most Linux systems), U-Boot and Barebox, including issues that could enable bypass of Secure Boot – the mechanism that prevents unauthorised software from running at startup.<br>What Matters<br>Impressive though these demonstrations are, they have important limitations. If you strip away the scaffolding – the tool integrations, the iterative planning loops, the connections to existing static analysis software – then the raw model performance on benchmarks is considerably less impressive. The best models score in the low 20s on standard accuracy measures for real-world C/C++ vulnerability detection without that scaffolding.<br>What is determinative here is system design or the architecture rather than model capability per se. Achieving these outcomes is thus a function of engineering investment, not exclusive access to frontier models – and such investment is well within the reach of capable adversaries.<br>The Mythos Moment<br>So, to the most publicised event. On 7 April Anthropic announced Claude Mythos Preview and a defensive consortium called Project...