The accelerated discovery of previously unknown software vulnerabilities has been making headlines for months. It’s an issue that has even led the US government to create a vulnerability clearing house named Gold Eagle to coordinate research efforts in vulnerability discovery, mitigation and fixes.
An indication of the broader pressure facing cyber-defenders can be drawn from the sheer number of patches being delivered in Microsoft’s Patch Tuesday through the last four months: 169 CVEs in April, 118 CVEs in May, 571 CVEs overall in June (including 208 direct Microsoft CVEs) and another 622 vulnerabilities in July that included zero-days under active exploitation.
A keynote at Black Hat USA 2026 detailed research by associate professor Yan Shoshitaishvili and his undergraduate students at Arizona State University on the expanding use of AI models for vulnerability discovery. Mr. Shoshitaishvili referred to a Washington Post article from June that stated that Anthropic’s next-generation model Claude Mythos had discovered 479 vulnerabilities in the Linux kernel, which the university team used as a benchmark. Using previous generations of GPT models, meanwhile, the team had discovered ‘just’ around 300 flaws.
The difference was attributed to the use of workflows in Mythos, so the team set about integrating similar workflows into three GPTs, which resulted in the discovery of around 600 vulnerabilities. The team then trained the GPTs using the properties of previously known vulnerabilities and discovered approximately 1,000 vulnerabilities. They hit the barrier of discovering vulnerabilities at such speed that they could not keep pace reporting them; for clarity, reporting means detailed research and proposed fixes, rather than just the issue itself. The scale calls into question the whole process of responsible disclosure, which in the team’s view was already broken as disclosure often creates increased risk.
Patching software in a timely fashion in production environments was already a stress point for many cybersecurity teams. Exponential growth like this could be the breaking point that causes either more unpatched software and greater opportunities for cybercriminals or patching without testing, which, in turn, could cause compatibility issues in many environments.
If I take a logical view of this issue and adopt an optimistic mindset, then it could be that we are heading towards a peak in discovery – and that somewhere over this peak is a meadow of peace and calm with an improved normality. Humans researching vulnerabilities has traditionally been a resource-intensive process, producing a steady stream of discoveries that have been increasing year on year. This is potentially due to there being more researchers, more software and more motivation to discover the vulnerabilities for financial gain through bug bounty programs and such like. Switch from humans to AI, and it’s like a quantum approach to discovery, but note that AI is still in a learning phase: as detailed by the Arizona team, tweaking the model and its workflow potentially uncovers more vulnerabilities.
Then there’s also legacy software. Consider the enormous volume of software written over the past 30 years – no amount of human effort could possibly uncover all the vulnerabilities in the current and back catalogues of software. The scale of AI-assisted discovery, however, could potentially reach the end of the catalogue at some stage, and then new discoveries would only be through improvements to the model being used to unearth the vulnerabilities.
Let’s not forget that new software is being developed all the time, of course. Here, too, of course, logic should suggest that any development team today would use the same available AI capabilities to remove any potential vulnerabilities prior to releasing their software. And as the models improve, the prospect of virtually flaw-free software could become a reality.
If this logic prevails, we may reach the calm and peaceful meadow with very few new vulnerabilities being found. This view could, of course, be just a dream, or my misplaced optimism.