What happened?
A cybersecurity expert used OpenAI’s o3 reasoning system to find a previously unknown bug in the Linux kernel. The AI model went through about 12,000 lines of source code and checked all the SMB command processing functions. During this analysis, it spotted a security issue that nobody had found before.
The fix
The vulnerability now has an official identifier (CVE-2025-37899) and Linux maintainers have already pushed out a patch to fix it. This shows how AI tools can help find security problems that human reviewers might miss when going through large codebases.
Has anyone else tried using AI models to hunt for bugs in open source projects? I’m curious about the effectiveness of this approach.
We’ve been experimenting with this at work for the past few months, though not specifically with o3. Started with smaller codebases first and the results were mixed - lots of noise to filter through but definitely caught some edge cases our manual reviews overlooked. The key is setting up proper context windows and giving the model specific patterns to look for rather than just throwing code at it. What struck me about this Linux kernel discovery is that 12k lines is actually manageable for current models, but the SMB protocol complexity makes it genuinely impressive. The real question is whether this scales economically for regular security audits or if it’s just good for one-off deep dives on critical infrastructure code.
thats actually pretty impressive for an ai to catch something in 12k lines that humans missed. makes me wonder tho - how many false positives did it flag before finding the real vulnerability? also kinda scary that we’re relying on ai to find our security holes now lol
I’ve been doing static analysis for about eight years now and this development honestly changes the game. Traditional tools like Coverity or SonarQube excel at finding common patterns but struggle with complex logical flaws that require understanding program flow across multiple functions. What makes this o3 discovery significant is that SMB parsing involves intricate state management and buffer handling that automated scanners typically miss. The fact that it could trace through the command processing pipeline and identify a genuine vulnerability suggests these models can perform semantic analysis rather than just pattern matching. My concern though is verification - when an AI flags something as vulnerable, you still need experienced developers to validate the finding and understand the actual exploit path. But for initial discovery phases, this could dramatically expand our ability to audit large codebases that would otherwise take months of manual review.