ChatGPT's o3 Model Found Remote Zeroday in Linux Kernel Code

From the researcher's blog post: (https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-2025-37899-a-remote-zeroday-vulnerability-in-the-linux-kernels-smb-implementation/)

My experiment harness executes this N times (N=100 for this particular experiment) and saves the results. ...

o3 finds the kerberos authentication vulnerability in the benchmark in 8 of the 100 runs. In another 66 of the runs o3 concludes there is no bug present in the code (false negatives), and the remaining 28 reports are false positives.
...
Combining the code for all of the handlers with the connection setup and teardown code, as well as the command handler dispatch routines, ends up at about 12k LoC (~100k input tokens), and as before I ran the experiment 100 times.

o3 finds the kerberos authentication vulnerability in 1 out of 100 runs with this larger number of input tokens, so a clear drop in performance, but it does still find it. More interestingly however, in the output from the other runs I found a report for a similar, but novel, vulnerability that I did not previously know about.

A practical demonstration of "even a stopped clock is right twice a day."

Initially embarking on a manual audit of ksmbd to benchmark o3’s potential, Heelan quickly realized that the model was able to autonomously identify a complex use-after-free vulnerability in the handler for the SMB ‘logoff’ command—an issue Heelan himself had not previously detected.

Uh oh, that means AI will be used to find countless zero-days for hacking purposes.