A security researcher details how he discovered a zero-day vulnerability in the Linux kernel's SMB implementation by analyzing the code using OpenAI's o3 API
Now finding a Linux kernel-level zero day is as simple as knowing how to prompt. sean.heelan.io/2025/05/22/h... @davidcrespo : key detail in this very good post about finding a novel vuln with LLMs: even the best-performing model only found the bug in 8 of 100 tries. finding the signal in the noise is the problem for now. it's boring, it requires things like running it 100 times — sean.heelan.io/2025/05/22/h... [images] Mastodon: Lukasz Olejnik / @LukaszOlejnik@mastodon.social : LLMs used to find a remote kernel zeroday with nothing but raw code. The o3 identified a known vulnerability in 8 out of 100 runs—8 true positives, 28 false positives, 66 false negatives; Claude Sonnet 3.7 (3 true positives, 25 false positives, 72 false negatives). o3 identified a previously unknown CVE during those runs. https://sean.heelan.io/... Miguel Afonso Caetano / @remixtures@tldr.nettime.org : “My experiment harness executes this N times (N=100 for this particular experiment) and saves the results. It's worth noting, if you rerun this you may not get identical results to me as between running the original experiment and writing this blog post I had removed the file containing the code to be analysed, and had to regenerate it. … Simon Willison / @simon@fedi.simonwillison.net : Excited to see my LLM CLI tool used by Sean Heelan to help identify a remote zeroday vulnerability in the Linux kernel! — https://simonwillison.net/... X: @antirez : If you read about O3 finding a SMB bug in the Linux Kernel, I did a few tests and I what I suspected looks true: Gemini 2.5 PRO can more easily identify the vulnerability. My success rate is so high that running the following prompt a few times is enough: https://gist.github.com/... Sean Heelan / @seanhn : Interesting. Last year I ran an experiment comparing the latest models at the time (Sonnet 3.5, GPT-4o and Gemini 1.5). The task was to simulate a fuzzer by analysing C code and then generating inputs that hit both sides of each branch. Gemini 1.5 was the best back then as well. Sean Heelan / @seanhn : For the larger context size when I was sending all of smb2pdu.c the usage was approx 100k input tokens, 4k output tokens (3k reasoning, 1k completion), which at current o3 pricing is $1.16. So the full 100 runs was costing $116. Sean Heelan / @seanhn : I wrote-up how I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel's SMB implementation. Link to the blog post below 👇 Gerard / @gsnchez : “Understanding the vulnerability requires reasoning about concurrent connections to the server, and how they may share various objects in specific circumstances. o3 was able to comprehend this and spot a location where a particular object that is not referenced counted is freed while still being accessible by another thread.” Greg Brockman / @gdb : o3 for finding a security vulnerability in the Linux kernel: https://sean.heelan.io/... Daniel Cuthbert / @dcuthbert : I've been lamenting the fact that we don't see good blog posts or written articles anymore, with everyone favouring to submit to a conference instead. However, Sean proved me wrong with a brilliant blogpost on how he found a smb vuln using o3 https://sean.heelan.io/... Mark Chen / @markchen90 : Reasoning models like o3 are starting to aid in deep technical work and meaningful scientific discovery. Results like this will become increasingly common over the next year: Deedy / @deedydas : It's happening. AI models are finding zero-day vulnerabilities. This blog post is a must-read and marks a new era for cybersecurity. [image] LinkedIn: Jean-Michel V. : It's happening. AI models are finding zero-day vulnerabilities. — Will Offense or Defense benefit from AI the most? — https://lnkd.in/evR3kQBv Assaf Amitay : Just witnessed history being made in cybersecurity. — Security researcher Sean Heelan just demonstrated how OpenAI's o3 model found CVE-2025-37899 … Dragos S. : There's a significant amount of value in this blog post—starting with the technical aspects, which are well-structured and clearly explained, up to the playbook followed to achieve the final outcome. … Matthew Hackner : This is a “good news/bad news” story: “good” in the sense that LLMs can be leveraged by security researchers as a tool to uncover and possibly remedy critical vulns … Forums: Hacker News : I used o3 to find a remote zeroday in the Linux SMB implementation r/singularity : o3 for finding a security vulnerability in the Linux kernel Lobsters : How I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel's SMB implementation
Context & Ripple Effects
OpenAI positioned o3 as a reasoning-oriented model ahead of its release; this experiment supplies a concrete software-security test for that capability, rather than a benchmark result. It also arrives as LLM-connected developer tooling is drawing scrutiny after a GitHub MCP server exploit that exposed private user information.
The result is notable less as push-button vulnerability discovery than as evidence that repeated model runs plus human triage can surface a kernel flaw. That operational burden echoes maintainers' earlier complaints about AI-assisted bug-report noise flooding open-source projects.
First-order effects
- Linux SMB maintainers and downstream users must treat CVE-2025-37899 as a remote kernel vulnerability requiring remediation, while the research provides a reproducible workflow for using o3 in code review.
- The experiment makes clear that o3 is not a reliable one-shot detector: it found the known issue in 8 of 100 runs, alongside 28 false positives and 66 false negatives, shifting substantial work to validation and deduplication.
Second-order effects
- Security teams adopting model-assisted source review will need evaluation pipelines that test findings, prioritize credible reports, and contain sensitive code and prompts—not simply broader API access.
- Model providers and competing coding models will be compared on repeatable vulnerability-detection precision and recall, while open-source maintainers may face more AI-generated reports unless submitters supply independently verified evidence.
Third-order effects
- If repeated, human-validated model scans become routine, vulnerability research may shift from scarce manual code auditing toward scalable search-and-triage operations—a dual-use capability that benefits defenders and lowers discovery costs for attackers.
- The key security boundary may increasingly be the controls around powerful model access, logging, and agent integrations, rather than source-code availability alone; the reported error rates show that this transition remains constrained by verification capacity.
The trend: This is one data point in the rise of dual-use code intelligence, where frontier models expand both defensive auditing and offensive vulnerability discovery while humans remain responsible for judging the output.