Which LLM is the best at finding real vulnerabilities?
A recent evaluation tested various LLMs for their ability to identify vulnerabilities in code. The models were assessed based on their accuracy and quality of reporting, with GPT-OSS and Gemma performing particularly well. The results highlighted the strengths and weaknesses of each model, especially regarding precision and the generation of duplicate vulnerabilities.
- ▪Seven LLMs were tested on their ability to find vulnerabilities in a fake banking web application.
- ▪GPT-OSS scored the highest with 19 out of 22 points, identifying 10 of the 13 critical vulnerabilities.
- ▪Gemma, despite having fewer parameters, found 8 critical vulnerabilities and produced a better report than GPT-OSS.
2 outlets in our directory ran this story, first to last over 18 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,613 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Medium |
| Canonical URL | https://medium.com/@lp1/which-llm-is-the-best-at-finding-real-vulnerabilities-part-1-2c51802cd55b |
| Publication time | Fri, 29 May 2026 16:18:51 +0000 |
| Retrieval time | 2026-05-29T16:25:02.263Z |
| Last seen | 2026-05-29T16:25:02.263Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | cNPiT_90mSOG · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Which LLM is the best at finding real vulnerabilities (Part 1)?Jeremie A <lp1>5 min read·1 hour ago--ListenSharePress enter or click to view image in full sizeA few weeks ago, I built a framework that allows me to automatically decompile and apps, binaries and audit code.I used it to find 500 actual vulns on public apps (that I'm not even sure what to do with) and now I'm using this toolset to try and find the most cost-effective LLM to do vulnerability research.I was teaching a class in Paris when I created this exercise https://github.com/lp1dev/Mybank_WebSec_Exercise/ , the assignment is simple: run and audit the application, write a penetration testing report and send it to me!The app has a list of 13 vulnerabilities that must absolutely be reported, they are the ones that should (in…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
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