HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

Petr Simecek; Elnaz Babayeva; Jiri Balhar; Michal Bida; Michal Buran; Vaclav Cadek; Luigino Camastra; Tomas Dulka; Michal Janocko; Tomas Klohna; Pavel Kohout; Ondrej Kokes; Adam Krivka; Jakub Kubik; Patrik Mada; Igor Morgenstern; Marek Pavelka; Joshua Rogers; Petr Stastny; Jan Tattermusch; Dmitrijs Trizna; Martin Votruba; Guido Vranken; Jakub Zikl; Evelina Gabasova; Stanislav Fort

arXiv · 2607.27030

HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

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Abstract

LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs across 78 projects, including OpenSSL, curl, and GnuTLS. We introduce HoF-Bench (named after AISLE's public Hall of Fame), a benchmark built from 95 of these public AI-discovered CVEs across eight repositories pinned at vulnerable commits. Analyzers receive source and target-file scope but not CVE identifiers, descriptions, fixes, or expected mechanisms; a detector-blinded frontier-model judge credits only findings that identify the same code path, root cause, attack condition, and impact. A deliberately minimal LLM-based analyzer rediscovers up to 65 of the 95 CVEs (68%) under this strict protocol. No frontier model performs detection anywhere in the study. The ten detector backbones are five open-weight models (21B--284B total parameters, 3--13B active) and five proprietary small or "flash"-tier models. All of them run in the fixed scaffold with four repeated passes, an optional generated-context stage, and a replayable multi-round triage stage (7,600 model--CVE pass records). Difficulty is strongly structured by language; the CVEs missed by every model concentrate in C infrastructure code. HoF-Bench provides a compact test bed for comparing vulnerability scanners, their reliability across repeated runs, and the candidate volume they create. The dataset is available at https://huggingface.co/datasets/aisleinc/HoF-Bench.

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Petr Simecek, Elnaz Babayeva, Jiri Balhar, Michal Bida, Michal Buran, Vaclav Cadek, Luigino Camastra, Tomas Dulka, Michal Janocko, Tomas Klohna, Pavel Kohout, Ondrej Kokes, Adam Krivka, Jakub Kubik, Patrik Mada, Igor Morgenstern, Marek Pavelka, Joshua Rogers, Petr Stastny, Jan Tattermusch, Dmitrijs Trizna, Martin Votruba, Guido Vranken, Jakub Zikl, Evelina Gabasova, Stanislav Fort. 2026-07-29. HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models. https://arxiv.org/abs/2607.27030

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