Can Agentic Code Review Catch Vulnerabilities Before They Are Merged?

The paper introduces SecPRBench, a benchmark that evaluates large language model agents on real-world bug‑introducing commits from critical software systems such as Firefox, V8, and OpenSSL. It automates harvesting, building, and validating these commits, and uses an LLM‑as‑a‑judge to match findings with ground‑truth vulnerabilities. Across eight state‑of‑the‑art models, recall ranges from 32.5% to 78.1%, with Grok 4.6 and GPT‑6 sol performing best, while security‑focused harnesses like OpenAI’s Codex Security and Anthropic’s Defending Code influence recall differently.

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• llms
• agents
• benchmarks