Insight for: Show HN: I benchmarked LLM agents on fixing real-world security vulnerabilities

A benchmark for LLM agents fixing real-world security vulnerabilities (CVEs).

Analyzed: Jun 5, 2026

This benchmark reveals a 50% success rate for LLM agents in fixing real-world security vulnerabilities, with a critical observation: some fixes pass regression tests but fail to resolve the underlying vulnerability. This highlights a significant trust gap for enterprise adoption in security-critical domains. The primary differentiator among models is cost, not performance, with cheaper models yielding statistically similar results to more expensive counterparts. This implies that for specific, well-defined tasks like vulnerability patching, cost-efficiency should drive model selection. The market trend indicates a nascent but unreliable capability for autonomous security remediation. Enterprises must implement robust verification layers and human oversight, as current agent performance is insufficient for unassisted deployment in production security workflows.

LLM agents benchmark real-world security vulnerabilities CVEs Python projects OpenAI poolside prompts full advisory locate diagnose sandboxed environment security tests regression tests solve rate cost gpt-5.5 gpt-5.4-mini statistically similar results within-family performance gaps model training data power analysis task count

Hacker News Post

Parent Entity

Show HN: I benchmarked LLM agents on fixing real-world security vulnerabilities

Score: 4