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Show HN: I benchmarked LLM agents on fixing real-world security vulnerabilities
A quantitative evaluation of LLM agent performance and cost-effectiveness in automated security vulnerability patching, using real-world CVEs and sandboxed environments.
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A quantitative evaluation of LLM agent performance and cost-effectiveness in automated security vulnerability patching, using real-world CVEs and sandboxed environments.
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.
I built a benchmark with 20 real CVEs across 18 Python projects (Pillow, GitPython, yt-dlp, urllib3, etc). I've run it over 5 LLM agents (3 OpenAI, 2 poolside) and 3 different prompts (full advisory, locate, diagnose) with a total of 300 runs. The agents are tasked to fix security vulnerabilities in a sandboxed environment and they are scored against a hidden security tests from the maintainer's own fix.Best solve rate was 50%. On the other 50%, some fixes are sometimes coherent and pass all regression tests, but vulnerability still present.The main differentiator I found between models is cost: gpt-5.5 at 12× more expensive than gpt-5.4-mini while producing statistically similar results. Within-family performance gaps are small, which points out the difference is likely due to model training data. I also did a power analysis and the task count needed to detect a meaningful within-family edge at ~700.Full write-up: https://giovannigatti.github.io/cve-benchCode: https://github.com/GiovanniGatti/cve-bench
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I benchmarked LLM agents on fixing real-world security vulnerabilities is analyzed by our AI as: A quantitative evaluation of LLM agent performance and cost-effectiveness in automated security vulnerability patching, using real-world CVEs and sandboxed environments.. It focuses on This benchmark reveals a 50% success rate for LLM agents in fixing real-world security vulnerabilities, with a critical observation: some fixes pas...
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Data for I benchmarked LLM agents on fixing real-world security vulnerabilities was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was I benchmarked LLM agents on fixing real-world security vulnerabilities publicly launched?
The initial public indexing or launch date for I benchmarked LLM agents on fixing real-world security vulnerabilities within our tracked developer communities was recorded on June 5, 2026.
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I benchmarked LLM agents on fixing real-world security vulnerabilities has achieved measurable traction, logging over 4 traction score and facilitating 3 recorded discussions or engagements.
Which technical categories define I benchmarked LLM agents on fixing real-world security vulnerabilities?
Based on metadata extraction, I benchmarked LLM agents on fixing real-world security vulnerabilities is categorized under topics such as: LLM agents, benchmark, real-world security vulnerabilities, CVEs.
What are some commercial alternatives to I benchmarked LLM agents on fixing real-world security vulnerabilities?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Loom Record for agents, which offers overlapping value propositions.
How does the creator describe I benchmarked LLM agents on fixing real-world security vulnerabilities?
The original author or development team describes the product as follows: "I built a benchmark with 20 real CVEs across 18 Python projects (Pillow, GitPython, yt-dlp, urllib3, etc). I've run it over 5 LLM agents (3 OpenAI, 2 poolside) and 3 different prompts (full advisor..."
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