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arXiv · 2609.34450

ReproBench: Benchmarking LLM Agents on Reproducing Vulnerability From Scratch

Liang He·Sheng Wu·Haomiao Hao·Hongduo Zhao·Jia Yan·Purui Su
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Abstract

Large language model (LLM) agents are increasingly evaluated on cybersecurity tasks such as vulnerability reproduction, exploitation, and patching. However, existing cybersecurity benchmarks predominantly operate under a post-environment evaluation paradigm, i.e., handing the agent source code, a container, or an executable binary. This setup bypasses the critical environment reconstruction step, leaving a fundamental question for real-world vulnerability analysis: can an agent autonomously reconstruct the required execution environment and reproduce a vulnerability entirely from scratch? To address this gap, we present ReproBench, an evidence-grounded benchmark designed to evaluate agent capabilities in end-to-end vulnerability reproduction starting from solely a CVE identifier. ReproBench decomposes the full reproduction workflow into six distinct phases, and assesses performance on each phase independently using verifiable experimental artifacts: downloaded firmware images, unpacked binaries, granular analysis logs, and validated crash samples, among others. We instantiate ReproBench with 30 real-world IoT firmware vulnerabilities, which serve as ideal test cases for our from-scratch evaluation setting. Our evaluation demonstrates that 45.3% of test runs resort to vulnerability simulation - a prevalent remediation workaround adopted across all evaluated LLM agents - while only 5.3% of CVE-model pairs achieve successful reproduction of real-world vulnerabilities. Despite the low overall success rate, these non-trivial successful cases confirm that state-of-the-art LLM agents already possess the capacity for fully autonomous end-to-end vulnerability reproduction. Concurrently, our in-depth analysis of failed cases identifies core bottlenecks impeding LLM agents throughout the reproduction pipeline, offering actionable insights for subsequent research.

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Liang He, Sheng Wu, Haomiao Hao, Hongduo Zhao, Jia Yan, Purui Su. 2026-09-28. ReproBench: Benchmarking LLM Agents on Reproducing Vulnerability From Scratch. https://arxiv.org/abs/2609.34450

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