Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers. In the meantime, Large Language Models (LLMs) assisted tools show potential in vulnerability detection, location, and repair tasks. [Hypothesis:] While LLM-assistance is hypothesized to accelerate patching, it also risks introducing hallucinations or insecure code, leading to a higher likelihood of generating superficial repairs that bypass the standard functionality checks but fail the security validation. [Objective:] We aim to present an empirical experiment, unveiling the capability of LLM-assisted vulnerability patching compared to manua...
| Authors | Giulian Biolo, Michael Tezza, Yuanjun Gong et al. |
|---|---|
| Primary category | cs.SE |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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