Authors create LLMCVE dataset of LLM-in-the-loop vulnerabilities and demonstrate that agent-based repair methods achieve low success rates on them, particularly prompt injections at 28.57% Pass@1.
VulnRepairEval: An exploit-based evaluation framework for assessing large language model vulnerability repair capabilities,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A study protocol proposing a balanced crossover experiment to test whether LLM assistance in vulnerability patching accelerates fixes or introduces superficial insecure patches that pass functional but fail security validation.
citing papers explorer
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Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities
Authors create LLMCVE dataset of LLM-in-the-loop vulnerabilities and demonstrate that agent-based repair methods achieve low success rates on them, particularly prompt injections at 28.57% Pass@1.
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Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
A study protocol proposing a balanced crossover experiment to test whether LLM assistance in vulnerability patching accelerates fixes or introduces superficial insecure patches that pass functional but fail security validation.