UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
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A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.
citing papers explorer
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UntrustVul: An Automated Approach for Identifying Untrustworthy Alerts in Vulnerability Detection Models
UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
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From Helpful to Trustworthy: LLM Agents for Pair Programming
A research proposal for three studies on multi-agent LLM pair programming that externalizes intent and uses automated validation to increase trustworthiness.