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Large Language Models (LLMs) have been suggested for use in automatedvulnerability repair, but benchmarks showing they can consistently identifysecurity-related bugs are lacking. We thus develop SecLLMHolmes, a fullyautomated evaluation framework that performs the most detailed investigation todate on whether LLMs can reliably identify and reason about security-relatedbugs. We construct a set of 228 code scenarios and analyze eight of the mostcapable LLMs across eight different investigative dimensions using ourframework. Our evaluation shows LLMs provide non-deterministic responses,incorrect and unfaithful reasoning, and perform poorly in real-world scenarios.Most importantly, our findings reveal significant non-robustness in even themost advanced models like `PaLM2' and `GPT-4': by merely changing function orvariable names, or by the addition of library functions in the source code,these models can yield incorrect answers in 26% and 17% of cases, respectively.These findings demonstrate that further LLM advances are needed before LLMs canbe used as general purpose security assistants.

LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?): A Comprehensive Evaluation, Framework, and Benchmarks