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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 perform the most detailedinvestigation to date on whether LLMs can reliably identify security-relatedbugs. We construct a series of 228 code scenarios and analyze eight of the mostcapable LLMs across eight different investigative dimensions in an automatedframework. Our evaluation shows LLMs provide non-deterministic responses,incorrect and unfaithful reasoning, and perform poorly in real-world scenariosoutside their knowledge cut-off date. Most importantly, our findings revealsignificant non-robustness in even the most advanced models like `PaLM2' and`GPT-4': by merely changing function or variable names, or by the addition oflibrary functions in the source code, these models can yield incorrect answersin 26% and 17% of cases, respectively. These findings demonstrate that furtherLLM advances are needed before LLMs can be used as general purpose securityassistants.

Can Large Language Models Identify And Reason About Security Vulnerabilities? Not Yet