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

arXiv: https://arxiv.org/abs/2312.12575 · HTML: https://mlsanity.com/paper/2312.12575/

## Key facts

- Cited by 97 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2023.

## Built on by (most cited first, 30 of 97)

- [What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering](https://mlsanity.com/paper/2406.12334/) (2024)
- [SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks](https://mlsanity.com/paper/2506.11791/) (2025)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence](https://mlsanity.com/paper/2406.07599/) (2024)
- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection](https://mlsanity.com/paper/2408.12986/) (2024)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents](https://mlsanity.com/paper/2505.10961/) (2025)
- [Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis](https://mlsanity.com/paper/2502.01853/) (2025)
- [To Err is Machine: Vulnerability Detection Challenges LLM Reasoning](https://mlsanity.com/paper/2403.17218/) (2024)
- [LAMD: Context-driven Android Malware Detection and Classification with LLMs](https://mlsanity.com/paper/2502.13055/) (2025)
- [From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs](https://mlsanity.com/paper/2509.01835/) (2025)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [SECURE: Benchmarking Large Language Models for Cybersecurity](https://mlsanity.com/paper/2405.20441/) (2024)
- [VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models](https://mlsanity.com/paper/2406.07595/) (2024)
- [API-guided Dataset Synthesis to Finetune Large Code Models](https://mlsanity.com/paper/2408.08343/) (2024)
- [VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection](https://mlsanity.com/paper/2512.07533/) (2025)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation](https://mlsanity.com/paper/2501.07849/) (2025)
- [VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching](https://mlsanity.com/paper/2409.10756/) (2024)
- [Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions](https://mlsanity.com/paper/2502.14202/) (2025)
- [Automated Profile Inference with Language Model Agents](https://mlsanity.com/paper/2505.12402/) (2025)
- [SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?](https://mlsanity.com/paper/2605.26548/) (2026)
- [When Developer Aid Becomes Security Debt: A Systematic Analysis of Insecure Behaviors in LLM Coding Agents](https://mlsanity.com/paper/2507.09329/) (2025)
- [VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection](https://mlsanity.com/paper/2509.11523/) (2025)
- [How Well Do Large Language Models Serve as End-to-End Secure Code Agents for Python?](https://mlsanity.com/paper/2408.10495/) (2024)

## Abstract (arXiv)

Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We thus develop SecLLMHolmes, a fully automated evaluation framework that performs the most detailed investigation to date on whether LLMs can reliably identify and reason about security-related bugs. We construct a set of 228 code scenarios and analyze eight of the most capable LLMs across eight different investigative dimensions using our framework. 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 the most advanced models like `PaLM2' and `GPT-4': by merely changing function or variable 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 can be used as general purpose security assistants.