# LLM-CSEC: Empirical Evaluation of Security in C/C++ Code Generated by Large Language Models

arXiv: https://arxiv.org/abs/2511.18966 · HTML: https://mlsanity.com/paper/2511.18966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants](https://mlsanity.com/paper/2208.09727/) (2022)
- [How Secure is Code Generated by ChatGPT?](https://mlsanity.com/paper/2304.09655/) (2023)
- [SALLM: Security Assessment of Generated Code](https://mlsanity.com/paper/2311.00889/) (2023)
- [Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation](https://mlsanity.com/paper/2310.16263/) (2023)
- [Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation](https://mlsanity.com/paper/2409.19182/) (2024)
- [From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper](https://mlsanity.com/paper/2503.07450/) (2025)

## Built on by (most cited first, 1 of 1)

- [Enhancing Reliability in LLM-Based Secure Code Generation](https://mlsanity.com/paper/2605.24300/) (2026)

## Abstract (arXiv)

The security of code generated by large language models (LLMs) is a significant concern, as studies indicate that such code often contains vulnerabilities and lacks essential defensive programming constructs. This work focuses on examining and evaluating the security of LLM-generated code, particularly in the context of C/C++. We categorized known vulnerabilities using the Common Weakness Enumeration (CWE) and, to study their criticality, mapped them to CVEs. We used ten different LLMs for code generation and analyzed the outputs through static analysis. The amount of CWEs present in AI-generated code is concerning. Our findings highlight the need for developers to be cautious when using LLM-generated code. This study provides valuable insights to advance automated code generation and encourage further research in this domain.