# "You still have to study" -- On the Security of LLM generated code

arXiv: https://arxiv.org/abs/2408.07106 · HTML: https://mlsanity.com/paper/2408.07106/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [How Secure is Code Generated by ChatGPT?](https://mlsanity.com/paper/2304.09655/) (2023)
- [Automated Unit Test Improvement using Large Language Models at Meta](https://mlsanity.com/paper/2402.09171/) (2024)
- [A Deep Dive into Large Language Models for Automated Bug Localization and Repair](https://mlsanity.com/paper/2404.11595/) (2024)

## Built on by (most cited first, 3 of 3)

- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval](https://mlsanity.com/paper/2508.00546/) (2025)
- [Enhancing Reliability in LLM-Based Secure Code Generation](https://mlsanity.com/paper/2605.24300/) (2026)

## Abstract (arXiv)

We witness an increasing usage of AI-assistants even for routine (classroom) programming tasks. However, the code generated on basis of a so called "prompt" by the programmer does not always meet accepted security standards. On the one hand, this may be due to lack of best-practice examples in the training data. On the other hand, the actual quality of the programmers prompt appears to influence whether generated code contains weaknesses or not. In this paper we analyse 4 major LLMs with respect to the security of generated code. We do this on basis of a case study for the Python and Javascript language, using the MITRE CWE catalogue as the guiding security definition. Our results show that using different prompting techniques, some LLMs initially generate 65% code which is deemed insecure by a trained security engineer. On the other hand almost all analysed LLMs will eventually generate code being close to 100% secure with increasing manual guidance of a skilled engineer.