# Vulnerability Handling of AI-Generated Code -- Existing Solutions and Open Challenges

arXiv: https://arxiv.org/abs/2408.08549 · HTML: https://mlsanity.com/paper/2408.08549/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2024.

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

- [How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study](https://mlsanity.com/paper/2412.18989/) (2024)
- [CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs](https://mlsanity.com/paper/2506.11059/) (2025)
- [Revisiting Vul-RAG: Reproducibility and Replicability of RAG-based Vulnerability Detection with Open-Weight Models](https://mlsanity.com/paper/2606.04739/) (2026)

## Abstract (arXiv)

The increasing use of generative Artificial Intelligence (AI) in modern software engineering, particularly Large Language Models (LLMs) for code generation, has transformed professional software development by boosting productivity and automating development processes. This adoption, however, has highlighted a significant issue: the introduction of security vulnerabilities into the code. These vulnerabilities result, e.g., from flaws in the training data that propagate into the generated code, creating challenges in disclosing them. Traditional vulnerability handling processes often involve extensive manual review. Applying such traditional processes to AI-generated code is challenging. AI-generated code may include several vulnerabilities, possibly in slightly different forms as developers might not build on already implemented code but prompt similar tasks. In this work, we explore the current state of LLM-based approaches for vulnerability handling, focusing on approaches for vulnerability detection, localization, and repair. We provide an overview of recent progress in this area and highlight open challenges that must be addressed in order to establish a reliable and scalable vulnerability handling process of AI-generated code.