# Harnessing the Power of LLMs in Source Code Vulnerability Detection

arXiv: https://arxiv.org/abs/2408.03489 · HTML: https://mlsanity.com/paper/2408.03489/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://mlsanity.com/paper/2405.17238/) (2024)
- [SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities](https://mlsanity.com/paper/1807.06756/) (2018)
- [VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector](https://mlsanity.com/paper/2001.02350/) (2020)
- [LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics](https://mlsanity.com/paper/1901.11479/) (2019)
- [On using distributed representations of source code for the detection of C security vulnerabilities](https://mlsanity.com/paper/2106.01367/) (2021)
- [Automated Performance Testing Based on Active Deep Learning](https://mlsanity.com/paper/2104.02102/) (2021)
- [A Hierarchical Deep Neural Network for Detecting Lines of Codes with Vulnerabilities](https://mlsanity.com/paper/2211.08517/) (2022)

## Built on by (most cited first, 6 of 6)

- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Recent Advances in Attack and Defense Approaches of Large Language Models](https://mlsanity.com/paper/2409.03274/) (2024)
- [Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimization](https://mlsanity.com/paper/2507.03051/) (2025)
- [Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection](https://mlsanity.com/paper/2505.05600/) (2025)
- [A systematic literature Review for Transformer-based Software Vulnerability detection](https://mlsanity.com/paper/2604.24822/) (2026)

## Abstract (arXiv)

Software vulnerabilities, caused by unintentional flaws in source code, are a primary root cause of cyberattacks. Static analysis of source code has been widely used to detect these unintentional defects introduced by software developers. Large Language Models (LLMs) have demonstrated human-like conversational abilities due to their capacity to capture complex patterns in sequential data, such as natural languages. In this paper, we harness LLMs' capabilities to analyze source code and detect known vulnerabilities. To ensure the proposed vulnerability detection method is universal across multiple programming languages, we convert source code to LLVM IR and train LLMs on these intermediate representations. We conduct extensive experiments on various LLM architectures and compare their accuracy. Our comprehensive experiments on real-world and synthetic codes from NVD and SARD demonstrate high accuracy in identifying source code vulnerabilities.