Context-Enhanced Vulnerability Detection Based on Large Language Model

• URL: http://arxiv.org/abs/2504.16877v1
• Date: Wed, 23 Apr 2025 16:54:16 GMT
• Title: Context-Enhanced Vulnerability Detection Based on Large Language Model
• Authors: Yixin Yang, Bowen Xu, Xiang Gao, Hailong Sun,
• Abstract summary: We propose a context-enhanced vulnerability detection approach that combines program analysis with large language models.<n>Specifically, we use program analysis to extract contextual information at various levels of abstraction, thereby filtering out irrelevant noise.<n>Our goal is to strike a balance between providing sufficient detail to accurately capture vulnerabilities and minimizing unnecessary complexity.
• Score: 17.922081397554155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vulnerability detection is a critical aspect of software security. Accurate detection is essential to prevent potential security breaches and protect software systems from malicious attacks. Recently, vulnerability detection methods leveraging deep learning and large language models (LLMs) have garnered increasing attention. However, existing approaches often focus on analyzing individual files or functions, which limits their ability to gather sufficient contextual information. Analyzing entire repositories to gather context introduces significant noise and computational overhead. To address these challenges, we propose a context-enhanced vulnerability detection approach that combines program analysis with LLMs. Specifically, we use program analysis to extract contextual information at various levels of abstraction, thereby filtering out irrelevant noise. The abstracted context along with source code are provided to LLM for vulnerability detection. We investigate how different levels of contextual granularity improve LLM-based vulnerability detection performance. Our goal is to strike a balance between providing sufficient detail to accurately capture vulnerabilities and minimizing unnecessary complexity that could hinder model performance. Based on an extensive study using GPT-4, DeepSeek, and CodeLLaMA with various prompting strategies, our key findings includes: (1) incorporating abstracted context significantly enhances vulnerability detection effectiveness; (2) different models benefit from distinct levels of abstraction depending on their code understanding capabilities; and (3) capturing program behavior through program analysis for general LLM-based code analysis tasks can be a direction that requires further attention.

Related papers

• Reasoning with LLMs for Zero-Shot Vulnerability Detection [0.9208007322096533]
  We present textbfVulnSage, a comprehensive evaluation framework and a curated dataset from diverse, large-scale open-source system software projects. The framework supports multi-granular analysis across function, file, and inter-function levels. It employs four diverse zero-shot prompt strategies: Baseline, Chain-of-context, Think, and Think & verify.
  arXiv  Detail & Related papers  (2025-03-22T23:59:17Z)
• LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights [12.424610893030353]
  Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection.<n>This paper provides a detailed survey of LLMs in vulnerability detection.<n>We address challenges such as cross-language vulnerability detection, multimodal data integration, and repository-level analysis.
  arXiv  Detail & Related papers  (2025-02-10T21:33:38Z)
• From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning [71.41062111470414]
  The proposed plug-and-play framework interfaces with any open-vocabulary detector.<n>At its core, our approach combines (i) a symbolic regression mechanism exploring relationship patterns among detected entities.<n>We compared our training-free framework against specialized event recognition systems across diverse application domains.
  arXiv  Detail & Related papers  (2025-02-09T10:30:54Z)
• Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
  Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks. We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction. We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2024-11-01T04:05:59Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
  This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T15:31:26Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses. Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies. We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• Towards Explainable Vulnerability Detection with Large Language Models [17.96542494363619]
  Software vulnerabilities pose significant risks to the security and integrity of software systems.<n>The advent of large language models (LLMs) has introduced transformative potential due to their advanced generative capabilities.<n>In this paper, we propose LLMVulExp, an automated framework designed to specialize LLMs for the dual tasks of vulnerability detection and explanation.
  arXiv  Detail & Related papers  (2024-06-14T04:01:25Z)
• Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models [8.167614500821223]
  We introduce MSIVD, multitask self-instructed fine-tuning for vulnerability detection, inspired by chain-of-thought prompting and LLM self-instruction. Our experiments demonstrate that MSIVD achieves superior performance, outperforming the highest LLM-based vulnerability detector baseline (LineVul) with a F1 score of 0.92 on the BigVul dataset, and 0.48 on the PreciseBugs dataset.
  arXiv  Detail & Related papers  (2024-06-09T19:18:05Z)
• How Far Have We Gone in Vulnerability Detection Using Large Language Models [15.09461331135668]
  We introduce a comprehensive vulnerability benchmark VulBench. This benchmark aggregates high-quality data from a wide range of CTF challenges and real-world applications. We find that several LLMs outperform traditional deep learning approaches in vulnerability detection.
  arXiv  Detail & Related papers  (2023-11-21T08:20:39Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.