CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection

• URL: http://arxiv.org/abs/2501.04510v1
• Date: Wed, 08 Jan 2025 13:56:17 GMT
• Title: CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection
• Authors: Ruijun Feng, Hammond Pearce, Pietro Liguori, Yulei Sui,
• Abstract summary: This paper introduces a new code graph-enhanced, structure-aware soft prompt tuning method for vulnerability detection, referred to as CGP-Tuning.<n>It employs innovative type-aware embeddings to capture the rich semantic information within code graphs, along with a novel and efficient cross-modal alignment module.<n> Experimental results demonstrate that CGP-Tuning outperforms the best state-of-the-art method by an average of 3.5 percentage points in accuracy.
• Score: 15.013699967804987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have been proposed as powerful tools for detecting software vulnerabilities, where task-specific fine-tuning is typically employed to provide vulnerability-specific knowledge to the LLMs for this purpose. However, traditional full-parameter fine-tuning is inefficient for modern, complex LLMs, which contain billions of parameters. Soft prompt tuning has been suggested as a more efficient alternative for fine-tuning LLMs in general cases. However, pure soft prompt tuning treats source code as plain text, losing structural information inherent in source code. Meanwhile, graph-enhanced soft prompt tuning methods, which aim to address this issue, are unable to preserve the rich semantic information within code graphs, as they are primarily designed for general graph-related tasks and focus more on adjacency information. They also fail to ensure computational efficiency while accounting for graph-text interactions. This paper, therefore, introduces a new code graph-enhanced, structure-aware soft prompt tuning method for vulnerability detection, referred to as CGP-Tuning. It employs innovative type-aware embeddings to capture the rich semantic information within code graphs, along with a novel and efficient cross-modal alignment module that achieves linear computational cost while incorporating graph-text interactions. The proposed CGP-Tuning is evaluated on the latest DiverseVul dataset and the most recent open-source code LLMs, CodeLlama and CodeGemma. Experimental results demonstrate that CGP-Tuning outperforms the best state-of-the-art method by an average of 3.5 percentage points in accuracy, without compromising its vulnerability detection capabilities for long source code.

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