Making code vulnerability detection faster and smarter by helping LLMs understand code patterns.
CGP-Tuning helps code LLMs better detect software vulnerabilities by introducing a special way to understand code structure and patterns that traditional methods often miss.
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https://arxiv.org/abs/2501.04510
Original Problem 🤔:
→ Current code LLMs struggle with vulnerability detection because they treat code as plain text, missing crucial structural information about how code elements relate to each other.
→ Existing methods either lose code structure details or require too much computational power when handling complex code graphs.
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Solution in this Paper 💡:
→ The paper introduces CGP-Tuning, a structure-aware soft prompt tuning method.
→ It uses type-aware embeddings to capture rich semantic details within code graphs.
→ Implements an efficient cross-modal alignment module that keeps computational costs linear while incorporating graph-text interactions.
→ Leverages innovative pooling techniques to handle long source code sequences effectively.
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Key Insights 🔍:
→ Code structure information is crucial for accurate vulnerability detection
→ Type-aware embeddings significantly improve model's understanding of code semantics
→ Linear computational cost can be achieved without sacrificing performance
→ The method works well even with very long code sequences
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Results 📊:
→ Outperforms current state-of-the-art methods by 3.5 percentage points in accuracy
→ Maintains high performance on long source code samples
→ Achieves linear computational complexity proportional to |V| + N
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