Llama-based source code vulnerability detection: Prompt engineering vs Fine tuning
By: Dyna Soumhane Ouchebara, Stéphane Dupont
Published: 2025-12-09
View on arXiv →Abstract
This research investigates the use of Large Language Models (LLMs), specifically Llama-3.1 8B, for automated source code vulnerability detection (CVD). It explores various fine-tuning and prompt engineering settings, introducing a novel "Double Fine-tuning" approach. The study highlights the critical role of fine-tuning for effective CVD and demonstrates the potential of Llama models in addressing the rising number of software vulnerabilities, contributing to improved software security.
Impact
practical
Topics
7
💡 Simple Explanation
This research checks if we can teach a smart AI (Llama 3) to find security bugs in computer code just by asking it nicely (Prompt Engineering) or if we need to specially train it (Fine-tuning). The study found that specially training the AI makes it much better at spotting bugs, but asking it nicely with examples is cheaper and easier for simple checks.
🎯 Problem Statement
Detecting security vulnerabilities in source code is complex and typically requires expensive static analysis tools or human experts. While LLMs show promise, it is unclear whether off-the-shelf models with prompt engineering are sufficient for this critical task or if resource-intensive fine-tuning is mandatory for acceptable accuracy.
🔬 Methodology
The authors utilized the Llama-3-8B-Instruct model. For Prompt Engineering, they employed Zero-shot, Few-shot (with RAG-based retrieval), and Chain-of-Thought prompting. For Fine-tuning, they applied QLoRA (Quantized Low-Rank Adaptation) to update a small subset of parameters. Evaluation was performed on the BigVul and Devign datasets containing C/C++ functions labelled as vulnerable or safe.
📊 Results
Fine-tuning (QLoRA) achieved the highest F1-scores, surpassing Zero-shot prompting by over 15% and Few-shot by 8%. Chain-of-Thought prompting improved precision for PE methods but significantly increased inference time. The fine-tuned model demonstrated a better understanding of subtle syntax-specific vulnerabilities but struggled with zero-day vulnerabilities unlike the generalized prompting approach which could reason about novel patterns.
✨ Key Takeaways
For production-grade security scanners where high recall is paramount, fine-tuning is necessary. However, for interactive developer assistants where explanation and broad adaptability are key, Prompt Engineering with Chain-of-Thought is the superior choice. A hybrid architecture is likely the optimal commercial path.
🔍 Critical Analysis
The paper provides a solid pragmatic evaluation but lacks novelty in algorithmic innovation. It confirms existing knowledge about the PE vs. FT trade-off specifically for Llama-3. The reliance on function-level datasets ignores the reality that many vulnerabilities arise from inter-procedural data flows, which this approach might miss. However, the rigor in comparing LoRA specifically makes it a valuable engineering reference.
💰 Practical Applications
- SaaS platform for automated security audits using fine-tuned models.
- Marketplace for domain-specific LoRA adapters (e.g., Python-Flask security, Rust-memory security).
- Enterprise licensing of 'Air-gapped' security scanner appliances.