# DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection

arXiv: https://arxiv.org/abs/2405.01202 · HTML: https://mlsanity.com/paper/2405.01202/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2024.

## Built on by (most cited first, 13 of 13)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection](https://mlsanity.com/paper/2503.09433/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [How Well Do Large Language Models Serve as End-to-End Secure Code Agents for Python?](https://mlsanity.com/paper/2408.10495/) (2024)
- [How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study](https://mlsanity.com/paper/2412.18989/) (2024)
- [Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review](https://mlsanity.com/paper/2603.18740/) (2026)
- [Adaptive Hierarchical Evaluation of LLMs and SAST tools for CWE Prediction in Python](https://mlsanity.com/paper/2601.01320/) (2026)
- [Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points](https://mlsanity.com/paper/2412.11194/) (2024)
- [C-ing Clearly: Enhanced Binary Code Explanations using C code](https://mlsanity.com/paper/2512.14500/) (2025)
- [From Illusion to Insight: Change-Aware File-Level Software Defect Prediction Using Agentic AI](https://mlsanity.com/paper/2512.23875/) (2025)
- [From Evaluation to Optimisation: Hierarchy-Aware Training Signals for CWE Prediction in Python](https://mlsanity.com/paper/2607.21069/) (2026)

## Abstract (arXiv)

Software vulnerability detection is generally supported by automated static analysis tools, which have recently been reinforced by deep learning (DL) models. However, despite the superior performance of DL-based approaches over rule-based ones in research, applying DL approaches to software vulnerability detection in practice remains a challenge due to the complex structure of source code, the black-box nature of DL, and the domain knowledge required to understand and validate the black-box results for addressing tasks after detection. Conventional DL models are trained by specific projects and, hence, excel in identifying vulnerabilities in these projects but not in others. These models with poor performance in vulnerability detection would impact the downstream tasks such as location and repair. More importantly, these models do not provide explanations for developers to comprehend detection results. In contrast, Large Language Models (LLMs) have made lots of progress in addressing these issues by leveraging prompting techniques. Unfortunately, their performance in identifying vulnerabilities is unsatisfactory. This paper contributes DLAP, a underlineDeep underlineLearning underlineAugmented LLMs underlinePrompting framework that combines the best of both DL models and LLMs to achieve exceptional vulnerability detection performance. Experimental evaluation results confirm that DLAP outperforms state-of-the-art prompting frameworks, including role-based prompts, auxiliary information prompts, chain-of-thought prompts, and in-context learning prompts, as well as fine-turning on multiple metrics.