A Deep Learning Augmented Large Language Model Prompting Framework for Effective Software Vulnerability Detection

核心概念

DLAP, a Deep Learning Augmented Large Language Model Prompting framework, combines the strengths of deep learning models and large language models to achieve exceptional performance in software vulnerability detection.

要約

The paper proposes DLAP, a Deep Learning Augmented Large Language Model Prompting framework, to address the limitations of existing approaches for software vulnerability detection.

Key highlights:

• DLAP leverages the advantages of both deep learning (DL) models and large language models (LLMs) to achieve superior vulnerability detection performance.
• DLAP uses two prompt techniques:
  1. In-Context Learning (ICL) prompts that incorporate detection probabilities from a pre-trained DL model to stimulate implicit fine-tuning of LLMs.
  2. Chain-of-Thought (COT) prompts that combine results from static analysis tools and DL models to generate customized prompts for LLMs.
• Experiments on four large-scale software projects show that DLAP outperforms state-of-the-art prompting frameworks and fine-tuning techniques in terms of detection accuracy, cost-effectiveness, and explainability.
• The paper conducts a rigorous analysis to determine the most suitable DL model to integrate with DLAP, finding that the Linevul model achieves the best performance.

Overall, DLAP demonstrates the effectiveness of combining DL and LLMs through prompt engineering to address the challenges of software vulnerability detection.

統計

"Software vulnerability detection is paramount for safeguarding system security and individual privacy."
"Many automated static analysis tools (ASATs) have been applied for vulnerability detection."
"DL models that perform well on experimental datasets may suffer from severe performance degradation in real-world projects."
"LLMs have not achieved satisfactory results in vulnerability detection."

引用

"DLAP, a Deep Learning Augmented Large Language Model Prompting framework, combines the advantages of DL models and LLMs while overcoming their respective shortcomings."
"Experiments on four large-scale software projects show that DLAP outperforms state-of-the-art prompting frameworks and fine-tuning techniques in terms of detection accuracy, cost-effectiveness, and explainability."
"The paper conducts a rigorous analysis to determine the most suitable DL model to integrate with DLAP, finding that the Linevul model achieves the best performance."

抽出されたキーインサイト

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

by Yanjing Yang... 場所 arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01202.pdf

深掘り質問

How can DLAP be extended to other software engineering tasks beyond vulnerability detection?

DLAP can be extended to other software engineering tasks by adapting the prompting framework to suit the specific requirements of different tasks. Here are some ways in which DLAP can be extended:

Task-specific Prompt Engineering: Develop task-specific prompts tailored to the unique characteristics of different software engineering tasks. By customizing prompts to address the specific needs of tasks such as code review, code refactoring, or software maintenance, DLAP can enhance the performance of LLMs in a variety of software engineering domains.
Integration with Domain-Specific Knowledge: Incorporate domain-specific knowledge and expertise into the prompting framework. By leveraging domain-specific information, DLAP can provide more contextually relevant prompts for different software engineering tasks, improving the accuracy and effectiveness of LLMs.
Multi-Task Learning: Explore the potential of multi-task learning, where DLAP is trained on multiple software engineering tasks simultaneously. This approach can help the framework learn common patterns and features across different tasks, leading to better generalization and performance.
Continuous Learning and Adaptation: Implement mechanisms for continuous learning and adaptation, allowing DLAP to dynamically adjust its prompting strategies based on feedback and new data. This adaptive approach can ensure that the framework remains effective across a range of software engineering tasks.

What are the potential limitations of the DLAP framework, and how can they be addressed in future research?

While DLAP shows promising results in software vulnerability detection, there are some potential limitations that need to be addressed in future research:

Generalization to New Domains: DLAP may face challenges when applied to new and diverse software engineering domains with different characteristics and requirements. Future research could focus on enhancing the adaptability and generalization capabilities of DLAP to ensure its effectiveness across a wide range of domains.
Interpretability and Explainability: The black-box nature of DL models and LLMs used in DLAP may limit the interpretability and explainability of detection results. Future research could explore methods to improve the transparency and interpretability of the framework, enabling developers to better understand and trust the detection outcomes.
Scalability and Efficiency: As the complexity and size of software projects increase, DLAP may face scalability and efficiency challenges. Future research could investigate techniques to optimize the framework for large-scale projects, ensuring timely and resource-efficient vulnerability detection.
Data Imbalance and Bias: Imbalanced datasets and biases in training data can impact the performance of DLAP. Future research could focus on developing strategies to address data imbalance and mitigate biases, enhancing the robustness and reliability of the framework.

How can the insights from this work on combining DL and LLMs be applied to other domains beyond software engineering?

The insights from combining DL and LLMs in DLAP can be applied to various domains beyond software engineering by:

Natural Language Processing: Leveraging the prompting techniques and fine-tuning strategies from DLAP can enhance the performance of LLMs in natural language processing tasks such as text generation, sentiment analysis, and language translation.
Healthcare: Applying the framework to healthcare domains can improve the accuracy of medical diagnosis, patient monitoring, and treatment recommendation systems by integrating domain-specific knowledge and data into the prompting process.
Finance: Utilizing DLAP in financial domains can enhance fraud detection, risk assessment, and investment analysis by developing task-specific prompts and fine-tuning strategies tailored to financial data and regulations.
Marketing and Customer Service: Implementing DLAP in marketing and customer service domains can improve customer sentiment analysis, personalized recommendations, and chatbot interactions by providing contextually relevant prompts and explanations for decision-making processes.