Enhancing Software Vulnerability Detection Using Prompt-Engineered ChatGPT

Conceitos Básicos

Prompt engineering can significantly improve the performance of ChatGPT in detecting software vulnerabilities by incorporating structural and sequential auxiliary information of source code.

Resumo

This paper presents a study on using prompt-engineered ChatGPT for software vulnerability detection. The key highlights are:

The authors complement previous work by applying various improvements to the basic prompt and investigating the vulnerability detection capabilities of ChatGPT on collected vulnerability datasets covering two programming languages (Java and C/C++).

They incorporate structural and sequential auxiliary information of the source code, such as API call sequences and data flow graphs, into the prompt design. This is shown to be helpful for ChatGPT to detect vulnerabilities better.

The authors leverage ChatGPT's ability to memorize multi-round dialogue through chain-of-thought prompting, leading to further improvements in the performance of vulnerability detection.

Extensive experiments on two vulnerability datasets demonstrate the effectiveness of prompt-enhanced vulnerability detection using ChatGPT. The authors also analyze the merits and demerits of using ChatGPT for vulnerability detection.

The results show that prompt engineering can significantly boost the performance of ChatGPT in vulnerability detection, outperforming traditional rule-based and machine learning-based baselines. Incorporating auxiliary information and leveraging chain-of-thought prompting are key to achieving these improvements.

Estatísticas

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Citações

"Prompt engineering modifies the original user input using a textual prompt, and the resulting text has some unfilled slots. Then, LLMs are asked to fill the unfilled information which is actually the answer that the user requires."
"We leverage ChatGPT's ability of memorizing multi-round dialogue to design suitable prompts for vulnerability detection."

Principais Insights Extraídos De

Prompt-Enhanced Software Vulnerability Detection Using ChatGPT

by Chenyuan Zha... às arxiv.org 04-15-2024

https://arxiv.org/pdf/2308.12697.pdf

Perguntas Mais Profundas

How can the prompt-enhanced vulnerability detection approach be extended to other types of software defects beyond security vulnerabilities?

To extend the prompt-enhanced vulnerability detection approach to other types of software defects beyond security vulnerabilities, the prompt design can be tailored to address specific defect types. Here are some ways to achieve this:

Customized Prompts: Develop prompts that are specific to different types of software defects, such as performance issues, logical errors, or code quality issues. By crafting prompts that target the characteristics of each defect type, ChatGPT can be guided to focus on relevant aspects during the detection process.

Incorporating Domain Knowledge: Include domain-specific information in the prompts to guide ChatGPT towards understanding and detecting various software defects. This could involve providing context about common patterns or indicators of specific defect types.

Utilizing Different Auxiliary Information: Besides API calls and data flow information, incorporate other types of auxiliary information relevant to different defect types. For example, for performance issues, include details about resource usage or time complexity in the prompts.

Chain-of-Thought Prompting: Implement a chain-of-thought approach where ChatGPT first summarizes the code to identify the potential defect type and then proceeds to detect the specific defect. This method can help in identifying a broader range of software defects.

By adapting the prompt design and incorporating diverse auxiliary information, the prompt-enhanced vulnerability detection approach can be extended to effectively detect various types of software defects beyond security vulnerabilities.

What are the potential limitations and drawbacks of relying on ChatGPT for vulnerability detection compared to traditional rule-based or machine learning-based methods?

While ChatGPT offers promising capabilities for vulnerability detection, there are several limitations and drawbacks compared to traditional rule-based or machine learning-based methods:

Interpretability: ChatGPT's decisions are based on complex language models, making it challenging to interpret how it arrives at a particular detection result. In contrast, rule-based methods provide transparent rules for vulnerability detection.

Training Data Dependency: ChatGPT's performance heavily relies on the quality and diversity of the training data. If the training data is biased or lacks representation of certain vulnerabilities, ChatGPT may struggle to detect them accurately.

Limited Context Understanding: ChatGPT may not fully grasp the intricate technical details and context of software vulnerabilities, especially in complex scenarios where deep domain knowledge is required.

Scalability: ChatGPT's computational requirements and inference time may hinder its scalability for large-scale vulnerability detection tasks compared to more optimized machine learning models.

False Positives and Negatives: ChatGPT may produce false positives or false negatives in vulnerability detection, leading to inaccuracies in the results. Traditional methods may offer more control over minimizing such errors.

Adaptability to New Vulnerabilities: ChatGPT may struggle to adapt quickly to newly emerging vulnerabilities without extensive retraining, unlike machine learning models that can be updated with new data more easily.

While ChatGPT has shown promise in various natural language processing tasks, its application in vulnerability detection comes with certain limitations that need to be considered when compared to traditional rule-based or machine learning-based methods.

How can the prompt design be further improved to better capture the semantic and structural information of the source code to enhance the vulnerability detection performance?

To enhance the vulnerability detection performance by improving the prompt design to capture semantic and structural information more effectively, the following strategies can be implemented:

Semantic Prompting: Develop prompts that explicitly guide ChatGPT to focus on specific semantic aspects of the code related to vulnerabilities. This can involve providing context about common vulnerability patterns or indicators in the prompt.

Structural Information Inclusion: Incorporate detailed structural information, such as control flow graphs, data flow graphs, or program dependence graphs, directly into the prompt. This can help ChatGPT better understand the code's architecture and relationships.

Multi-Step Prompting: Implement a multi-step prompting approach where ChatGPT is guided through a series of prompts that gradually delve deeper into the semantic and structural aspects of the code. This can enable a more comprehensive analysis of vulnerabilities.

Domain-Specific Prompts: Tailor prompts to include domain-specific terminology and concepts relevant to software vulnerabilities. By using domain-specific language, ChatGPT can better grasp the nuances of vulnerability detection.

Feedback Loop Integration: Incorporate a feedback loop mechanism where the results of previous detections are used to refine and improve subsequent prompts. This iterative process can help ChatGPT learn from its detection outcomes and enhance its performance over time.

Contextual Information Incorporation: Include contextual information about the software environment, libraries used, and potential threat vectors in the prompts. This contextual information can aid ChatGPT in understanding the broader context of vulnerabilities.

By implementing these strategies and continuously refining the prompt design, the semantic and structural information of the source code can be better captured, leading to improved vulnerability detection performance using ChatGPT.