How ChatGPT is Solving Vulnerability Management Problem

最近，ChatGPT在代码分析领域引起了极大的关注。之前的研究表明，ChatGPT具有处理基础代码分析任务的能力，例如抽象语法树生成，这表明使用ChatGPT理解代码语法和静态行为的潜力。然而，ChatGPT是否能够完成更复杂的现实世界的漏洞管理任务，例如安全相关性和补丁正确性的预测，尚不清楚，这需要全面理解各个方面，包括代码语法、程序语义和相关手动注释。 在本文中，我们使用包含78,445个样本的大规模数据集，探索ChatGPT在涉及完整漏洞管理流程的6个任务中的能力。对于每个任务，我们将ChatGPT与SOTA方法进行比较，调查不同提示的影响，并探索困难之处。结果表明，在利用ChatGPT协助漏洞管理方面具有很大的潜力。一个值得注意的例子是ChatGPT在生成软件缺陷报告标题等任务方面的熟练程度。此外，我们的研究结果揭示了ChatGPT遇到的困难，并为未来的研究方向提供了启示。例如，直接在提示中提供随机演示示例不能始终保证漏洞管理的良好性能。相反，利用ChatGPT的自我启发方式--从演示示例中提取专业知识并将提取的专业知识整合到提示中是一个有前途的研究方向。此外，ChatGPT可能会误解和误用提示中的信息。因此，有效地引导ChatGPT关注有用的信息而不是无关的内容仍然是一个未解决的问题。

The paper explores whether ChatGPT can complete more complicated real-world vulnerability management tasks, such as the prediction of security relevance and patch correctness, which require an all-encompassing understanding of various aspects, including code syntax, program semantics, and related manual comments.

The paper investigates ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 78,445 samples. The paper compares ChatGPT against SOTA approaches, investigates the impact of different prompts, and explores the difficulties. The paper suggests promising potential in leveraging ChatGPT to assist vulnerability management.

The paper highlights ChatGPT's proficiency in tasks like generating titles for software bug reports. The paper reveals the difficulties encountered by ChatGPT and sheds light on promising future directions, such as leveraging ChatGPT in a self-heuristic way and effectively guiding ChatGPT to focus on helpful information. The paper uses a large-scale dataset containing 78,445 samples and compares ChatGPT against SOTA approaches. The paper does not provide open-source code.

Recent related work includes using machine learning techniques for vulnerability management, such as deep learning models for vulnerability detection and prediction. Some related papers include 'DeepVulDetector: A Deep Learning-Based System for Vulnerability Detection' and 'VulDeePecker: A Deep Learning-Based System for Vulnerability Detection'.