A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality

• URL: http://arxiv.org/abs/2408.00435v1
• Date: Thu, 1 Aug 2024 10:14:05 GMT
• Title: A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality
• Authors: M. Mehdi Kholoosi, M. Ali Babar, Roland Croft,
• Abstract summary: ChatGPT is a Large Language Model (LLM) that can perform a variety of tasks with remarkable semantic understanding and accuracy. This study aims to gain an understanding of the potential of ChatGPT as an emerging technology for supporting software security. It was determined that security practitioners view ChatGPT as beneficial for various software security tasks, including vulnerability detection, information retrieval, and penetration testing.
• Score: 1.7624347338410744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) advancements have enabled the development of Large Language Models (LLMs) that can perform a variety of tasks with remarkable semantic understanding and accuracy. ChatGPT is one such LLM that has gained significant attention due to its impressive capabilities for assisting in various knowledge-intensive tasks. Due to the knowledge-intensive nature of engineering secure software, ChatGPT's assistance is expected to be explored for security-related tasks during the development/evolution of software. To gain an understanding of the potential of ChatGPT as an emerging technology for supporting software security, we adopted a two-fold approach. Initially, we performed an empirical study to analyse the perceptions of those who had explored the use of ChatGPT for security tasks and shared their views on Twitter. It was determined that security practitioners view ChatGPT as beneficial for various software security tasks, including vulnerability detection, information retrieval, and penetration testing. Secondly, we designed an experiment aimed at investigating the practicality of this technology when deployed as an oracle in real-world settings. In particular, we focused on vulnerability detection and qualitatively examined ChatGPT outputs for given prompts within this prominent software security task. Based on our analysis, responses from ChatGPT in this task are largely filled with generic security information and may not be appropriate for industry use. To prevent data leakage, we performed this analysis on a vulnerability dataset compiled after the OpenAI data cut-off date from real-world projects covering 40 distinct vulnerability types and 12 programming languages. We assert that the findings from this study would contribute to future research aimed at developing and evaluating LLMs dedicated to software security.

Related papers

• Multimodal Situational Safety [73.63981779844916]
  We present the first evaluation and analysis of a novel safety challenge termed Multimodal Situational Safety. For an MLLM to respond safely, whether through language or action, it often needs to assess the safety implications of a language query within its corresponding visual context. We develop the Multimodal Situational Safety benchmark (MSSBench) to assess the situational safety performance of current MLLMs.
  arXiv  Detail & Related papers  (2024-10-08T16:16:07Z)
• Developers' Perceptions on the Impact of ChatGPT in Software Development: A Survey [13.257222195239375]
  We conducted a survey with 207 software developers to understand the impact of ChatGPT on software quality, productivity, and job satisfaction. The study delves into developers' expectations regarding future adaptations of ChatGPT, concerns about potential job displacement, and perspectives on regulatory interventions.
  arXiv  Detail & Related papers  (2024-05-20T17:31:16Z)
• Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection [9.763041664345105]
  Software poses potential cybersecurity risks due to inherent vulnerabilities. Deep learning has shown promise as an effective tool for this task due to its ability to perform well without extensive feature engineering. Recent research highlights the deep learning efficacy in diverse tasks. This paper investigates the capability of models, specifically a recent language model, to generalize beyond the programming languages used in their training data.
  arXiv  Detail & Related papers  (2024-01-15T04:45:27Z)
• Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
  We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
  arXiv  Detail & Related papers  (2023-11-11T11:01:13Z)
• Evaluating the Impact of ChatGPT on Exercises of a Software Security Course [2.3017018980874617]
  ChatGPT can identify 20 of the 28 vulnerabilities we inserted in the web application in a white-box setting. ChatGPT makes nine satisfactory penetration testing and fixing recommendations for the ten vulnerabilities we want students to fix.
  arXiv  Detail & Related papers  (2023-09-18T18:53:43Z)
• Prompt-Enhanced Software Vulnerability Detection Using ChatGPT [9.35868869848051]
  Large language models (LLMs) like GPT have received considerable attention due to their stunning intelligence. This paper launches a study on the performance of software vulnerability detection using ChatGPT with different prompt designs.
  arXiv  Detail & Related papers  (2023-08-24T10:30:33Z)
• Using Machine Learning To Identify Software Weaknesses From Software Requirement Specifications [49.1574468325115]
  This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
  arXiv  Detail & Related papers  (2023-08-10T13:19:10Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• A Categorical Archive of ChatGPT Failures [47.64219291655723]
  ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study.
  arXiv  Detail & Related papers  (2023-02-06T04:21:59Z)
• Semantic Similarity-Based Clustering of Findings From Security Testing Tools [1.6058099298620423]
  In particular, it is common practice to use automated security testing tools that generate reports after inspecting a software artifact from multiple perspectives. To identify these duplicate findings manually, a security expert has to invest resources like time, effort, and knowledge. In this study, we investigated the potential of applying Natural Language Processing for clustering semantically similar security findings.
  arXiv  Detail & Related papers  (2022-11-20T19:03:19Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.