"False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing

• URL: http://arxiv.org/abs/2307.16325v3
• Date: Tue, 18 Jun 2024 19:46:47 GMT
• Title: "False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing
• Authors: Amit Seal Ami, Kevin Moran, Denys Poshyvanyk, Adwait Nadkarni,
• Abstract summary: This paper describes a qualitative study that explores the assumptions, expectations, beliefs, and challenges experienced by developers who use SASTs. We perform in-depth, semi-structured interviews with 20 practitioners who possess a diverse range of software development expertise. We identify $17$ key findings that shed light on developer perceptions and desires related to SASTs.
• Score: 15.403953373155508
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The demand for automated security analysis techniques, such as static analysis based security testing (SAST) tools continues to increase. To develop SASTs that are effectively leveraged by developers for finding vulnerabilities, researchers and tool designers must understand how developers perceive, select, and use SASTs, what they expect from the tools, whether they know of the limitations of the tools, and how they address those limitations. This paper describes a qualitative study that explores the assumptions, expectations, beliefs, and challenges experienced by developers who use SASTs. We perform in-depth, semi-structured interviews with 20 practitioners who possess a diverse range of software development expertise, as well as a variety of unique security, product, and organizational backgrounds. We identify $17$ key findings that shed light on developer perceptions and desires related to SASTs, and also expose gaps in the status quo - challenging long-held beliefs in SAST design priorities. Finally, we provide concrete future directions for researchers and practitioners rooted in an analysis of our findings.

Related papers

• A Comprehensive Study on Static Application Security Testing (SAST) Tools for Android [22.558610938860124]
  VulsTotal is a unified evaluation platform for defining and describing tools' supported vulnerability types. We select 11 free and open-sourced SAST tools from a pool of 97 existing options, adhering to clearly defined criteria. We then unify 67 general/common vulnerability types for Android SAST tools.
  arXiv  Detail & Related papers  (2024-10-28T05:10:22Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Hacking, The Lazy Way: LLM Augmented Pentesting [0.0]
  "LLM Augmented Pentesting" is demonstrated through a tool named "Pentest Copilot" Our research includes a "chain of thought" mechanism to streamline token usage and boost performance. We propose a novel file analysis approach, enabling LLMs to understand files.
  arXiv  Detail & Related papers  (2024-09-14T17:40:35Z)
• The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
  Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies. We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
  arXiv  Detail & Related papers  (2024-09-10T10:12:37Z)
• A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality [1.7624347338410744]
  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.
  arXiv  Detail & Related papers  (2024-08-01T10:14:05Z)
• Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems [45.31340537171788]
  Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. Despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited.
  arXiv  Detail & Related papers  (2024-05-28T20:54:41Z)
• Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We? [14.974832502863526]
  In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static application security testing (SAST) tools have been proposed for detecting vulnerabilities in smart contracts. In this paper, we propose an up-to-date and fine-grained taxonomy that includes 45 unique vulnerability types for smart contracts.
  arXiv  Detail & Related papers  (2024-04-28T13:40:18Z)
• Exploiting Library Vulnerability via Migration Based Automating Test Generation [16.39796265296833]
  In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. Vulnerability exploits, as code snippets provided for reproducing vulnerabilities after disclosure, contain a wealth of vulnerability-related information. This study proposes a new method based on vulnerability exploits, called VESTA, which provides vulnerability exploit tests as the basis for developers to decide whether to update dependencies.
  arXiv  Detail & Related papers  (2023-12-15T06:46:45Z)
• Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models [41.068780235482514]
  This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants. CyberSecEval provides a thorough evaluation of LLMs in two crucial security domains: their propensity to generate insecure code and their level of compliance when asked to assist in cyberattacks.
  arXiv  Detail & Related papers  (2023-12-07T22:07:54Z)
• Large Language Models for Software Engineering: Survey and Open Problems [35.29302720251483]
  This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE) Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE.
  arXiv  Detail & Related papers  (2023-10-05T13:33:26Z)
• 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.