Understanding Hackers' Work: An Empirical Study of Offensive Security Practitioners
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Abstract (arXiv)
Offensive security-tests are a common way to pro-actively discover potential vulnerabilities. They are performed by specialists, often called penetration-testers or white-hat hackers. The chronic lack of available white-hat hackers prevents sufficient security test coverage of software. Research into automation tries to alleviate this problem by improving the efficiency of security testing. To achieve this, researchers and tool builders need a solid understanding of how hackers work, their assumptions, and pain points. In this paper, we present a first data-driven exploratory qualitative study of twelve security professionals, their work and problems occurring therein. We perform a thematic analysis to gain insights into the execution of security assignments, hackers' thought processes and encountered challenges. This analysis allows us to conclude with recommendations for researchers and tool builders to increase the efficiency of their automation and identify novel areas for research.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 1.
8 papers.
- 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Frameworkcited by 30
- 2023LLMs as Hackers: Autonomous Linux Privilege Escalation Attackscited by 25
- 2025Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Designcited by 13
- 2024Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvementscited by 11
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agentscited by 8
- 2024AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?cited by 7
- 2025Restoring Gaussian Blurred Face Images for Deanonymization Attackscited by 2
- 2026The Ethics of Autonomous AI Agents for Offensive Securitycited by 1
- 2026The Ethics of Autonomous AI Agents for Offensive Security22 Jul 2026
- 2025Restoring Gaussian Blurred Face Images for Deanonymization Attacks14 Jun 2025
- 2025AutoPentest: Enhancing Vulnerability Management With Autonomous LLM Agents15 May 2025
- 2025Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design14 Apr 2025
- 2025VulnBot: Autonomous Penetration Testing for A Multi-Agent Collaborative Framework23 Jan 2025
- 2024AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?2 Nov 2024
- 2024Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements22 Oct 2024
- 2023LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks17 Oct 2023