mlsanityData to 14 Oct 2026

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit

arXiv 2604.09998 ↗First posted 11 Apr 2026cs.CR cs.AIcited by 1

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Abstract (arXiv)

Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs. However, there remains a limited empirical understanding of how such tools are used, perceived, and adopted by real-world security practitioners. To address this gap, we conduct a mixed-methods analysis of discussions in cybersecurity-focused forums to learn how a diverse group of practitioners use and perceive modern LLM tools for security operations. More specifically, we analyzed 892 posts between December 2022 and September 2025 from three cybersecurity-focused forums on Reddit, and, using a combination of qualitative coding and statistical analysis, examined how security practitioners discuss LLM tools across three dimensions: (1) their stated tools and use cases, (2) the perceived pros and cons of each tool across a set of critical factors, and (3) their adoption of such tools and the expected impacts on the cybersecurity industry and individual analysts. Overall, our findings reveal nuanced patterns in LLM tools adoption, highlighting independent use of LLMs for low-risk, productivity-oriented tasks, alongside active interest around enterprise-grade, security-focused LLM platforms. Although practitioners report meaningful gains in efficiency and effectiveness in LLM-assisted workflows, persistent issues with reliability, verification overheads, and security risks sharply constrain the autonomy granted to LLM tools. Based on these results, we also provide recommendations for developing and adopting LLM tools to ensure the security of organizations and the safety of cybersecurity practitioners.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 105 references that are in the map. First posted on arXiv 11 Apr 2026.

Builds on

24 of 105 references.

References · 24

  1. 2024A Survey on Large Language Models for Code Generationcited by 658
  2. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  3. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  4. 2024The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinismcited by 116
  5. 2024JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMscited by 95
  6. 2024Non-Determinism of "Deterministic" LLM Settingscited by 92
  7. 2025SAGA: A Security Architecture for Governing AI Agentic Systemscited by 45
  8. 2023Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPTcited by 36
  9. 2024Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concernscited by 28
  10. 2025LLMs in the SOC: An Empirical Study of Human-AI Collaboration in Security Operations Centrescited by 14
  11. 2025CORTEX: Collaborative LLM Agents for High-Stakes Alert Triagecited by 14
  12. 2025IRCopilot: Automated Incident Response with Large Language Modelscited by 13
12 more
  1. 2025CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoningcited by 12
  2. 2025From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMscited by 10
  3. 2025Locus: Agentic Predicate Synthesis for Directed Fuzzingcited by 9
  4. 2025A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomycited by 6
  5. 2020An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Centercited by 5
  6. 2024NL2KQL: From Natural Language to Kusto Querycited by 3
  7. 2025Towards AI-Driven Human-Machine Co-Teaming for Adaptive and Agile Cyber Security Operation Centerscited by 2
  8. 2019Human-AI Co-Learning for Data-Driven AIcited by 1
  9. 2025CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Educationcited by 1
  10. 2025Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCscited by 1
  11. 2025Effective and Efficient Threat Hunting with Small Language Modelscited by 1
  12. 2026Before the Vicious Cycle Starts: Preventing Burnout Across SOC Roles Through Flow-Aligned Designcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.