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[2602.14106] Anticipating Adversary Behavior in DevSecOps Scenarios through Large Language Models
Summary
This paper explores the integration of Large Language Models (LLMs) in anticipating adversary behavior within DevSecOps environments, proposing a proactive security strategy through automated attack defense trees.
Why It Matters
As cyber threats become increasingly sophisticated, traditional security measures are insufficient. This research highlights the importance of AI-driven strategies in enhancing cybersecurity within DevOps, particularly for sensitive systems, making it crucial for organizations to adopt these methodologies to protect their data and infrastructure.
Key Takeaways
- Integration of LLMs can automate the creation of attack defense trees.
- Proactive security measures are essential for modern DevSecOps environments.
- The proposed methodology combines Security Chaos Engineering with AI to anticipate threats.
- Organizations managing sensitive data must prioritize advanced cybersecurity strategies.
- The research includes replicable experiments to validate the proposed approach.
Computer Science > Cryptography and Security arXiv:2602.14106 (cs) [Submitted on 15 Feb 2026] Title:Anticipating Adversary Behavior in DevSecOps Scenarios through Large Language Models Authors:Mario Marín Caballero, Miguel Betancourt Alonso, Daniel Díaz-López, Angel Luis Perales Gómez, Pantaleone Nespoli, Gregorio Martínez Pérez View a PDF of the paper titled Anticipating Adversary Behavior in DevSecOps Scenarios through Large Language Models, by Mario Mar\'in Caballero and 5 other authors View PDF HTML (experimental) Abstract:The most valuable asset of any cloud-based organization is data, which is increasingly exposed to sophisticated cyberattacks. Until recently, the implementation of security measures in DevOps environments was often considered optional by many government entities and critical national services operating in the cloud. This includes systems managing sensitive information, such as electoral processes or military operations, which have historically been valuable targets for cybercriminals. Resistance to security implementation is often driven by concerns over losing agility in software development, increasing the risk of accumulated vulnerabilities. Nowadays, patching software is no longer enough; adopting a proactive cyber defense strategy, supported by Artificial Intelligence (AI), is crucial to anticipating and mitigating threats. Thus, this work proposes integrating the Security Chaos Engineering (SCE) methodology with a new LLM-based flow to automate...
