# Offensive Security for AI Systems: Concepts, Practices, and Applications

arXiv: https://arxiv.org/abs/2505.06380 · HTML: https://mlsanity.com/paper/2505.06380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 2 of 2)

- [Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology](https://mlsanity.com/paper/2003.05155/) (2020)
- [The AI Security Pyramid of Pain](https://mlsanity.com/paper/2402.11082/) (2024)

## Built on by (most cited first, 1 of 1)

- [Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives](https://mlsanity.com/paper/2609.16694/) (2026)

## Abstract (arXiv)

As artificial intelligence (AI) systems become increasingly adopted across sectors, the need for robust, proactive security strategies is paramount. Traditional defensive measures often fall short against the unique and evolving threats facing AI-driven technologies, making offensive security an essential approach for identifying and mitigating risks. This paper presents a comprehensive framework for offensive security in AI systems, emphasizing proactive threat simulation and adversarial testing to uncover vulnerabilities throughout the AI lifecycle. We examine key offensive security techniques, including weakness and vulnerability assessment, penetration testing, and red teaming, tailored specifically to address AI's unique susceptibilities. By simulating real-world attack scenarios, these methodologies reveal critical insights, informing stronger defensive strategies and advancing resilience against emerging threats. This framework advances offensive AI security from theoretical concepts to practical, actionable methodologies that organizations can implement to strengthen their AI systems against emerging threats.