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

Josh Harguess, Chris M. Ward

TL;DR

This paper tackles the heightened security challenges of AI systems by proposing a structured offensive security framework that complements traditional defenses. It details a three-tier approach—vulnerability assessment, penetration testing, and red teaming—mapped to an AI-specific inverted pyramid of threat realism and impact. The methodology integrates asset inventories, threat intelligence, automated scanning, and rigorous adversarial testing, including adversarial examples and jailbreak-style prompts, to reveal concrete weaknesses and prioritize remediation. By bridging foundational research with field-ready practices and emphasizing post-engagement knowledge transfer, the work aims to yield robust, trustworthy AI deployments across high-stakes domains.

Abstract

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.

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

TL;DR

This paper tackles the heightened security challenges of AI systems by proposing a structured offensive security framework that complements traditional defenses. It details a three-tier approach—vulnerability assessment, penetration testing, and red teaming—mapped to an AI-specific inverted pyramid of threat realism and impact. The methodology integrates asset inventories, threat intelligence, automated scanning, and rigorous adversarial testing, including adversarial examples and jailbreak-style prompts, to reveal concrete weaknesses and prioritize remediation. By bridging foundational research with field-ready practices and emphasizing post-engagement knowledge transfer, the work aims to yield robust, trustworthy AI deployments across high-stakes domains.

Abstract

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.
Paper Structure (28 sections, 5 figures, 2 tables)

This paper contains 28 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. AI Development Lifecycle
  4. Unique Vulnerabilities of AI Systems
  5. Current AI Security Efforts
  6. Defensive vs. Offensive Security
  7. A Deep Dive into Offensive Security for AI
  8. Vulnerability Assessment
  9. Asset identification:
  10. Threat intelligence:
  11. Automated scanning and analysis:
  12. Review of security controls:
  13. Reporting:
  14. Penetration Testing
  15. Scope and rules of engagement:
  16. ...and 13 more sections

Figures (5)

  • Figure 1: AI lifecycle based on CRISP-ML(Q) process model. This process highlights stages such as data engineering, model engineering, evaluation, deployment, and ongoing monitoring. Incorporating security and quality checks into each stage (data validation, model performance monitoring, etc.) is essential for AI system assurance.
  • Figure 2: The AI Security Pyramid of Pain (Ward et. al.ward2024pyramid)
  • Figure 3: The Build–Attack–Defend triangle illustrates a secure ecosystem in which developers build the system (Yellow Team), defenders protect it (Blue Team), and attackers test it (Red Team). Information flows between teams include design inputs from developers to defenders, red team findings shared with defenders to improve monitoring, and offensive insights passed to developers to address design flaws. These exchanges support continuous security improvement across the AI lifecycle.
  • Figure 4: The Inverted Pyramid of Red Teaming
  • Figure 5: The Inverted Pyramid of Red Teaming