Adversarial Testing
Adversarial testing is the practice of deliberately probing AI systems with malicious, misleading, or edge-case inputs to expose vulnerabilities before they reach production. It encompasses techniques such as prompt injection, jailbreaking, poisoning attacks, and evasion attacks applied to models ranging from classical ML classifiers to large language models. The goal is to find failure modes — outputs that are harmful, biased, deceptive, or exploitable — so that developers can harden the system through retraining, filtering, or architectural changes.
As AI systems are deployed in high-stakes contexts — hiring, healthcare, finance, legal advice — their failure modes carry real-world harm, regulatory exposure, and reputational risk. Regulators including the EU AI Act and NIST AI RMF now explicitly require adversarial evaluations for high-risk AI systems, making this a compliance-critical skill. Companies building LLM-powered products are actively hiring red teamers, AI security engineers, and safety evaluators who can run systematic adversarial assessments before and after deployment.
🎓 Courses
Red Teaming LLM Applications
by Giskard team (in collaboration with DeepLearning.AI)
Hands-on short course covering LLM vulnerability taxonomy, manual and automated red teaming, and a full red-teaming assessment workflow using the open-source Giskard library. Free to audit.
Secure AI: Red-Teaming & Safety Filters
Covers prompt injection, jailbreaking, and content manipulation defenses using industry tools including PyRIT, NVIDIA Garak, and Promptfoo. Suitable for AI developers and cybersecurity professionals.
A Deep Dive into LLM Red Teaming
Practical course focused specifically on LLM red teaming, covering attack generation, evaluation, and defense strategies for production LLM systems.
Adversarial Testing for Generative AI (Guide)
by Google ML team
Official Google guide describing a complete adversarial testing workflow for generative AI, covering explicit and implicit adversarial query types, test design principles, and mitigation pathways. Free.
LLM Red Teaming (Open Source Guide)
by Promptfoo team
Comprehensive open-source documentation and CLI tool for automated LLM red teaming. Covers attack plugins, custom strategies, and CI/CD integration for continuous adversarial evaluation.
📖 Books
Adversarial Machine Learning: Mechanisms, Vulnerabilities, and Strategies for Trustworthy AI
Jason Edwards · 2024
The most comprehensive recent practitioner book on the full AML lifecycle — attacks at training, deployment, and inference stages, with defense strategies and trade-offs. Written by a CISSP with real-world cybersecurity leadership experience. Published by Wiley.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025)
Vassilev, Oprea, Fordyce, Anderson et al. (NIST) · 2025
The authoritative NIST reference document defining attack taxonomies (poisoning, evasion, privacy) and mitigations. Free PDF. Essential reading for practitioners who need to align with US federal AI risk standards.
Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Springer Authors · 2024
Academic treatment of adversarial attacks across computer vision, NLP, and cybersecurity domains. Covers game-theoretical frameworks and proposes robust neural network defenses. Best for readers who want theoretical depth alongside applied coverage.
🛠️ Tutorials & Guides
What is Adversarial Testing? A Practical Guide for Safer AI in 2025
Accessible beginner guide covering attack types (prompt injection, jailbreaking, backdoor attacks), a four-step practical testing workflow, and advice on embedding adversarial testing into continuous development pipelines.
A Guide to Adversarial Testing for AI
Practical guide from a security consultancy perspective covering red teaming workflows, LLM-specific attack surfaces, and best practices for integrating adversarial testing into the AI development lifecycle.
OWASP AI Testing Guide
Community-driven open-source framework for AI security testing, extending OWASP's established application security methodology to AI and ML systems. Useful for teams that already use OWASP standards for web security.
🏅 Certifications
AI Red Teaming Professional (via GTK Cyber)
GTK Cyber · Paid (varies)
One of the few structured AI red teaming training programs available in 2026. GTK Cyber is listed among the top 5 AI red teaming training providers and focuses on practical adversarial evaluation of AI systems.
Learning resources last updated: June 18, 2026