Model Security
Model Security is the discipline of protecting machine learning and AI models against attacks that exploit their vulnerabilities — including adversarial examples crafted to fool models at inference time, data poisoning that corrupts training sets, model inversion or extraction attacks that steal intellectual property, and backdoors implanted during fine-tuning. It covers both offense (understanding how attacks work) and defense (robustness training, input validation, monitoring, threat modeling). The field bridges classical cybersecurity with the unique failure modes introduced by statistical learning systems.
As AI models move into high-stakes domains — fraud detection, healthcare triage, autonomous systems, and enterprise chatbots — their attack surface becomes a board-level risk, and regulators such as the EU AI Act now mandate risk assessments for high-risk AI. Organizations building or deploying models increasingly need specialists who can perform ML threat modeling, red-team LLM deployments, and implement MLSecOps pipelines. The OWASP Top 10 for LLM Applications (updated 2025) and NIST AI RMF have made model security a compliance requirement, not just a best practice.
🎓 Courses
Securing AI Systems
Hands-on course covering adversarial attacks, data poisoning, model theft, and defense strategies with guided labs. Directly targets AI/ML security practitioners and is one of the most comprehensive dedicated offerings on Coursera.
Secure AI: Threat Model & Test Endpoints
Teaches how to analyze AI inference threat models, identify attack vectors, design adversarial robustness test cases, and integrate AI security testing into CI/CD pipelines — practical MLSecOps focus.
Securing Generative AI (Pearson)
Covers LLM-specific risks including prompt injection, training data poisoning, model denial of service, insecure plugin design, and RAG security — aligned with OWASP LLM Top 10 2025.
OWASP Gen AI Security Project — Resources & Learning
by OWASP GenAI Security Community
Free community-maintained library of whitepapers, red-teaming guides, cheat sheets, and the authoritative LLM Top 10 for 2025. Essential reference for anyone working on LLM or agentic AI security.
LLM Red Teaming: The Complete Step-By-Step Guide
by Confident AI
Practical walkthrough of red-teaming methodology for LLMs: identifying model vs. system weaknesses, bias/toxicity probing, jailbreak testing, and applying mitigations. Free and current (2025).
📖 Books
Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps
John Sotiropoulos · 2024
The most comprehensive recent practitioner book on AI security, written by an OWASP LLM Top 10 co-lead. Covers offense and defense across the full attack taxonomy (evasion, poisoning, extraction), threat modeling, and MLSecOps. Reviewed positively by Help Net Security.
🛠️ Tutorials & Guides
Building Secure AI by Design: A Defense-in-Depth Approach
Practical MLSecOps guide showing how to integrate security tasks at each phase of the AI development lifecycle, aligned with OWASP LLM Top 10, MITRE ATLAS, and NIST AI-RMF.
LLM Security Guide (OWASP GenAI Top-10, red-teaming tools, mitigations)
Comprehensive open-source reference covering OWASP GenAI Top-10 risks, prompt injection, adversarial attacks, real-world incidents, red-teaming tool catalogs, and guardrail strategies. Updated with 2025 content including agentic AI and RAG vulnerabilities.
OWASP LLM Top 10 (2025 Edition)
The canonical ranked list of the ten most critical LLM security risks for 2025, with descriptions, examples, and mitigations. Free, peer-reviewed, and the de facto industry standard referenced in compliance frameworks and hiring criteria.
🏅 Certifications
Practical DevSecOps — Certified AI Security Professional (CASP)
Practical DevSecOps · Paid (varies)
Emerging certification targeting AI/ML security practitioners, covering adversarial ML, LLM risks, and MLSecOps. Increasingly recognized as the field professionalizes around OWASP and NIST AI frameworks.
Learning resources last updated: June 18, 2026