The EC-Council Certified Offensive AI Security Professional credential establishes that the holder can identify, exploit, document, and recommend remediation for vulnerabilities specific to artificial intelligence systems, with particular emphasis on large language model deployments, machine learning pipelines, and the supporting infrastructure that surrounds them. Holders typically work as AI red teamers, penetration testers expanding their coverage into machine learning targets, application security engineers, ML platform security specialists, and offensive security consultants serving AI-heavy clients in regulated industries.
The exam covers the full attack surface of modern AI systems. Adversarial machine learning content addresses evasion attacks against image classifiers and natural language models, poisoning attacks on training data and fine-tuning corpora, model inversion attacks that recover training examples, membership inference that determines whether a record was in the training set, and model extraction through carefully crafted query budgets that reconstruct functional copies of a target model. Defenses, detection strategies, and the practical limits of robustness training and differential privacy are examined alongside the attacks themselves.
Large language model security is treated with the depth the current threat model demands: direct and indirect prompt injection, jailbreaks and persona overrides, system prompt extraction, training data extraction through divergent attacks, tool-use exploitation in agentic systems where the model is given write access to external services, retrieval augmented generation poisoning through corpus injection, and the supply chain risks associated with model hubs, parameter-efficient adapters, and open weight releases. The OWASP Top 10 for LLM Applications and the MITRE ATLAS knowledge base are used as organizing frameworks, with mapped scenarios for each technique.
AI infrastructure hardening covers the security posture of inference endpoints, vector databases, embedding services, fine-tuning APIs, training clusters, and the data labeling pipelines that feed them. Topics include authentication and rate limiting on model APIs, isolation between tenant workloads on shared GPU pools, secure handling of model artifacts, signed model provenance, and the detection of model theft through watermarking and behavioral fingerprinting.
Red team methodology content addresses scoping engagements where the target is an AI feature rather than a traditional application, designing test plans that probe both the model and the surrounding application plumbing, evidence collection that withstands engineering review, and reporting that translates probabilistic findings into actionable severity ratings stakeholders will accept and act on.
The volume includes 120 practice questions covering each exam domain, with detailed answer explanations that walk through the technique, the underlying weakness it exploits, and the controls that mitigate it.
Intended readers include penetration testers adding AI to their service offering, ML engineers responsible for production security, application security teams whose products now embed LLMs, and security researchers preparing for the credential. Familiarity with at least one ML framework and standard web application security is assumed.
Format: 8.5x11 perfect-bound, large-format study layout with attack-defense pairs, scenario walkthroughs, and labeled diagrams of representative system topologies.
Drafted with frontier large language models and adversarially verified for technical accuracy. This is an independent publication and is not affiliated with, endorsed by, or sponsored by EC-Council; all trademarks are property of their respective owners.
