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Course Outline
The AI Threat Landscape
- Why AI security differs: non-determinism, opaque reasoning, and prompts as attack surfaces.
- Attack taxonomy: training-time vs. inference-time vs. supply chain attacks.
- The ML adversary model: identifying who attacks AI systems and why.
OWASP Top 10 for LLM Applications
- Prompt injection: direct and indirect attack vectors.
- Insecure output handling and cross-plugin request forgery.
- Training data poisoning and supply chain vulnerabilities.
- Model denial of service, sensitive information disclosure, and excessive agency.
- Hands-on lab: exploiting each OWASP category against a test application.
Prompt Injection and Jailbreak Red Teaming
- Taxonomy of injection techniques: direct, indirect, multi-turn, and multi-modal.
- Automated red-teaming using Giskard, Garak, and custom fuzzing tools.
- Jailbreak classification and defense evaluation.
- Building a red-team harness for continuous LLM security testing.
Model-Level Attacks and Defenses
- Model extraction: stealing model weights and functionality via API queries.
- Membership inference: determining if specific data was included in the training set.
- Adversarial examples: perturbations designed to fool classifiers and embeddings.
- Data poisoning: corrupting training data to induce backdoors or degrade performance.
Input and Output Security Controls
- Input sanitization strategies extending beyond traditional web defenses.
- Output filtering: addressing toxicity, PII leakage, and hallucinated code execution.
- Guardrails as security infrastructure: leveraging NeMo, Guardrails AI, and custom policies.
- Enforcing structured output as a security boundary.
AI Supply Chain Security
- Model provenance: verifying model authenticity and integrity.
- Dependency scanning for ML frameworks and model formats.
- Secure model serving: implementing sandboxing, network isolation, and least-privilege access.
- Vetting fine-tuned and community models for embedded malware.
Operational Security for AI Systems
- Access control for model endpoints, vector stores, and agent tools.
- Audit logging for every model interaction and decision.
- Incident response for AI-specific breaches: when the model itself is compromised.
- Continuous security testing within CI/CD pipelines for ML.
Building an AI Security Program
- AI security maturity model and roadmap.
- Integrating AI security into existing AppSec and cloud security programs.
- Governance frameworks and emerging regulations for AI systems.
- Creating and maintaining an organizational AI security playbook.
Requirements
- Experience deploying ML models or LLM applications in production.
- Familiarity with security concepts including authentication, authorization, and threat modeling.
- Proficiency in Python for conducting adversarial testing exercises.
Audience
- Security engineers expanding into AI/ML threat surfaces.
- ML engineers responsible for model safety and robustness.
- Red team members incorporating AI systems into their testing scope.
14 Hours
