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Duration 14 hours
Course Outline
The AI Threat Landscape
- Why AI security is unique: non-determinism, opaque reasoning, and prompts as attack surfaces
- Attack taxonomy: distinguishing training-time, inference-time, and supply chain attacks
- The ML adversary model: understanding who attacks AI systems and their motivations
OWASP Top 10 for LLM Applications
- Prompt injection: analyzing 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
- Classification of jailbreaks and evaluation of defense mechanisms
- Creating 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 part of 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 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
- Using structured output enforcement as a security boundary
AI Supply Chain Security
- Model provenance: verifying the authenticity and integrity of models
- 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 all model interactions and decisions
- Incident response for AI-specific breaches, including cases where the model itself is compromised
- Integrating continuous security testing into ML pipeline CI/CD processes
Building an AI Security Program
- Developing an AI security maturity model and roadmap
- Integrating AI security into existing AppSec and cloud security programs
- Applying 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 environments.
- Familiarity with core security concepts, such as authentication, authorization, and threat modeling.
- Proficiency in Python for adversarial testing exercises.
Target Audience
- Security engineers expanding their expertise into AI/ML threat surfaces.
- ML engineers responsible for ensuring model safety and robustness.
- Red team members incorporating AI systems into their testing scope.
Testimonials (1)
inventory and identifying the different risk exposures within AI
