Course Outline
Module 1 — How AI Apps Break
Lab: none — architecture walkthrough & discussion
Developing a builder’s mental model of the attack surface.
Topics:
- LLM, RAG, and agent architectures from the developer’s perspective
- The request/response lifecycle of an AI feature
- Prompt flow: system, developer, user, and tool messages
- Points where untrusted data enters (and re-enters) the model
- Trust boundaries owned by the developer versus those inherited
- Why AI attacks are semantic rather than syntactic
- Mapping the OWASP LLM Top 10 to code implementations
Key insight: Every point where untrusted text reaches the model—or where model output reaches application code—is a boundary under the developer’s control.
Module 2 — Prompt Injection for Builders
Lab: Lab 01 — 01-Prompt-Injection
The "SQL injection moment" for AI—with the caveat that it cannot be fully escaped.
Topics:
- Direct vs. indirect prompt injection
- Hidden instructions in documents, web pages, and tool outputs
- Jailbreaks and role-confusion
- The importance of separating instructions from data
- Defensive prompt design (delimiters, structure, minimal authority)
- Why prevention is partial—designing for containment
Hands-on:
- Attack your own chatbot
- Bypass a naïve filter
- Restructure the prompt to reduce the blast radius
Module 3 — Treating Model Output as Untrusted
Lab: Lab 02 — 02-Output-Handling
The most underestimated class of bugs among developers.
Topics:
- Viewing model output as untrusted input to the rest of the app
- Insecure output handling (LLM02): XSS, SSRF, and downstream command/SQL injection
- Avoiding eval/exec/render of raw model output
- Structured outputs and schema validation
- Output encoding and allowlists
- Safe rendering in web/UI contexts
Hands-on:
- Identify and fix an insecure-output-handling vulnerability
- Enforce a JSON schema on model responses
Module 4 — RAG Security
Lab: Lab 03 — 03-RAG-Security
One of the largest new attack surfaces—which is yours to secure.
Topics:
- Vector DB and retrieval threats
- Ingestion sanitization
- Document provenance and trust scoring
- Retrieval scoping and metadata isolation
- Hidden instructions in retrieved content (indirect injection)
- Data exfiltration via retrieval
Hands-on: Poison a RAG pipeline with a malicious document, then add ingestion sanitization and retrieval scoping to defend it
Module 5 — Agent & Tool Safety
Lab: Lab 04 — 04-Agent-Safety
Where a software bug becomes an executed action.
Topics:
- Excessive agency (LLM06) and tool abuse
- Least privilege for agents
- Tool allowlists and argument validation
- Approval gates and human-in-the-loop mechanisms
- Sandboxing tool execution
- Scoped, short-lived credentials for agents
- Limiting autonomous loops and chaining
Hands-on:
- Lock down an over-permissioned agent
- Add an allowlist and approval gate to a dangerous tool
Module 6 — Secrets, Identity & Cost
Lab: Lab 05 — 05-Secrets-and-Cost
The operational mistakes that cause the most immediate damage.
Topics:
- API key and secret management (excluded from prompts, code, and logs)
- Per-user authentication and authorization for AI features
- Propagating user identity to tools and retrieval systems
- Denial-of-wallet: unbounded token/cost consumption
- Rate limits, token budgets, and timeouts
- Logging without leaking secrets or PII
Hands-on:
- Remove secrets from the prompt/code path
- Add per-user rate limits and a token/cost budget
Module 7 — Guardrail Libraries
Lab: Lab 06 — 06-Guardrails
Deciding when to buy vs. build for input/output safety.
Topics:
- Capabilities and limitations of guardrail frameworks
- Input guardrails: injection/PII/topic classifiers
- Output guardrails: validation, filtering, and grounding checks
- When to use guardrails vs. custom deterministic checks
- Layering guardrails with controls from previous modules
- Performance, false positives, and failure modes
Hands-on:
- Add an input/output guardrail layer to an AI feature
- Measure its coverage and identify gaps
Module 8 — Red-Teaming Your Own App
Lab: Lab 07 — 07-Red-Teaming
Ship with the assumption that an attacker already has access.
Topics:
- Building an abuse/test suite for AI features
- Automated prompt-injection and jailbreak tests
- Regression-testing guardrails and policies
- Integrating AI security checks into CI
- Model and dependency supply chain security (provenance, pinning)
- A pre-ship security checklist for AI features
Hands-on:
- Write automated red-team tests for an AI feature
- Integrate these tests into a CI pipeline
Module 9 — Scoring AI Security: The SAIS-100 Framework
Lab: none — scoring exercise (uses the Capstone app)
Converting your built defenses into a repeatable score.
Topics:
- The AI Security Hexagon: six questions instead of "is it secure?"
- Six scored categories (Data, Prompt, Agent, Supply Chain, Detection, Governance)
- The 100-point rubric and its weightings
- Verdict bands and the single-category override rule
- The Elephant Scale Secure AI Score (SAIS-100) as a branded, re-runnable framework
- Using pre/post-hardening scores as a metric
Hands-on:
- Score the Capstone app on the 100-point scale
- Identify the single change that most increases the score
Key insight: The three highest-weighted categories align with the trust boundaries owned by the developer—ensuring the score measures exactly what this course teaches.
Capstone
Students harden a deliberately vulnerable AI application end-to-end.
The starter app contains:
- An injectable prompt
- Insecure output handling
- An unscoped RAG pipeline
- An over-permissioned agent
- Secrets in the prompt path
- No cost limits
Students apply course concepts to:
- Restructure prompts for containment
- Validate and encode model output
- Sanitize and scope retrieval
- Apply least privilege and approval gates to the agent
- Move secrets out and add cost/rate limits
- Add guardrails and automated red-team tests
Deliverable: A hardened application plus a short OWASP LLM Top 10 self-assessment.
Module - Lab map
Labs follow module order. The course comprises 9 modules and 7 labs. Module 1 is an architecture walkthrough/discussion and Module 9 is a scoring exercise; neither has a dedicated lab folder.
- Lab 01 - 01-Prompt-Injection: Attack your chatbot & design for containment (Module 2)
- Lab 02 - 02-Output-Handling: Fix an insecure-output-handling bug (Module 3)
- Lab 03 - 03-RAG-Security: Poison then defend a RAG pipeline (Module 4)
- Lab 04 - 04-Agent-Safety: Lock down an over-permissioned agent (Module 5)
- Lab 05 - 05-Secrets-and-Cost: Secure keys + add cost guardrails (Module 6)
- Lab 06 - 06-Guardrails: Add an input/output guardrail layer (Module 7)
- Lab 07 - 07-Red-Teaming: Automated red-team tests in CI (Module 8)
Module 1 (How AI Apps Break) has no lab—it runs as an architecture walkthrough and discussion. Module 9 (Scoring AI Security) has no lab folder—it runs as a scoring exercise against the Capstone app.
Requirements
- Skill level: Intermediate.
- Students should be proficient in: building and consuming REST APIs, using a scripting language (labs use Python), basic application authentication, Git, and the CLI
- No machine learning background is required—this is an application security course for those building with LLMs, not training them.
Audience
- Software / backend engineers developing LLM features
- Full-stack and API developers
- AI/ML application engineers
- Platform engineers shipping copilots and agents
- Tech leads and senior engineers responsible for AI features
Testimonials (3)
inventory and identifying the different risk exposures within AI
Gary Cook - Cybersecurity and Information Technology Risk Division
Course - Introduction to AI Trust, Risk, and Security Management (AI TRiSM)
I really enjoyed learning about AI attacks and the tools out there to begin practicing and actively using for security testing. I took a lot of knowledge away which I didn't have at the beginning and the course met what I hoped it would be. My favorite part shown from the training was Comet Browser and was amazed at what it could do. Definitely something will be looking into more. Overall it was a great course and enjoyed learning all OWASP GenAI Top 10.
Patrick Collins - Optum
Course - OWASP GenAI Security
The profesional knolage and the way how he presented it before us
