Course Outline
Module 1 — How AI Apps Break
Lab: None — Architecture walkthrough & discussion
A builder’s mental model of the AI attack surface.
Topics:
- LLM, RAG, and agent architectures from a developer's perspective
- The request/response lifecycle of an AI feature
- Prompt flows: system, developer, user, and tool messages
- Where untrusted data enters (and re-enters) the model
- Trust boundaries owned by the developer versus inherited ones
- Why AI attacks are semantic, not syntactic
- Mapping the OWASP LLM Top 10 to your codebase
Key insight: Every point where untrusted text reaches the model—or where model output reaches your code—is a boundary you own.
Module 2 — Prompt Injection for Builders
Lab: Lab 01 — 01-Prompt-Injection
The “SQL injection moment” for AI — but you cannot fully escape it.
Topics:
- Direct vs. indirect prompt injection
- Hidden instructions within documents, web pages, and tool outputs
- Jailbreaks and role-confusion techniques
- The importance of instruction/data separation
- Defensive prompt design (delimiters, structure, minimal authority)
- Why prevention is only partial — design for containment
Hands-on:
- Attack your own chatbot
- Bypass naive filters
- Restructure prompts to shrink the blast radius
Module 3 — Treating Model Output as Untrusted
Lab: Lab 02 — 02-Output-Handling
The bug class developers most underestimate.
Topics:
- Treating model output as untrusted input to the rest of the application
- Insecure output handling (LLM02): downstream XSS, SSRF, command/SQL injection
- Never eval/exec/render 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 — and it is yours to build.
Topics:
- Vector database 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; add ingestion sanitization and retrieval scoping to defend it.
Module 5 — Agent & Tool Safety
Lab: Lab 04 — 04-Agent-Safety
Where a bug becomes an 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
- Limits on autonomous loops and chaining
Hands-on:
- Lock down an over-permissioned agent
- Add an allowlist + approval gate to a dangerous tool
Module 6 — Secrets, Identity & Cost
Lab: Lab 05 — 05-Secrets-and-Cost
The operational mistakes that cause the fastest impact.
Topics:
- API key and secret management (never include in prompts, code, or logs)
- Per-user authentication and authorization for AI features
- Propagating user identity to tools and retrieval processes
- Denial-of-wallet: managing unbounded token/cost consumption
- Rate limits, token budgets, and timeouts
- Logging without leaking secrets or PII
Hands-on:
- Move secrets out of the prompt/code path
- Add per-user rate limits and a token/cost budget
Module 7 — Guardrail Libraries
Lab: Lab 06 — 06-Guardrails
Buy vs. build decisions for input/output safety.
Topics:
- What guardrail frameworks do (and do not) provide
- Input guardrails: injection/PII/topic classifiers
- Output guardrails: validation, filtering, and grounding checks
- When to use a guardrail versus your own deterministic check
- Layering guardrails with controls from earlier modules
- Performance, false positives, and failure modes
Hands-on:
- Add an input/output guardrail layer to an AI feature
- Measure what it catches and what it misses
Module 8 — Red-Teaming Your Own App
Lab: Lab 07 — 07-Red-Teaming
Ship it as if 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
- Running AI security checks in CI pipelines
- 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
- Wire them into a CI check
Module 9 — Scoring AI Security: The SAIS-100 Framework
Lab: None — Scoring exercise (uses the Capstone app)
Turn everything you’ve built into a repeatable score.
Topics:
- The AI Security Hexagon: six questions instead of “is it secure?”
- The 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
- Scoring before/after hardening as a metric
Hands-on:
- Score the Capstone app on the 100-point scale
- Identify the single change that most raises the score
Key insight: The three highest-weighted categories map to the trust boundaries a developer owns — so the score measures exactly what this course taught.
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 embedded in the prompt path
- No cost limits
Students apply course learnings to:
- Restructure prompts for containment
- Validate and encode model output
- Sanitize and scope retrieval mechanisms
- 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 app plus a short OWASP LLM Top 10 self-assessment.
Module - Lab map
Labs run in order, following the module sequence. The course has 9 modules and 7 labs: Module 1 is an architecture walkthrough/discussion and Module 9 is a scoring exercise, so neither includes 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, scripting languages (labs use Python), basic application authentication, Git, and the Command Line Interface (CLI).
- No machine learning background is required—this is an application security course for developers who integrate LLMs into their applications, not those who train the models.
Audience
- Software and backend engineers developing LLM features
- Full-stack and API developers
- AI/ML application engineers
- Platform engineers deploying copilots and agents
- Tech leads and senior engineers responsible for AI features
Testimonials (2)
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
