Attack Your AI Systems Before Attackers Do
Join Nahid Farady, PhD (Principal Tech Lead, AI Security & Privacy · Microsoft) for a free live session.
Nahid Farady, PhD
Principal Tech Lead, AI Security & Privacy · Microsoft
⭐ 4.9 / 5
You can't defend AI systems you haven't attacked.
Security engineering job descriptions now ask for AI security skills — prompt injection exploitation, agentic attack surfaces, model supply chain risk — and most security engineers have never touched them. In 6 weeks you'll build a mapped, tested attack surface for any LLM system, exploit the full OWASP LLM Top 10 and the emerging Agentic AI Top 10 before attackers do, and produce the audit reports and ship/hold frameworks your organization will act on. The skills that define the AI Security Engineer role, built through hands-on offensive and defensive work.
What You'll Learn
LLM Threat Model
OWASP LLM Top 10-prioritized attack surface map — input vectors, agent trust boundaries, RAG pipeline exposure, output risks — for any architecture you secure. The reusable starting document for every security review.
Prompt Injection Playbook
10+ variants (direct, indirect, stored, multi-turn, jailbreak) run with Garak and PyRIT — documented by success condition and architectural cause. CI/CD-ready test suite you drop into your pipeline after the course.
RAG and Agentic Attack Surfaces
Retrieval poisoning, context window stuffing, and embedding manipulation against live vector stores — plus tool call injection, memory poisoning, and cross-agent propagation from the OWASP Agentic AI Top 10 (2026).
AI Security Review Framework
SecAI+ Domain 2-aligned audit — attack surface coverage, defense depth, guardrail evaluation, residual risk, and CI/CD security gates — producing a ship/hold decision your security lead, CISO, and legal team can act on.
Who Is This For?
This course is designed for:
Security Engineers Moving into AI
Who know network security, cloud platforms, and AppSec cold — but have never faced an LLM attack surface and need a structured offensive and defensive path to own AI security in their practice.
Senior Security Engineers Expanding into AI
Who lead security reviews for AI deployments at their organization but have no structured framework for LLM-specific attack surfaces, agentic systems, or model supply chain risk — and are building that capability now.
AppSec Engineers and Security Leads
Who are responsible for pre-release security sign-off on AI features and have no repeatable audit process for LLM systems — and need a ship/hold framework their stakeholders will actually act on.
Course Outline
6 weeks · 3 sessions per week
Projects You'll Ship
Leave with real work to show, not just a certificate.
AI Threat Model
A complete, prioritized attack surface map for a real AI system — input vectors, agent trust boundaries, output leakage, and model supply chain exposure — structured against the OWASP LLM Top 10. Reusable as the standard starting document for every security review you run.
Full Attack Suite (Injection + RAG + Agentic)
A combined, reproducible attack library covering prompt injection (10+ variants), RAG retrieval exploitation (retrieval poisoning, embedding manipulation), and OWASP Agentic AI Top 10 attack classes — built with Garak and PyRIT, documented by success condition and architectural cause. CI/CD-ready: integrates directly into any deployment pipeline to catch regressions automatically.
AI Security Audit Report + Ship/Hold Decision
A SecAI+ Domain 2-aligned audit report — attack surface coverage, defense depth, guardrail selection rationale, CI/CD gate implementation, and residual risk documentation — with a justified ship/hold recommendation your security lead, CISO, and legal team can act on. The format becomes your standard pre-release process for every AI deployment.
Your Instructors
Nahid Farady, PhD
Principal Tech Lead, AI Security & Privacy · Microsoft
⭐ 4.9 / 5
Nahid leads AI security, privacy, and responsible AI engineering at Microsoft Copilot, with prior roles at Google Cloud and Capital One CyberML. She holds a PhD from Virginia Tech and brings 10+ years of applied experience in cybersecurity, threat modeling, and ML deployment at scale. She also teaches AI and security as adjunct faculty at UC Berkeley.
What Students Say
⭐⭐⭐⭐⭐
"The agentic attack lab in Week 4 covered attack classes our entire security team had never seen. We went back and audited our production agent system the next week and found two live vulnerabilities."
Jordan Park
Senior Security Engineer · Okta
⭐⭐⭐⭐⭐
"The prompt injection test suite from Week 2 is now part of our CI/CD pipeline. We've caught two real issues before they reached production since the cohort ended."
Maya Torres
Security Engineer · Databricks
⭐⭐⭐⭐⭐
"The ship/hold framework in Week 6 changed how our security reviews work. For the first time, engineering and legal are aligned on what 'secure enough to ship' actually means."
Ryan Okafor
Tech Lead · Stripe
Course Schedule
All sessions are instructor-led and live. Recordings available within 24 hours.
SUNDAY
9:00 AM PDT
Live ClassDeep dive with live attack labs, adversarial exercises, and tool-level breakdowns. Offensive and defensive every week.
WEDNESDAY
6:00 PM PDT
Lab SessionStructured attack or defense lab with instructor guidance. Bring your system, your findings, your blockers.
THURSDAY
6:00 PM PDT
Build & ShipBuild and red-team your weekly deliverable. Peer review before submission.
Frequently Asked Questions
Related reading
The JD-backed research behind this course — from Dexity Intel.
The Cybersecurity + AI Career Path in 2026 →How far do you want to go?
Start free to experience our offering, choose the program length you would want to commit to.
You build. Nobody demos at you.
Every session is follow-along — you build the thing yourself while a practitioner works beside you. That is why the hours look long: they are yours to build in, with an expert on hand to guide you. None of it is a traditional lecture.
In the session, you'll: