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Course Outline
Day 1 — Agentic AI Security Deep Dive
Session 1 — 09:30 to 10:50 · Recap and Prompt Injection at Depth
- Quick recap of the OWASP LLM Top 10 (2025) — agreed baseline
- Advanced prompt injection: indirect injection, multi-turn manipulation, cross-modal injection
- Jailbreak techniques and defensive taxonomies
- System prompt leakage and information extraction patterns
- Interactive Slido poll: "What's the most sensitive tool your agents have access to today?"
Break — 10:50 to 11:10
Session 2 — 11:10 to 12:30 · Securing AI Pipelines — Data, Models, and RAG
- Training data integrity: poisoning, backdoors, and provenance
- Model supply chain risks: fine-tuning pipelines, adapter models, and registry hygiene
- RAG-specific attack surfaces: vector store poisoning, context manipulation, retrieval attacks
- Embedding security: what embeddings leak and how to protect them
- Hands-on lab (~30 minutes): Delegates poison a small RAG corpus and then defend it. Paired exercise followed by group debrief.
Lunch — 12:30 to 13:20
Session 3 — 13:20 to 14:40 · OWASP Top 10 for Agentic Applications (2026) — Part 1
- Agent goal manipulation and objective subversion
- Tool-use permission abuse and privilege escalation via tool chains
- Memory manipulation: persistent, episodic, and shared memory attacks
- Planning and reasoning exploits
- Identity and authentication in agent systems
- Short live demo: A goal-manipulation attack against a simple planning agent
Break — 14:40 to 15:00
Session 4 — 15:00 to 16:30 · OWASP Top 10 for Agentic Applications (2026) — Part 2 + MCP Security
- MCP (Model Context Protocol) architecture and trust boundaries
- MCP server security: authentication, tool scoping, and permission models
- Multi-step workflow attacks: chaining, indirect execution, cascading failures
- Cross-agent communication and trust
- Agent observability and forensic readiness
- Day 1 close: each delegate identifies one critical agentic risk in their own stack
- Q&A
Day 2 — Red-Teaming, Architecture, and Incident Response
Session 1 — 09:30 to 10:50 · AI Red-Teaming — Methodology
- What AI red-teaming is (and is not) — distinction from traditional pentesting
- Red-teaming frameworks: MITRE ATLAS, OWASP Agentic Top 10 mapping, NIST AI RMF
- Scoping a red-team engagement for an LLM or agent system
- Manual techniques: prompt-engineering attacks, jailbreak libraries, goal-hijacking
- Automated tooling landscape: Garak, PyRIT, Promptfoo, custom harnesses
- Ethics, safety, and responsible disclosure for AI vulnerabilities
Break — 10:50 to 11:10
Session 2 — 11:10 to 12:30 · Hands-On Red-Teaming Lab
- Extended hands-on lab (~60 minutes): Delegates work in pairs against a prepared target — a multi-step agentic application with at least three known vulnerabilities. Each pair produces a short red-team report, including attack path, impact assessment, and recommended mitigations.
- Group share-back and collective debrief
Lunch — 12:30 to 13:20
Session 3 — 13:20 to 14:40 · Secure Architecture Patterns for Agentic AI in Government
- Defence-in-depth for agent systems: isolation, sandboxing, and blast-radius reduction
- Designing safe tool catalogues: allow-listing, parameter validation, output inspection
- Human-in-the-loop patterns and when to require confirmation
- Sensitive data boundaries: where PII and OFFICIAL-SENSITIVE data can and cannot flow
- Aligning with UK AI Principles, NIST AI RMF, and ISO/IEC 42001 controls
- Architectural case study: a realistic government agentic service walkthrough
Break — 14:40 to 15:00
Session 4 — 15:00 to 16:30 · Incident Response, Playbook Build, and Close
- AI-specific incident classes: prompt-injection escalation, tool misuse, data exfiltration via agents, model-misbehaviour incidents
- Detection signals and logging patterns for agent systems
- Response playbook structure: containment, eradication, recovery, lessons learned
- Capstone exercise (~45 minutes): Delegates build a one-page agent security playbook for a representative service from their own domain
- Implementation planning: 30-day, 60-day, 90-day actions
- Resources, further reading, and next steps
- Q&A and course close
Requirements
- Confident with at least one modern programming language (Python strongly recommended for labs)
- Prior completion of AI Security Fundamentals for Developers or equivalent working knowledge of the OWASP Top 10 for LLM Applications (2025)
- Familiarity with REST APIs, containerisation basics, and general secure development practices
- Experience with at least one LLM API (OpenAI, Anthropic Claude, Azure OpenAI, or similar) is helpful but not essential
Audience
- Software engineers and AI/ML engineers building agentic or tool-using AI systems
- Security engineers and security champions working with AI-enabled products
- Platform and DevOps engineers responsible for LLM and agent infrastructure
- Technical leads and architects designing AI-powered government services
- Those who have completed AI Security Fundamentals for Developers or have equivalent experience
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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