Can prompt injections lead to complete infrastructure takeovers? Could AI agents, MCP-connected tools, or poisoned external context be abused to compromise backend services? Can data poisoning in AI copilots impact a company’s stock? Can jailbreaks create false crisis alerts in security systems? This immersive, CTF-styled training in GenAI, LLM, agent, and MCP security dives into these pressing questions. Engage in realistic attack-and-defense scenarios focused on real-world threats, from prompt injection and remote code execution to backend compromise, tool abuse, unsafe agent orchestration, and MCP-specific trust and authorization failures. Tackle hands-on challenges with live AI applications to understand vulnerabilities and build robust defenses. Learn how to create a comprehensive security pipeline, master AI red and blue team strategies, secure tool-connected and agentic systems, build resilient guardrails for LLMs, and handle incident response for AI-based threats. You will also explore governance, Responsible AI, and enterprise security patterns for modern AI ecosystems.
By the end of this training, you will be able to:
- Exploit vulnerabilities in AI applications to achieve code and command execution, uncovering scenarios such as instruction injection, agent control bypass, remote code execution for infrastructure takeover, as well as chaining multiple agents for goal hijacking.
- Conduct AI red-teaming using adversary simulation, OWASP LLM Top 10, and MITRE ATLAS frameworks, while applying AI security and ethical principles in real-world scenarios.
- Execute and defend against adversarial attacks, including prompt injection, data poisoning, jailbreaks, agentic attacks, and insecure tool-connected workflows.
- Perform advanced AI red and blue teaming through multi-agent auto-prompting attacks, implementing a 3-way autonomous system consisting of attack, defend, and judge models.
- Build and deploy enterprise-grade LLM defenses, including custom guardrails for input/output protection, security benchmarking, penetration testing of LLM agents, and defensive controls for MCP-enabled integrations.
- Understand MCP fundamentals and assess how they expand the attack surface of modern AI systems.
- Establish a comprehensive LLM SecOps process to secure the supply chain from adversarial attacks and create a robust threat model for enterprise applications, including AI systems connected to external tools and data sources through MCP-like architectures.
- Implement an incident response and risk management plan for enterprises developing or using AI services.
Course Level
Intermediate
Course Requirements
- A laptop with a modern browser and reliable internet access.
- An OpenAI API key.
- A Google Colab account.
- Completion of the pre-training setup before Day 1.
Key takeaways
- Learn how to identify, exploit, and defend against real-world attacks on AI applications, agents, and tool-connected systems, including prompt injection, jailbreaks, agent abuse, and chained compromise paths.
- Build practical defensive capabilities for enterprise AI, including guardrails, security scanners, monitoring, and response patterns for public, private, and MCP-enabled AI services.
- Gain hands-on experience using modern AI techniques for security testing, validation, and red/blue teaming, including judge-LLM workflows, attack automation, and securing agentic AI supply chains.
