Can prompt injections lead to complete infrastructure takeovers? Could AI applications be exploited 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 cohort in GenAI and LLM 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. Tackle hands-on challenges with actual AI applications to understand vulnerabilities and develop robust defenses. You’ll learn how to create a comprehensive security pipeline, mastering AI red and blue team strategies, building resilient defenses for LLMs, and handling incident response for AI-based threats. Additionally, implement a Responsible AI (RAI) program to enforce ethical AI standards across enterprise services, fortifying your organization’s AI security foundation.

By the end of this training, you will be able to:

- Exploit vulnerabilities in live AI applications to achieve code and command execution, uncovering scenarios such as cross-site scripting, SQL injection, insecure agent designs, and remote code execution for infrastructure takeover.

- Conduct GenAI 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, model inversion, and agentic attacks.

- 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.

- Develop LLM security scanners to detect and protect against injections, jailbreaks, manipulations, and risky behaviors, as well as defending LLMs with LLMs.

- Build and deploy enterprise-grade LLM defenses, including custom guardrails for input/output protection, security bench-marking, and penetration testing of LLM agents.

- Utilize open-source tools like HuggingFace, OpenAI, NeMo, Streamlit, and Garak to build custom GenAI tools and enhance your GenAI development skills.

- Establish a comprehensive AI SecOps process to secure the supply chain from adversarial attacks and create a robust threat model for enterprise applications.

- Implement an incident response and risk management plan for enterprises developing or using GenAI services.