2-Day Training: June 23-24, 2026
Level: Intermediate
Trainer:Abhinav Singh
You may attend this training course in person or virtually
To register, please purchase your training ticket
here. Training and conference are two separate ticket purchases.
Can prompt injections lead to complete infrastructure takeovers? Could AI agents be exploited to compromise backend services? Can jailbreaks create false crisis alerts in security systems? In multi-agent systems, what if an attacker takes over an agent’s goals, turning other agents into coordinated threats? This immersive, CTF-styled training in AI 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 & agentic systems 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 AI apps & agents, 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 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 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 benchmarking, and penetration testing of LLM agents.
- Establish a comprehensive LLM 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.
