This whitepaper explores how AI red teaming must evolve to address the emerging risks of agentic AI systems – complex workflows powered by LLMs, tools, APIs, and autonomous agents. Built on real-world insights from automated red teaming across enterprise applications, the paper provides a practical guide for security leaders, AI engineers, and compliance teams aiming to secure next-generation AI deployments. Learn how to adapt testing strategies, model new risks, and bring transparency to complex AI workflows.

Understand and Secure Agentic AI Workflows

• See how agentic systems differ from traditional LLM apps – and why they require new testing approaches

• Identify key risks like multi-turn prompt injection, tool misuse, data leakage, and RAG poisoning

• Walk through real-world multi-agent attack scenarios and how to test against them

Threat Modeling as an Agentic AI Red Teaming Foundation

• Apply the MAESTRO Framework to assess risks across layered agentic architectures

• Avoid incomplete testing by modeling inter-agent communication, autonomy, and emergent behavior

• Use Agentic Radar to map agents, tools, decision paths, and vulnerabilities for precise gray-box testing

• Build context-aware red teaming strategies targeting realistic attack surfaces

Multi-Turn Attacks & Evolving Red Teaming Workflows

• Learn how multi-turn injections exploit memory, context persistence, and agent chaining

• Understand how benign prompts can escalate to tool misuse, data exfiltration, or policy bypass

• Explore a real-world example of cascading prompt injection across agents and tools

• See why red teaming must shift from black-box to automated, gray-box testing tailored to agentic complexity

Download the whitepaper and start securing agentic AI today.