Whitepaper
The Current State Of Agentic AI Red Teaming
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.
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