Towards Building Safe & Secure Agentic AI - Dawn Song, UC Berkeley; UC Berkeley Center for Responsible Decentralized Intelligence & Matt White, Linux Foundation/PyTorch Foundation
Recent advancements in agentic AI have unlocked powerful new capabilities, however, they also introduce fundamentally new security risks. In this talk, I present a system-level view of the security landscape of agentic AI, drawing on a comprehensive systematization of attacks and defenses across modern agent architectures.
I show how increasing agent flexibility along different dimensions expands attack surfaces and enables threats such as prompt injection, memory poisoning, unsafe data flows, credential leakage, and unauthorized execution. Using real-world incidents and CVE analyses, I illustrate how agents can be manipulated through external content, compromised tools, or poisoned internal components.
The talk also provides a systematic overview of end-to-end automatic red teaming and risk assessment for agentic AI systems as well as a defense-in-depth framework for building secure agentic systems, spanning runtime guardrails, access control, information-flow tracking, privilege separation, and secure-by-design architectures, helping practitioners assess risk, close security gaps, and deploy agents safely at scale.
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Progressive Tool Discovery Using MCP
Agentic AI Foundation April 15, 2026 4:32 pm