Securing AI Agents with Information-Flow Control - Costa, Manuel; Köpf, Boris; Kolluri, Aashish; Paverd, Andrew; Russinovich, Mark; Salem, Ahmed; Tople, Shruti; Wutschitz, Lukas; Zanella-Béguelin, Santiago (arXiv, 2025)
As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control (IFC) to provide security guarantees for AI agents. We present a formal model to reason about the security and expressiveness of agent planners. Using this model, we characterize the class of properties enforceable by dynamic taint-tracking and construct a taxonomy of tasks to evaluate security and utility trade-offs of planner designs. Informed by this exploration, we present Fides, a planner that tracks confidentiality and integrity labels, deterministically enforces security policies, and introduces novel primitives for selectively hiding information. Its evaluation in AgentDojo demonstrates that this approach broadens the range of tasks that can be securely accomplished. A tutorial to walk readers through the concepts introduced in the paper can be found at https://github.com/microsoft/fides.