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This research introduces a formal runtime monitoring framework using Datalog to enforce security policies on agentic systems, addressing the critical need for provable safety guarantees in autonomous AI agents. By constructing event graphs to validate actions against specified rules before execution, the work bridges the gap between coarse-grained mitigation strategies and rigorous correctness proofs for multi-agent environments.
Formal Policy Enforcement for Agentic Systems
arXiv:2602.16708v4 Announce Type: replace-cross Abstract: As agentic computing systems grow increasingly popular, they raise serious security concerns due to their susceptibility to various attacks. While prior research efforts aim to mitigate these concerns, they are often too coarse-grained and offer no formal correctness guarantees. In this work, we present a novel approach to agentic security based on runtime monitoring. Our method allows the user to specify their intended policy in Datalog and enforces the specified policy at runtime by constructing event graphs. When an agent is about to execute an action such as a tool call, our approach determines whether the action would violate the policy in light of the current system state and the history of past events. The action is then blocked or permitted accordingly. We implemented our approach and evaluated it across multiple benchmarks, demonstrating its merits in real-world settings. We view this work as a step towards agentic systems that are provably safe and secure.
Abstract:As agentic computing systems grow increasingly popular, they raise serious security concerns due to their susceptibility to various attacks. While prior research efforts aim to mitigate these concerns, they are often too coarse-grained and offer no formal correctness guarantees. In this work, we present a novel approach to agentic security based on runtime monitoring. Our method allows the user to specify their intended policy in Datalog and enforces the specified policy at runtime by constructing event graphs. When an agent is about to execute an action such as a tool call, our approach determines whether the action would violate the policy in light of the current system state and the history of past events. The action is then blocked or permitted accordingly. We implemented our approach and evaluated it across multiple benchmarks, demonstrating its merits in real-world settings. We view this work as a step towards agentic systems that are provably safe and secure.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
arXiv:2602.16708 [cs.CR]
(or arXiv:2602.16708v4 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2602.16708 Focus to learn more arXiv-issued DOI via DataCite
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