Formal Policy Enforcement for Agentic Systems
cs.CR, cs.AI, cs.MA
Submitted: 2026-02-18
Updated: 2026-10-12
Code: https://github.com/invariantlabs-ai/invariant
License: http://creativecommons.org/licenses/by/4.0/
The gist: As agentic computing systems grow increasingly popular, they raise serious security concerns due to their susceptibility to various attacks.
Terminology
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.
Sources
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- ChemCrow: Augmenting large-language models with chemistry tools
- Why Do Multi-Agent LLM Systems Fail?
- Securing AI Agents with Information-Flow Control
- Defeating Prompt Injections by Design
- Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
- A Critical Evaluation of Defenses against Prompt Injection Attacks
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents
- The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
- Progent: Securing AI Agents with Privilege Control
- The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
- AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
- GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
- InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
Related papers
- SoK: AI-Augmented Binary Reversing
- Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
- Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
- Efficient Fuzzy PSI under One-Sided Assumptions
- Sealing the Audit-Runtime Gap for LLM Skills
- Token Composition: A Graph Based on EVM Logs