ToolGuardian: Declarative Security for AI Agent-Tool Interactions
LLM agents increasingly rely on external tools, expanding capability while creating a new security boundary: third-party tools may appear benign at the interface level while embedding unsafe behavior in implementation. Existing defenses rely on weak metadata, collapse characterization and policy judgment into a single decision, or use heuristic/LLM enforcement that lacks deterministic, auditable reasoning over task context and multi-tool composition. This paper presents ToolGuardian, a policy-driven framework for securing agent-tool interactions through pre-admission vetting and task-aware runtime authorization. ToolGuardian uses progressive characterization to convert evidence into struc...
| Authors | Arun Ravindran, Saurabh Deochake |
|---|---|
| Primary category | cs.CR |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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