Ajar: Measuring Open Privilege in Agent Defenses
A language model agent acts through the tools it is given. The data it reads while working on a task can redirect what it does with those tools. A growing set of techniques for safe and secure agent execution therefore sits between the agent and its tools, aiming to enforce access control, information flow or isolation at that boundary. Today these techniques are evaluated on agent-security benchmarks built around indirect prompt injection. Those benchmarks judge a defense by how far it brings the number of successful attacks down while preserving the agent's utility. A defense is judged only on the agent's execution. It can score well on both metrics while holding open a transfer, a dele...
| Authors | Reshabh K Sharma, Linxi Jiang, Shuo Chen et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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