PAuth - Precise Task-Scoped Authorization For Agents

Fuente: arXiv
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Autores principales: Sharma, Reshabh K, Jiang, Linxi, Lin, Zhiqiang, Chen, Shuo
Formato: Preprint
Publicado: 2026
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author Sharma, Reshabh K
Jiang, Linxi
Lin, Zhiqiang
Chen, Shuo
author_facet Sharma, Reshabh K
Jiang, Linxi
Lin, Zhiqiang
Chen, Shuo
contents The emerging agentic web envisions AI agents that reliably fulfill users' natural-language (NL)-based tasks by interacting with existing web services. However, existing authorization models are misaligned with this vision. In particular, today's operator-scoped authorization, exemplified by OAuth, grants broad permissions tied to operators (e.g., the transfer operator) rather than to the specific operations (e.g., transfer $100 to Bob) implied by a user's task. This will inevitably result in overprivileged agents. We introduce Precise Task-Scoped Implicit Authorization (PAuth), a fundamentally different model in which submitting an NL task implicitly authorizes only the concrete operations required for its faithful execution. To make this enforceable at servers, we propose NL slices: symbolic specifications of the calls each service expects, derived from the task and upstream results. Complementing this, we also propose envelopes: special data structure to bind each operand's concrete value to its symbolic provenance, enabling servers to verify that all operands arise from legitimate computations. PAuth is prototyped in the agent-security evaluation framework AgentDojo. We evaluate it in both benign settings and attack scenarios where a spurious operation is injected into an otherwise normal task. In all benign tests, PAuth executes the tasks successfully without requiring any additional permissions. In all attack tests, PAuth correctly raises warnings about missing permissions. These results demonstrate that PAuth's reasoning about permissions is indeed precise. We further analyze the characteristics of these tasks and measure the associated token costs.
format Preprint
id arxiv_https___arxiv_org_abs_2603_17170
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle PAuth - Precise Task-Scoped Authorization For Agents
Sharma, Reshabh K
Jiang, Linxi
Lin, Zhiqiang
Chen, Shuo
Cryptography and Security
Artificial Intelligence
Programming Languages
The emerging agentic web envisions AI agents that reliably fulfill users' natural-language (NL)-based tasks by interacting with existing web services. However, existing authorization models are misaligned with this vision. In particular, today's operator-scoped authorization, exemplified by OAuth, grants broad permissions tied to operators (e.g., the transfer operator) rather than to the specific operations (e.g., transfer $100 to Bob) implied by a user's task. This will inevitably result in overprivileged agents. We introduce Precise Task-Scoped Implicit Authorization (PAuth), a fundamentally different model in which submitting an NL task implicitly authorizes only the concrete operations required for its faithful execution. To make this enforceable at servers, we propose NL slices: symbolic specifications of the calls each service expects, derived from the task and upstream results. Complementing this, we also propose envelopes: special data structure to bind each operand's concrete value to its symbolic provenance, enabling servers to verify that all operands arise from legitimate computations. PAuth is prototyped in the agent-security evaluation framework AgentDojo. We evaluate it in both benign settings and attack scenarios where a spurious operation is injected into an otherwise normal task. In all benign tests, PAuth executes the tasks successfully without requiring any additional permissions. In all attack tests, PAuth correctly raises warnings about missing permissions. These results demonstrate that PAuth's reasoning about permissions is indeed precise. We further analyze the characteristics of these tasks and measure the associated token costs.
title PAuth - Precise Task-Scoped Authorization For Agents
topic Cryptography and Security
Artificial Intelligence
Programming Languages
url https://arxiv.org/abs/2603.17170