[2603.00991] Tracking Capabilities for Safer Agents

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Computer Science > Artificial Intelligence

arXiv:2603.00991 (cs)

[Submitted on 1 Mar 2026 (v1), last revised 7 May 2026 (this version, v2)]

Title:Tracking Capabilities for Safer Agents

Authors:Martin Odersky, Yaoyu Zhao, Yichen Xu, Oliver Bračevac, Cao Nguyen Pham<br>View a PDF of the paper titled Tracking Capabilities for Safer Agents, by Martin Odersky and 4 other authors

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Abstract:AI agents that interact with the real world through tool calls pose fundamental safety challenges: agents might leak private information, cause unintended side effects, or be manipulated through prompt injection. To address these challenges, we propose to put the agent in a programming-language-based "safety harness": instead of calling tools directly, agents express their intentions as code in a capability-safe language: Scala 3 with capture checking. Capabilities are program variables that regulate access to effects and resources of interest. Scala's type system tracks capabilities statically, providing fine-grained control over what an agent can do. In particular, it enables local purity, the ability to enforce that sub-computations are side-effect-free, preventing information leakage when agents process classified data. We demonstrate that extensible agent safety harnesses can be built by leveraging a strong type system with tracked capabilities. Our experiments show that agents can generate capability-safe code with no significant loss in task performance, while the type system reliably prevents unsafe behaviors such as information leakage and malicious side effects.

Subjects:

Artificial Intelligence (cs.AI); Programming Languages (cs.PL)

Cite as:<br>arXiv:2603.00991 [cs.AI]

(or<br>arXiv:2603.00991v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.00991

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arXiv-issued DOI via DataCite

Submission history<br>From: Yichen Xu [view email]<br>[v1]<br>Sun, 1 Mar 2026 08:39:37 UTC (4,774 KB)

[v2]<br>Thu, 7 May 2026 14:09:04 UTC (4,781 KB)

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