aiqbench analysis
This paper targets a concrete failure mode in agentic coding tools: approvers that judge commands by their text miss harm hidden in invoked code or workspace state. Caper's approach of pre-executing commands in a snapshotted sandbox and rolling back blocked ones is a practical design pattern, but the reported gains come from the authors' own benchmark, so independent replication matters. For AI readers, the key point is that safety for autonomous agents depends on observed consequences, not just the request's wording.
"Shall We Approve?" Consequence-Aware Approval for Autonomous Coding Agents
arXiv:2610.40753v1 Announce Type: cross Abstract: Coding agents increasingly rely on automated approvers to decide whether a proposed shell command may run. Existing approvers, from rule-based checkers to guardrail models and deployed permission classifiers, base their decisions on the text of the request and command. Yet a command's behavior depends on the code it invokes and the state of the workspace, and systems that observe file changes leave the assessment to the agent or a human after execution. We present Caper, a consequence-aware approval mechanism that pre-executes each command in a snapshotted sandbox, provides an independent approver with a bounded report of its observed effects, commits allowed commands, and rolls back blocked ones with a reason the agent can act on. Compared with six existing systems, Caper achieves the lowest harmful-outcome rate on SABER (6.7% versus at least 14.8%) and YoloFS-Ext, a benchmark we build in which harmful behavior is hidden inside invoked tools (7.8% versus at least 35.3%), while blocking 1.0% of the safe twins of the dangerous tasks in YoloFS-Ext.
Title:"Shall We Approve?" Consequence-Aware Approval for Autonomous Coding Agents
Abstract:Coding agents increasingly rely on automated approvers to decide whether a proposed shell command may run. Existing approvers, from rule-based checkers to guardrail models and deployed permission classifiers, base their decisions on the text of the request and command. Yet a command's behavior depends on the code it invokes and the state of the workspace, and systems that observe file changes leave the assessment to the agent or a human after execution. We present Caper, a consequence-aware approval mechanism that pre-executes each command in a snapshotted sandbox, provides an independent approver with a bounded report of its observed effects, commits allowed commands, and rolls back blocked ones with a reason the agent can act on. Compared with six existing systems, Caper achieves the lowest harmful-outcome rate on SABER (6.7% versus at least 14.8%) and YoloFS-Ext, a benchmark we build in which harmful behavior is hidden inside invoked tools (7.8% versus at least 35.3%), while blocking 1.0% of the safe twins of the dangerous tasks in YoloFS-Ext.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2610.40753 [cs.CR]
(or arXiv:2610.40753v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2610.40753 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 273 words