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Rohan Paul· @rohanpaul_ai · X· · 原发布时间 AI 评分54

卡内基梅隆大学论文研究:AI 代理违规后,日志能否证明责任

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这项研究提出用可核验的工具调用日志检查 AI 代理是否违反规则,并在违规则由服务商预存的押金赔付用户。研究在来自三个基准测试的 696 次违规运行中证明了 42.2%;作者估计,若将审批记录结构化,比例可达 83.0%。

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New Carnegie Mellon paper finds that OpenClaw's logs left 0 of 4 policy violations provable, a warning for any team relying on agent logs for audits.

They found that most agent policy violations in its tests could not be proven from logs, often because approvals and permissions were buried in chat text.

Also finds that only 10% to 30% of violating runs needed human judgment, so most agent rule breaks could be checked by code with better logs.

Safety work mostly tries to stop agents before they misbehave. When a mistake slips through, like a wiped inbox or a bad payment, users get little beyond slow, capped, case-by-case refunds.

In verifiable agent insurance, a provider posts a deposit on a blockchain and promises rules a program can check, like paying only approved merchants. A neutral verifier compares the agent's tool-call log with on-chain hashes, and if a rule was broken, the deposit pays the user.

Tested on 696 rule-breaking runs from 3 benchmarks, the checker proved 42.2%, often failing where approvals lived only in chat messages. With structured approval records, the authors estimate 83.0%, at under 1¢ of blockchain fees per session.

Design your agent so every permission, its scope, and each risky action land in a machine-readable log.

来源:Rohan Paul · x.com

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