arXiv cs.AI· Cheng Hua, Taro Tsuchiya, Nicolas Christin, Giulia Fanti·· 3 天前AI 评分30
可验证智能体保险(VAI):用智能合约与验证器为 AI 智能体行为兜底
Holding Agents Accountable with Verifiable Agent Insurance
AI 导读
研究者提出可验证智能体保险(VAI),让智能体提供方把程序化策略承诺写入不可篡改账本的智能合约并附资金托管,用户发现违规后可请验证器审查智能体执行轨迹,验证通过即从托管资金获赔。该框架已在以太坊 layer-2 网络端到端实现,在三个主流智能体基准上对含违规的运行检出率为 42.2%;若让智能体显式输出用户授权与安全相关结构化对象,检出率可升至 83.0%。
正文 · 原文
Abstract:While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused on preventing such misbehavior, we consider how to recover after an agent misbehaves. We propose verifiable agent insurance (VAI), a framework for agent providers to commit financially to promises, a list of programmatically-specified policies that an agent is meant to follow. These promises are embedded in a smart contract on an immutable ledger (e.g., a blockchain) to prevent tampering, along with a monetary escrow. A user who detects a violation of committed promises can prompt a verifier to review agent execution traces, which are logged through the agent harness's shared tool-execution path, without instrumenting each tool separately. Upon verification of the violation, the agent provider's escrow funds are paid to the reporting user. We instantiate VAI as an end-to-end implementation on an Ethereum layer-2 network, and evaluate its coverage on three prominent agent benchmarks, two of which we re-annotated to obtain task-level policy-compliance labels. Currently, many agents do not emit user authorizations and security-relevant information as explicit, structured data objects -- rather, they embed the data needed to verify policy violations in natural language exchanges. Even so, VAI manages to detect violations in 42.2% of runs containing violations across three benchmarks. By augmenting agents to explicitly emit such objects, violation detection rises to 83.0% of traces. Our findings suggest that for improved auditability, agents should be co-designed with their security policies to emit explicit, structured records of user authorization and security-relevant actions.
| Comments: | 21 pages, 1 figure, 7 tables |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18230 [cs.CR] |
| (or arXiv:2610.18230v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18230 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Cheng Hua [view email]
[v1]
Tue, 13 Oct 2026 12:01:11 UTC (58 KB)
来源:arXiv cs.AI · arxiv.org