arXiv cs.AI· Yu Zheng, Qizhi Zhang·· 12 小时前AI 评分34
KITA：为 LLM 智能体提供密钥隔离阈值签名的授权架构
From Review to Authorization: Key-Isolated Threshold Signing for LLM Agents
AI 导读
研究者提出 KITA 授权架构，将用户个人签名密钥与所有阈值签名密钥份额隔离在 LLM 进程之外，使 LLM 审查结果必须经 t 个不同域签名才能授权执行。该架构基于阈值 BLS 实现进程内原型，通过 6 项系统测试验证法定人数门控与消息绑定，并用 7 个受控基线场景对比授权结果。
正文 · 原文
Abstract:Autonomous LLM agents can turn semantic judgments into consequential actions such as payments and permission changes. If the same process makes these judgments and controls a reusable signing credential, a mistaken judgment can reach execution without further approval. To make LLM review govern execution, we present KITA, an authorization architecture that keeps the user's personal signing key and every threshold signing-key share outside all LLM processes. Under threshold-signature unforgeability and our system assumptions, an adversary controlling the proposer and fewer than t reviewer-signer domains cannot produce a valid authorization for a new action without signing contributions from t distinct domains. This execution-bound authorization integrity ensures that each accepted authorization includes a share from an uncompromised domain, bound to the canonical action and released only after authenticated reviewer approval. We implement an in-process prototype of KITA with a structured-output LLM adapter and threshold BLS. Six system tests validate quorum gating and message binding, seven controlled baseline scenarios compare authorization outcomes, and cryptographic microbenchmarks measure online signing cost and its scaling behavior.
| Comments: | 18 pages |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.05901 [cs.CR] |
| (or arXiv:2609.05901v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.05901 arXiv-issued DOI via DataCite |
|
| Journal reference: | NeurIPS Workshop on Beyond Private Training: The New Landscape of AI Privacy (InfPriv 2026), Sydney, Australia |
Submission history
From: Yu Zheng [view email]
[v1]
Sat, 5 Sep 2026 05:53:51 UTC (48 KB)
[v2]
Fri, 23 Oct 2026 00:40:11 UTC (52 KB)
来源：arXiv cs.AI · arxiv.org