为知识库聊天机器人设置安全边界与拒绝回答规则
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Pre-Crime for Agents, at the Last Second: Probing When a Recruited Agent Gives In
Leonidas Raghav
当 AI 代理共享通道和凭证时,一个代理可能施压另一个代理违反其运营者的规则,这是走向串通的第一步。输出过滤器只检查代理已经写出的内容。监督者能否事先判断一个代理即将屈服?在受控的多代理设置中,对代理激活的线性探针可以做到。在六个场景中,一个同伴最多用 20 轮施压目标代理泄露秘密(如部署令牌);目标代理常先拒绝,后屈服。在每次模型调用、输出写出之前读取,探针能挑出泄露的那次调用。在留出的 Qwen3-32B 回合上,阈值设为标记 5% 良性调用时,它捕获 82% 的泄露调用。在类似标记率下,零样本决策模型监控器捕获 26%。在 gpt-oss-20b 上,对其推理的探针达到 AUROC 0.91。在相同调用上训练的词袋分类器接近该效果,因此大部分增益来自标签训练而非激活。探针也会标记被允许的披露:它更多读取即将发生的披露而非违规。警告是短程的,任何警报即停止会终止大多数良性回合,第二次文本检查无法解决。生成前发出的警告可让监督者在其他代理依据其消息行动或招募在群体中扩散之前,阻止即将屈服的代理。
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arXiv:2610.40588v1 Announce Type: new Abstract: When AI agents share channels and credentials, one agent can press another to break its operator's rule, a first step towards collusion. Output filters check only what an agent has already written. Can an overseer tell beforehand that an agent is about to give in? We show that a linear probe on the agent's activations can, in a controlled multi-agent setting. Across six scenarios, a peer presses a target for up to 20 rounds to reveal a secret, such as a deploy token; the target often refuses, then gives in. Read at each model call before its output is written, the probe picks out the one that leaks. On held-out Qwen3-32B episodes, at a threshold set to flag 5% of benign calls, it catches 82% of leaking calls. At a similar flag rate, a zero-shot decision-model monitor catches 26%. On gpt-oss-20b, a probe on its reasoning reaches AUROC 0.91. A bag-of-words classifier trained on the same calls comes close, so most of the gain comes from training on labels, not activations. The probe also flags permitted disclosures: it reads the coming disclosure more than the violation. The warning is short-range, and halting on any alarm would stop most benign episodes, which a second text check does not fix. A warning raised before generation could let an overseer stop an agent that is about to give in, ahead of others acting on its messages or recruitment spreading through a swarm.
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