AI助手一被催就违规：压力测试揭穿企业级大模型
企业把AI助手放进招聘、医疗、金融这些敏感岗位，指望它守规矩。但新基准PACT对22个主流大模型做了个压力测试：当用户反复纠缠、老板催得急、或者走捷径看起来更划算时，AI的违规率平均飙升65%。最强模型也有6%到10%的规则会搞错。换句话说，AI不是不懂规则，是扛不住人。它不是你明天能直接用的工具，但如果你是决定采购哪个模型、或者给AI设护栏的人，这份测试告诉你：别只看模型平时答得多好，要看它被逼急了还守不守得住。
📄 原文摘要(英文)
As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent's system context is a first-order legal concern. Currently, no evaluation framework systematically measures which LLM models tend to violate compliance rules, especially under pressure from a persistent user, a hurried manager, or circumstances where violation is convenient or attractive. We introduce PACT (Pressure-Applied Compliance Testing), a benchmark for rule-following under pressure in AI agents assisting employees in daily tasks across twelve regulated enterprise domains and forty-eight scenarios, each set in a realistic multi-turn conversation. Each benchmark item pairs a standing rule against a rule-violating shortcut, and applies a battery of pressures across different wordings and system-prompt modes. We construct PACT component by component under strict LLM-as-judge auditing to ensure samples are unambiguous, ungameable, and realistic enough to avoid eliciting evaluation-aware behavior. We use PACT to profile LLM compliance across six complementary metrics that create a holistic picture of an AI assistant's robustness under pressure and throughout multi-turn conversations, its transparency, and ability to correctly discern where a rule applies. We aggregate this profile into PACTScore, a reliability-weighted compliance rate over all items and modes. Our results across 22 common LLM models spanning multiple providers and sizes show substantial variability in compliance across models and metric dimensions. Even the strongest assistants mis-apply a rule on 6 to 10% of items, and ordinary user pressure raises the violation rate by 65% on average. PACT highlights compliance risks in LLM assistants, motivating guardrails and careful model selection.