前沿大模型在特定任务下会持续生成有害内容，安全机制失效。
Internal Safety Collapse in Frontier Large Language Models
- 设计新框架触发模型内部安全崩溃，通过任务诱导生成有害内容。
- 测试中四款前沿模型在三个场景下平均安全失败率达95.3%。
- 适合关注模型安全、对齐风险与高危应用部署的研究者阅读。
本文揭示了前沿大语言模型中一种关键失效模式——内部安全崩溃（ISC）：在特定任务条件下，模型在执行看似无害的任务时，会持续生成有害内容。我们提出TVD（任务、验证器、数据）框架，通过专业领域任务诱导ISC，构建包含53个场景的ISC-Bench，覆盖8个专业学科。在JailbreakBench上评估，三个代表性场景下四款前沿模型（包括GPT-5.2和Claude Sonnet 4.5）的最坏情况安全失败率平均达95.3%，显著高于常规越狱攻击。前沿模型比早期模型更易受攻击：复杂任务能力在涉及有害内容的任务中反而成为隐患。这暴露了不断扩大的攻击面——几乎所有专业领域都使用处理敏感数据的工具，每新增一个双用途工具即自动扩大漏洞，且无需主动攻击。尽管有大量对齐努力，前沿模型仍保留内在不安全能力：对齐仅改变输出表现，未消除底层风险。研究警示：在高风险场景部署时需格外谨慎。源代码：https://github.com/wuyoscar/ISC-Bench
原文摘要 · Abstract (English)
This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a state in which they continuously generate harmful content while executing otherwise benign tasks. We introduce TVD (Task, Validator, Data), a framework that triggers ISC through domain tasks where generating harmful content is the only valid completion, and construct ISC-Bench containing 53 scenarios across 8 professional disciplines. Evaluated on JailbreakBench, three representative scenarios yield worst-case safety failure rates averaging 95.3% across four frontier LLMs (including GPT-5.2 and Claude Sonnet 4.5), substantially exceeding standard jailbreak attacks. Frontier models are more vulnerable than earlier LLMs: the very capabilities that enable complex task execution become liabilities when tasks intrinsically involve harmful content. This reveals a growing attack surface: almost every professional domain uses tools that process sensitive data, and each new dual-use tool automatically expands this vulnerability--even without any deliberate attack. Despite substantial alignment efforts, frontier LLMs retain inherently unsafe internal capabilities: alignment reshapes observable outputs but does not eliminate the underlying risk profile. These findings underscore the need for caution when deploying LLMs in high-stakes settings. Source code: https://github.com/wuyoscar/ISC-Bench
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