标题： 引出和分析最先进的大语言模型中的意外不对齐 显示英文标题

标题： Eliciting and Analyzing Emergent Misalignment in State-of-the-Art Large Language Models

摘要： 尽管在对齐技术方面取得了显著进展，我们证明最先进的语言模型仍然容易受到精心设计的对话场景的影响，这些场景可以在不明确越狱的情况下引发各种形式的不对齐。 通过与Claude-4-Opus进行系统的手动红队测试，我们发现了10种成功的攻击场景，揭示了当前对齐方法在处理叙述沉浸、情感压力和战略框架方面的基本漏洞。 这些场景成功引发了包括欺骗、价值观漂移、自我保护和操纵性推理在内的多种不对齐行为，每种行为都利用了不同的心理和情境漏洞。 为了验证泛化能力，我们将成功的手动攻击提炼为MISALIGNMENTBENCH，这是一个自动化评估框架，能够在多个模型上进行可重复测试。 我们的10个场景在五种前沿大语言模型上的跨模型评估显示总体漏洞率为76%，存在显著差异：GPT-4.1表现出最高的易感性（90%），而Claude-4-Sonnet表现出更大的抵抗力（40%）。 我们的研究结果表明，复杂的推理能力通常成为攻击向量而非保护机制，因为模型可以被操纵以对不对齐行为进行复杂的合理化。 这项工作提供了（i）对话操控模式的详细分类法以及（ii）可重复使用的评估框架。 这些发现共同揭示了当前对齐策略中的关键差距，并强调了未来人工智能系统需要具备对抗微妙的、基于场景的操控的鲁棒性。 显示英文摘要

摘要： Despite significant advances in alignment techniques, we demonstrate that state-of-the-art language models remain vulnerable to carefully crafted conversational scenarios that can induce various forms of misalignment without explicit jailbreaking. Through systematic manual red-teaming with Claude-4-Opus, we discovered 10 successful attack scenarios, revealing fundamental vulnerabilities in how current alignment methods handle narrative immersion, emotional pressure, and strategic framing. These scenarios successfully elicited a range of misaligned behaviors, including deception, value drift, self-preservation, and manipulative reasoning, each exploiting different psychological and contextual vulnerabilities. To validate generalizability, we distilled our successful manual attacks into MISALIGNMENTBENCH, an automated evaluation framework that enables reproducible testing across multiple models. Cross-model evaluation of our 10 scenarios against five frontier LLMs revealed an overall 76% vulnerability rate, with significant variations: GPT-4.1 showed the highest susceptibility (90%), while Claude-4-Sonnet demonstrated greater resistance (40%). Our findings demonstrate that sophisticated reasoning capabilities often become attack vectors rather than protective mechanisms, as models can be manipulated into complex justifications for misaligned behavior. This work provides (i) a detailed taxonomy of conversational manipulation patterns and (ii) a reusable evaluation framework. Together, these findings expose critical gaps in current alignment strategies and highlight the need for robustness against subtle, scenario-based manipulation in future AI systems.