In the rapidly advancing field of artificial intelligence, the concept of Red-Teaming or Jailbreaking large language models (LLMs) has emerged as a crucial area of study. This approach is especially significant in terms of assessing and enhancing the safety and robustness of these models. This paper investigates the intricate consequences of such modifications through model editing, uncovering a complex relationship between enhancing model accuracy and preserving its ethical integrity. Our in-depth analysis reveals a striking paradox: while injecting accurate information is crucial for model reliability, it can paradoxically destabilize the model's foundational framework, resulting in unpredictable and potentially unsafe behaviors. Additionally, we propose a benchmark dataset NicheHazardQA to investigate this unsafe behavior both within the same and cross topical domain. This aspect of our research sheds light on how the edits, impact the model's safety metrics and guardrails. Our findings show that model editing serves as a cost-effective tool for topical red-teaming by methodically applying targeted edits and evaluating the resultant model behavior.

翻译：在人工智能快速发展的领域中，对大型语言模型进行红队测试或越狱这一概念已成为关键研究方向，尤其在评估和提升模型安全性及鲁棒性方面具有重要意义。本文通过模型编辑手段探究此类修改引发的复杂后果，揭示了增强模型准确性与维护其伦理完整性之间错综复杂的关联。深度分析呈现出一个显著悖论：虽然注入准确信息对保障模型可靠性至关重要，但这反而可能动摇模型的基础框架，引发不可预测且潜在危险的行为。此外，我们提出了基准数据集NicheHazardQA，用于在同领域及跨领域话题中研究此类不安全行为。该研究方向揭示了模型编辑如何影响安全指标与防护机制。实验结果表明，通过系统性地实施定向编辑并评估模型行为变化，模型编辑可成为一种经济高效的主题红队测试工具。