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：在快速发展的人工智能领域，对大型语言模型（LLMs）进行红队测试（Red-Teaming）或越狱（Jailbreaking）的概念已成为一个关键研究领域。这种方法在评估和增强这些模型的安全性与鲁棒性方面尤为重要。本文通过模型编辑探究此类修改的复杂后果，揭示了提升模型准确性与维护其伦理完整性之间错综复杂的关系。我们的深入分析揭示了一个显著悖论：虽然注入准确信息对模型可靠性至关重要，但这一过程却可能破坏模型的基础框架，导致不可预测且潜在不安全的行為。此外，我们提出一个基准数据集NicheHazardQA，以在相同及跨主题领域内研究这种不安全行为。本研究的这一方面揭示了编辑如何影响模型的安全指标与防护措施。我们的研究结果表明，通过系统性地应用有针对性的编辑并评估由此产生的模型行为，模型编辑可成为一种成本效益高的主题红队测试工具。