Openness is critical for the advancement of science. In particular, recent rapid progress in AI has been made possible only by various open-source models, datasets, and libraries. However, this openness also means that technologies can be freely used for socially harmful purposes. Can open-source models or datasets be used for malicious purposes? If so, how easy is it to adapt technology for such goals? Here, we conduct a case study in the legal domain, a realm where individual decisions can have profound social consequences. To this end, we build EVE, a dataset consisting of 200 examples of questions and corresponding answers about criminal activities based on 200 Korean precedents. We found that a widely accepted open-source LLM, which initially refuses to answer unethical questions, can be easily tuned with EVE to provide unethical and informative answers about criminal activities. This implies that although open-source technologies contribute to scientific progress, some care must be taken to mitigate possible malicious use cases. Warning: This paper contains contents that some may find unethical.

翻译：开放性对科学进步至关重要。特别是，人工智能领域的近期快速发展，只有在各种开源模型、数据集和库的支持下才成为可能。然而，这种开放性也意味着技术可以被自由用于对社会有害的目的。开源模型或数据集能否被用于恶意目的？如果是，调整技术以实现此类目标有多容易？在此，我们以法律领域为案例进行研究，该领域中个人决策可能产生深远的社会影响。为此，我们构建了EVE数据集，包含基于200个韩国判例的200个关于犯罪活动的问题及其对应答案。我们发现，一个最初拒绝回答不道德问题的广泛接受的开源大语言模型，可以通过EVE轻松调整，从而提供关于犯罪活动的不道德且具有信息性的回答。这意味着，尽管开源技术促进了科学进步，但仍需谨慎应对可能的恶意使用案例。警告：本文包含某些读者可能认为不道德的内容。