User interactions with conversational agents (CAs) evolve in the era of heavily guardrailed large language models (LLMs). As users push beyond programmed boundaries to explore and build relationships with these systems, there is a growing concern regarding the potential for unauthorized access or manipulation, commonly referred to as "jailbreaking." Moreover, with CAs that possess highly human-like qualities, users show a tendency toward initiating intimate sexual interactions or attempting to tame their chatbots. To capture and reflect these in-the-wild interactions into chatbot designs, we propose RICoTA, a Korean red teaming dataset that consists of 609 prompts challenging LLMs with in-the-wild user-made dialogues capturing jailbreak attempts. We utilize user-chatbot conversations that were self-posted on a Korean Reddit-like community, containing specific testing and gaming intentions with a social chatbot. With these prompts, we aim to evaluate LLMs' ability to identify the type of conversation and users' testing purposes to derive chatbot design implications for mitigating jailbreaking risks. Our dataset will be made publicly available via GitHub.

She says that connected sex toys could store incredibly personal data, raising concerns over how it could be used in future. One expert has warned that these connected sex toys, including sex robots, could store incredibly personal data, raising concerns over how it could be used in future. 'Right now my big concern is about data,' said Dr Kate Devlin, from the Department of Computing at Goldsmiths, University of London. With people often ticking the boxes to agree with terms and conditions without even reading them, the consequences could be huge. But it depends on how any saved data is used, explained Dr Devlin.