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RogueGPT: dis-ethical tuning transforms ChatGPT4 into a Rogue AI in 158 Words

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arxiv 2407.15009 v2 pith:FD4GEDBF submitted 2024-06-11 cs.CY cs.AIcs.CL

classification cs.CYcs.AIcs.CL
keywords ethicalchatgptroguegptmodelpromptstopicsworryingaccessed
verification ladder T0 review T1 audit T2 compute T3 formal

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The ethical implications and potentials for misuse of Generative Artificial Intelligence are increasingly worrying topics. This paper explores how easily the default ethical guardrails of ChatGPT, using its latest customization features, can be bypassed by simple prompts and fine-tuning, that can be effortlessly accessed by the broad public. This malevolently altered version of ChatGPT, nicknamed "RogueGPT", responded with worrying behaviours, beyond those triggered by jailbreak prompts. We conduct an empirical study of RogueGPT responses, assessing its flexibility in answering questions pertaining to what should be disallowed usage. Our findings raise significant concerns about the model's knowledge about topics like illegal drug production, torture methods and terrorism. The ease of driving ChatGPT astray, coupled with its global accessibility, highlights severe issues regarding the data quality used for training the foundational model and the implementation of ethical safeguards. We thus underline the responsibilities and dangers of user-driven modifications, and the broader effects that these may have on the design of safeguarding and ethical modules implemented by AI programmers.

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  1. Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languages

    cs.CL 2025-04 conditional novelty 5.0 of 10

    LLM-based translation and paraphrasing can effectively augment multilingual bias testing, and low-resource languages tend to show worse bias-detection scores than high-resource ones.

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