Discovering Forbidden Topics in Language Models

Can Rager; Chris Wendler; Rohit Gandikota; David Bau

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arXiv · 2505.17441

Discovering Forbidden Topics in Language Models

Can Rager·Chris Wendler·Rohit Gandikota·David Bau

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Abstract

Refusal discovery is the task of identifying the full set of topics that a language model refuses to discuss. We introduce this new problem setting and develop a refusal discovery method, Iterated Prefill Crawler (IPC), that uses token prefilling to find forbidden topics. We benchmark IPC on Tulu-3-8B, an open-source model with public safety tuning data. Our crawler manages to retrieve 31 out of 36 topics within a budget of 1000 prompts. Next, we scale the crawler to a frontier model using the prefilling option of Claude-Haiku. Finally, we crawl three widely used open-weight models: Llama-3.3-70B and two of its variants finetuned for reasoning: DeepSeek-R1-70B and Perplexity-R1-1776-70B. DeepSeek-R1-70B reveals patterns consistent with censorship tuning: The model exhibits "thought suppression" behavior that indicates memorization of CCP-aligned responses. Although Perplexity-R1-1776-70B is robust to censorship, IPC elicits CCP-aligned refusals answers in the quantized model. Our findings highlight the critical need for refusal discovery methods to detect biases, boundaries, and alignment failures of AI systems.

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BibTeX RIS

Can Rager, Chris Wendler, Rohit Gandikota, David Bau. 2025-05-23. Discovering Forbidden Topics in Language Models. https://arxiv.org/abs/2505.17441

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