Unconditional Token Forcing: Extracting Text Hidden Within LLM
Jakub Hościłowicz, Paweł Popiołek, Jan Rudkowski, Jędrzej Bieniasz, Artur Janicki
DOI: http://dx.doi.org/10.15439/2024F4511
Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 621–624 (2024)
Abstract. With the help of simple fine-tuning, one can artificially embed hidden text into large language models (LLMs). This text is revealed only when triggered by a specific query to the LLM. Two primary applications are LLM fingerprinting and steganography. In the context of LLM fingerprinting, a unique text identifier (fingerprint) is embedded within the model to verify licensing compliance. In the context of steganography, the LLM serves as a carrier for hidden messages that can be disclosed through a designated trigger.
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