The paper introduces Rep2Text, a method that reconstructs text from a single hidden representation of a large language model. Experiments show that for short passages (~16 words) about half the words can be recovered while preserving overall meaning, and even longer texts retain recoverable main ideas. These results suggest that hidden representations encode more information than previously thought, raising concerns about privacy.
arXiv:2608.29943v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to re...
The paper introduces Sparse One-hot Discrete Adam (SODA), a white‑box search algorithm that can reconstruct textual inputs from partial knowledge of a language model’s output logits. SODA can recover natural sequences of up to 17 tokens with no false positives, outperforming prior methods by a factor of four in exact‑match reconstruction. The authors demonstrate that input length and vocabulary size are more critical to inversion difficulty than model size, and provide proof‑of‑concept security applications, including recovering backdoor triggers and personally identifiable information from a poisoned LLM and synthetic user‑chatbot interactions.
The paper introduces Dual-Embedding Watermarking (DEW), a semantic watermarking technique for large language models that combines contextual and token-level embeddings. DEW applies algebraic vector-space operations to generate a watermark signal that remains robust to paraphrasing and translation, while obfuscating the signal with pseudo-random matrices seeded by a secret key. Experiments demonstrate state‑of‑the‑art robustness, especially against translation, with minimal computational overhead and preserved text quality at lower watermark strengths.
arXiv:2508.03829v3 Announce Type: replace Abstract: The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To addres...
arXiv:2610.31995v1 Announce Type: cross Abstract: As large language models (LLMs) face growing demands for data removal driven by regulatory compliance, copyright concerns, and privacy protection, ap...
arXiv:2605.12890v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...
arXiv:2610.27817v1 Announce Type: new Abstract: Every prompt sent to a hosted language model is readable by whoever runs it. We study whether the prompt can remain on the client while the host receiv...
The paper introduces a rolewise decomposition framework to analyze large language model steganography beyond traditional end‑to‑end recovery metrics. By separately examining how private messages are encoded in the text, what decoders actually recover, and what can be deterministically verified, the authors uncover hidden issues in the encoding and decoding processes. Their study of 7,200 encoder responses reveals 168 verifiable encodings missed by decoders and 706 responses where private messages were reconstructed via unintended patterns, highlighting limitations of end‑to‑end evaluation.
arXiv:2607. 00852v1 Announce Type: cross Abstract: This work studies the hidden-state inversion problem: recovering the original input token sequence of a decoder-only language model from its last-layer hidden states.
arXiv:2610.27653v1 Announce Type: cross Abstract: Watermarking has emerged as a key tool for identifying text generated by large language models (LLMs), with current schemes embedding statistical sig...
With the widespread applications of large language models (LLMs), privacy-preserving inference has become increasingly essential for sensitive queries. To balance privacy and utility, a series of ligh...
arXiv:2608.27899v1 Announce Type: cross Abstract: With the growing prevalence of large language model (LLM) generated content, watermarking is considered a promising approach for attributing text to...