arXiv AI By Matthew Finlayson, Andreas Grivas, Xiang Ren, Swabha Swayamdipta

Token Rankings are Unforgeable Language Model Signatures

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arXiv:2606. 04459v1 Announce Type: cross Abstract: Language model parameters are known to impose unique (to each model) geometric constraints on their logit outputs, which serves as a signature that identifies the model, but also leaks the model's final layer parameters when an API distributes logits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jul 14

One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions

arXiv:2607. 10252v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly consumed through opaque serving chains - API aggregators, resellers, and inference providers - in which the client has no technical means to confirm that the model answering is the model advertised, and recent audits show that a substantial fraction of commercial endpoints deviate from the vendor's reference weights.

By Tomas Bruckner
arXiv AI
Sep 16

Permutation-Based Stegomalware in Large Language Models: Threats and Countermeasures

The paper investigates the threat of stegomalware—malicious payloads hidden in large language model weights—and explores permutation symmetry as a defense. It demonstrates that carefully chosen permutations can neutralize stegomalware across all weights, unlike prior methods that left many weights untouched. Conversely, the same symmetry can be exploited to embed malware losslessly, without retraining or payload-specific extraction scripts, while only minimally affecting model performance.

By Danny Wood, James Stringer