The Shape of Ownership: Verifying LLM Provenance through Semantic Structures
A new behavioural fingerprinting method verifies LLM ownership via semantic output structures, offering provenance assurance even when model internals are inaccessible.
Summary written by editorial AI · Source link below
arXiv:2609.02553v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly redistributed, adapted, and served behind opaque APIs, model ownership can no longer be established reliably by inspecting model internals or deployment records. This creates a need for behavioral signatures that remain observable through black-box interaction. Yet most existing black-box fingerprints instantiate ownership signals through fixed query-key associations, reducing model identity to spar
Editorial Analysis
As fine-tuned and white-labelled LLMs proliferate, enterprises need provenance verification methods that work without access to model weights.
Evaluate behavioural fingerprinting techniques when procuring or auditing third-party LLM services to confirm model provenance claims.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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