  Amid growing concerns over the ease of theft and misuse of Large Language
Models (LLMs), the need for fingerprinting models has increased.
Fingerprinting, in this context, means that the model owner can link a given
model to their original version, thereby identifying if their model is being
misused or has been completely stolen. In this paper, we first define a set
five properties a successful fingerprint should satisfy; namely, the
fingerprint should be Transparent, Efficient, Persistent, Robust, and
Unforgeable. Next, we propose Chain & Hash, a new, simple fingerprinting
approach that implements a fingerprint with a cryptographic flavor, achieving
all these properties. Chain & Hash involves generating a set of questions (the
fingerprints) along with a set of potential answers. These elements are hashed
together using a secure hashing technique to select the value for each
question, hence providing an unforgeability property-preventing adversaries
from claiming false ownership. We evaluate the Chain & Hash technique on
multiple models and demonstrate its robustness against benign transformations,
such as fine-tuning on different datasets, and adversarial attempts to erase
the fingerprint. Finally, our experiments demonstrate the efficiency of
implementing Chain & Hash and its utility, where fingerprinted models achieve
almost the same performance as non-fingerprinted ones across different
benchmarks.
