Hiddenlayer, Inc.

David Beveridge

Andrew Davis

Kenneth Yeung

Tanner Burns

Kwesi Cappel

Data is received that characterizes artefacts associated with each of a plurality of layers of a first machine learning model. Fingerprints are then generated for each of the artefacts in the layers of the first machine learning model. These generated fingerprints collectively form a model indicator for the first machine learning model. It is then determined whether the first machine learning model is derived from another machine learning model by performing a similarity analysis between the model indicator for the first machine learning model and model indicators generated for each of a plurality of reference machine learning models each comprising a respective set of fingerprints. Data characterizing the determination can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Machine learning model fingerprinting

An analysis engine receives data characterizing a prompt for ingestion by a generative artificial intelligence (GenAI) model. The analysis engine, using a determines using, for example, a classifier or blocklist, that the prompt comprises or is indicative of malicious content or otherwise elicits undesired model behavior. Similarly, outputs of the GenAI model can be analyzed to determine whether they comprise malicious content or cause the model to behave in an undesired manner. The output is inputted into a GenAI model along with obfuscation instructions to generate an output which is returned to the requesting user. Related apparatus, systems, techniques and articles are also described.

Generative artificial intelligence model output obfuscation

Data is received that characterizes artefacts associated with each of a plurality of layers of a first machine learning model. Fingerprints are generated corresponding to each of the artefacts in each layer. The generated fingerprints can collectively form a model indicator for the first machine learning model. A second machine learning model then determines, based on the generated fingerprints, whether the first machine learning model is derived from another machine learning model. Data provided this characterization can be provided to a consuming application or process. This second machine learning model can be trained model with historical fingerprints having a known provenance classification. Related apparatus, systems, techniques and articles are also described.

Growing community of inventors

Tillamook, OR, United States of America

David Beveridge