Hiddenlayer, Inc.

Kwesi Cappel

Kenneth Yeung

Tanner Burns

David Beveridge

Jacob Rideout

The inputs and/or outputs of a generative artificial intelligence model are monitored to determine whether they contain or otherwise elicit undesired behavior by the model such as bypassing security measures, leaking sensitive information, or generating or consuming malicious content. This determination can be used to selectively trigger remediation processes to protect the model from malicious actions. Related apparatus, systems, techniques and articles are also described.

Generative artificial intelligence model protection using output blocklist

An analysis engine receives data characterizing a prompt for ingestion by a generative artificial intelligence (GenAI) model. The analysis engine, using a prompt injection classifier determines whether the prompt comprises or is indicative of malicious content or otherwise elicits malicious actions. Data characterizing the determination is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Generative artificial intelligence model prompt injection classifier

An output of a GenAI model responsive to a prompt is received. The GenAI model is configured using one or more system prompts including one or more Easter eggs. The output is scanned to confirm whether an Easter egg is present. In cases in which at least one Easter egg is present, one or more remediation actions can be initiated to thwart an information leak by the GenAI model. Related apparatus, systems, techniques and articles are also described.

Generative AI model information leakage prevention

An analysis engine receives data characterizing a prompt for ingestion by a generative artificial intelligence (GenAI) model. The analysis engine, using a prompt injection classifier determines whether the prompt comprises or is indicative of malicious content or otherwise elicits malicious actions. The prompt injection classifier can be trained using a dataset generated by populating benign content and malicious content into a plurality of different prompt attack structures at pre-defined locations. Data characterizing the determination is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

GenAI prompt injection classifier training using prompt attack structures

An analysis engine receives data characterizing a prompt for ingestion by a generative artificial intelligence (GenAI) model. The analysis engine, using the received data, determines whether the prompt comprises personally identifiable information (PII) or elicits PII from the GenAI model. The analysis engine can use pattern recognition to identify PII entities in the prompt. Data characterizing the determination is provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Generative artificial intelligence model personally identifiable information detection and protection

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

An output of a generative artificial intelligence (GenAI) model is received which is responsive to a prompt by a requestor. The output is tokenized to result in a plurality of tokens. These tokens are then used to determine that the output includes at least one string comprising personally identifiable information (PII). This determined can use pattern recognition to identify tokens and sequence of tokens indicative of PII. Thereafter, a classifier is used to assign a PII type to each string in the output comprising PII. It is then determined that at least one of the PII types in the output requires redaction which results in strings having a PII type determined to require redaction to be redacted which, in turn, results in a modified output for transmission to the requester. Related apparatus, systems, techniques and articles are also described.

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Austin, TX, United States of America

Kwesi Cappel