DETERMINING AND MITIGATING ARTIFICIAL INTELLIGENCE MODEL VULNERABILITIES
개요
발명자
Calin Miron; Marian Radu; Arnd Korn
IPC 분류
CPC 분류
The present disclosure provides techniques for determining and mitigating AI model vulnerabilities. A processing device generates, via a first AI model, a plurality of prompt variations based on an indication of a vulnerability. The processing device determines that a second AI model is vulnerable to the vulnerability based on at least one prompt variation in the plurality of prompt variations. The processing device generates a plurality of filter variations based on a plurality of filters and the at least one prompt variation. The processing device tests the plurality of filter variations and the at least one prompt variation on the second AI model. The processing device generates, based on the testing, a report indicative of an effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the second AI model.
원문 (중국어)
The present disclosure provides techniques for determining and mitigating AI model vulnerabilities. A processing device generates, via a first AI model, a plurality of prompt variations based on an indication of a vulnerability. The processing device determines that a second AI model is vulnerable to the vulnerability based on at least one prompt variation in the plurality of prompt variations. The processing device generates a plurality of filter variations based on a plurality of filters and the at least one prompt variation. The processing device tests the plurality of filter variations and the at least one prompt variation on the second AI model. The processing device generates, based on the testing, a report indicative of an effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the second AI model.