US 12,314,380 B2
Scanning and detecting threats in machine learning models
Tanner Burns, Austin, TX (US); Chris Sestito, Austin, TX (US); James Ballard, Fredricksburg, VA (US); Thomas Bonner, Steeple Claydon (GB); Marta Janus, Twickenham (GB); and Eoin Wickens, Skibbereen (IE)
Assigned to HiddenLayer, Inc., Austin, TX (US)
Filed by HiddenLayer, Inc., Austin, TX (US)
Filed on Feb. 23, 2023, as Appl. No. 18/113,444.
Prior Publication US 2024/0289436 A1, Aug. 29, 2024
Int. Cl. G06F 21/53 (2013.01); G06F 21/56 (2013.01)
CPC G06F 21/53 (2013.01) [G06F 21/56 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method for scanning a machine learning model for threats, comprising:
receiving, by a scanning module, data for a machine learning model, the scanning module stored on a first server, the data associated with model parameters and received before execution of the machine learning model;
performing, by the scanning module, a plurality of checks based on the received machine learning model data, the checks performed while the machine learning model is not executing,
the performing comprising:
determining weights and biases of the machine learning model;
determining an expected entropy for the machine learning model based on clustering of known machine learning models guided by calculated entropy;
determining, based on the weights and biases, an actual entropy for the machine learning model; and
calculating the difference between the expected entropy and the actual entropy;
identifying, by the scanning module and based on the calculated difference, whether the machine learning model includes a threat within the machine learning model based on results of the plurality of checks; and
adding, by the scanning module and based on the identifying, an indicator to the machine learning model characterizing actual, potential or detected threats within the machine learning model.