AI Model Pipeline Poisoning Analysis for Attacker Goal Detection
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Solution Overview
Problem
Existing AI models are vulnerable to poisoning by malicious entities introducing tainted training data, leading to untrustworthy inferences and inefficient resource consumption for re-training, which disrupts computer-implemented services.
Innovation Solution
Implementing AI model management systems that include snapshotting and directed modification analysis to identify and mitigate poisoned training data, allowing for computationally efficient restoration and prevention of poisoning, thereby reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are re-trained to remove poisoned training data, then model reliability is improved, but computing resource consumption increases
Solution Approach 1:
The system performs directed modification analysis on training data before it is used to train AI models. By detecting poisoned data in advance and preventing its incorporation, the system avoids the need for costly re-training operations, thus maintaining model reliability while minimizing computing resource consumption
Solution Approach 2:
The system extracts and removes poisoned training data from the training pipeline through directed modification analysis. By identifying and excluding tainted data before model training, the system prevents poisoning without requiring full re-training, thereby reducing computing resource consumption while maintaining model reliability
2Reliability
If comprehensive training data screening is implemented, then poisoned data detection is improved, but system complexity increases
Solution Approach 1:
The system applies directed modification analysis specifically to training data that shows signs of synthetic generation or unusual modification patterns. Rather than screening all training data uniformly, the system targets local regions of the data pipeline where poisoning is most likely to occur, maintaining detection effectiveness while reducing overall system complexity
Data Source
AI summary
Methods and systems for managing an artificial intelligence (AI) model are disclosed. An AI model may be part of an evolving AI model pipeline, the processes of which may include obtaining training data from data sources used to update the AI model. An attacker may introduce poisoned training data via one or more of the data sources as a form of attack on the AI model. When the poisoned training data is identified, the poisoned training data may be compared to existing training data to determine the attacker's goal. Based on the attacker's goal, remedial actions may be performed that may update operation of pipeline. The updated operation of the pipeline may reduce the computational expense for remediating impact of the poisoned training data, and may reduce the likelihood of obtaining poisoned training data in the future.