AI Model Snapshot Recovery for Poisoned Inference Deployments

Solution Overview

Problem

AI models can be poisoned by malicious training data, leading to untrustworthy inferences that impact downstream consumers, requiring costly re-training and affecting hardware deployments, which can result in inefficient resource use and suboptimal hardware deployments.

Innovation Solution

Implement a system that periodically captures AI model snapshots to restore untainted models, estimates performance costs of poisoned inferences, and deploys additional hardware components to mitigate the impact of poisoned inferences, thereby reducing computational and financial costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the AI model is re-trained from scratch to remove poisoned training data, then the reliability of the AI model is improved, but the computational resources and time required increase significantly

Engineering Contradiction:

Improvereliability of AI modelVSAvoidtime required for re-training

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by creating snapshots of the AI model at regular intervals during the training process. These snapshots capture the model state before poisoned training data is introduced, enabling quick restoration without re-training from scratch. This preliminary preparation resolves the contradiction by having recovery points ready in advance, reducing both time and computational resources needed for reliability restoration.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of the AI model at specific training checkpoints through snapshots. When poisoning is detected, instead of re-training the entire model from scratch, the system restores a previous copy that was created before the poisoned data was introduced. This copying approach maintains reliability while significantly reducing the time and computational resources required compared to full re-training.

Inventive Principle:

Principle #26Copying

2Reliability

If the AI model is re-trained to remove poisoned training data, then the reliability of the AI model is improved, but the computational resources required increase significantly

Engineering Contradiction:

Improvereliability of AI modelVSAvoidcomputational resources for re-training

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by creating snapshots of the AI model at regular intervals during the training process. These snapshots capture the model state before poisoned training data is introduced, enabling quick restoration without re-training from scratch. This preliminary preparation resolves the contradiction by having recovery points ready in advance, reducing both time and computational resources needed for reliability restoration.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of the AI model at specific training checkpoints through snapshots. When poisoning is detected, instead of re-training the entire model from scratch, the system restores a previous copy that was created before the poisoned data was introduced. This copying approach maintains reliability while significantly reducing the time and computational resources required compared to full re-training.

Inventive Principle:

Principle #26Copying

3Reliability

If hardware components are added to mitigate the impact of poisoned inferences, then the performance cost is reduced, but the device complexity increases

Engineering Contradiction:

Improveperformance costVSAvoiddevice complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system extracts and isolates the poisoned inference components from the overall system. By identifying specific hardware components or model layers responsible for the poisoned inferences, the system can remove or neutralize only those specific elements rather than adding comprehensive mitigation hardware throughout the entire system. This selective extraction approach reduces performance cost while minimizing the increase in device complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12626160B2Managing impact of poisoned inferences on deployments of hardware to downstream consumers

Publication Date: 2026.05.12 DELL PROD LP

AI summary

Methods and systems for managing impact of inferences provided to inference consumers on decisions made by the inference consumers are disclosed. Poisoned training data may be introduced and used to train an AI model, which may then poison the AI model and lead to poisoned inferences being provided to the inference consumers. Inference consumers may deploy hardware to customers based on the poisoned inferences. To determine whether to modify the deployed hardware, a performance cost associated with the deployed hardware may be obtained. The performance cost may indicate a deviation between operation of the deployed hardware and operation of hardware that may have been deployed if an unpoisoned inference was used. If the performance cost meets a performance cost threshold, at least one additional hardware component may be deployed to the customer.