Neural Network Node Removal for Safer LLM Security Testing
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Solution Overview
Problem
Existing methods fail to effectively identify and mitigate harmful behaviors in artificial neural networks, particularly in large language models, which can generate unsafe or misleading outputs.
Innovation Solution
Iteratively modify artificial neural networks by removing nodes and determining performance scores to select a subset of nodes with optimized performance, thereby identifying and removing harmful layers such as Hopfield layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nodes are iteratively removed from the artificial neural network to identify harmful layers, then security and safety of the network is improved, but the complexity of the network structure and the time required for testing increase
Solution Approach 1:
The patent extracts and removes specific nodes or layers from the artificial neural network that are identified as generating harmful outputs. By systematically taking out components and testing their impact, the method identifies and eliminates harmful elements while preserving the overall network structure and functionality.
Solution Approach 2:
The patent segments the neural network into individual nodes and layers that can be independently modified and tested. This segmentation allows for systematic identification of harmful components through iterative removal and performance evaluation, making the security testing process more manageable and efficient.
2Object-generated harmful factors
If nodes are iteratively removed and performance scores are determined to identify harmful layers, then harmful outputs are reduced, but the time and computational resources required for testing increase
Solution Approach 1:
The patent applies partial action by removing only specific nodes or layers that are suspected of generating harmful outputs, rather than testing or removing the entire network. This targeted approach reduces the time and computational resources required while still effectively identifying and mitigating harmful behaviors.
Solution Approach 2:
The patent uses feedback loops where performance scores are determined after each node removal, and these scores guide subsequent modification decisions. This iterative feedback process efficiently identifies harmful layers by continuously evaluating network performance and adjusting the structure based on measured outcomes.
3Reliability
If the artificial neural network is modified to remove harmful nodes, then the safety of outputs is improved, but the performance and functionality of the network may deteriorate
Solution Approach 1:
The patent applies local quality by making specific modifications to particular nodes or layers identified as harmful, rather than applying uniform changes across the entire network. This localized approach preserves the functionality and performance of safe and beneficial parts of the network while eliminating only the harmful elements.
Solution Approach 2:
The patent uses performance score feedback to guide the modification process, ensuring that nodes are removed only when performance metrics indicate they are generating harmful outputs. This feedback-driven approach maintains network performance by preserving nodes that contribute positively while removing only those that generate harmful behaviors.
Data Source
AI summary
Systems, methods, and computer program products for modifying an artificial neural network are described herein. A method comprises reading an input artificial neural network; iteratively generating a modified artificial neural network, wherein generating the modified artificial neural network comprises removing at least one node from the artificial neural network; determining a performance score for the modified artificial neural network; and selecting a subset of the nodes of the artificial neural network. Determining the performance score may comprise providing a plurality of input prompts to the modified artificial neural network; generating a plurality of outputs based on the plurality of input prompts, determining output scores for the plurality of outputs, and determining the performance score based on the output scores.