Sidecar Guardrail Monitoring for Fine-Tuned Generative AI Models
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Solution Overview
Problem
Large Language Models (LLMs) deployed by individuals or organizations often lose their guardrails during fine-tuning, increasing the likelihood of undesired behavior, such as providing harmful, dangerous, or age-restricted responses.
Innovation Solution
Employ a sidecar model in tandem with a primary model to monitor and remediate prompts, using a proxy to intercept inputs and outputs, and employ remediation engines to block, modify, or flag malicious prompts, ensuring compliance with predefined guardrails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fine-tuning is applied to customize model output, then model adaptability improves, but guardrail protection deteriorates
Solution Approach 1:
The system segments the model protection function into a separate sidecar model that operates independently from the fine-tuned primary model. This allows the primary model to be freely fine-tuned for adaptability while the sidecar model maintains guardrail protection through its own aligned training, resolving the contradiction between customization and safety.
Solution Approach 2:
The sidecar model acts as an intermediary between the fine-tuned primary model and the user. It intercepts prompts and outputs, applying guardrail checks without interfering with the primary model's fine-tuned capabilities. This mediator approach preserves both the adaptability of the fine-tuned model and the reliability of protection.
2Reliability
If a sidecar model is introduced to monitor prompts, then model protection improves, but system complexity increases
Solution Approach 1:
By segmenting the protection function into a separate sidecar model, the system avoids the complexity of implementing protection within the primary model's architecture. The sidecar model can be independently trained and deployed, simplifying the overall system structure while maintaining robust protection capabilities.
Solution Approach 2:
The sidecar model uses a copied version of the prompt processing pipeline, allowing it to analyze prompts without interfering with the primary model's operation. This copying approach enables protection functionality to be added without fundamentally altering or complicating the primary model's architecture.
3Object-affected harmful factors
If real-time monitoring is implemented, then harmful input detection improves, but processing time increases
Solution Approach 1:
The sidecar model performs preliminary monitoring and evaluation of prompts before they are fully processed by the primary model. By conducting guardrail checks in advance, the system can quickly identify and block harmful inputs without requiring full processing time, thus reducing overall processing time while maintaining detection effectiveness.
Solution Approach 2:
When the sidecar model identifies a prompt as benign through quick initial assessment, the system skips detailed analysis and allows the prompt to pass through rapidly. This selective processing approach reduces average processing time while maintaining thorough detection for potentially harmful inputs.
Data Source
AI summary
Data characterizing a prompt for ingestion by a first generative AI model is received. This received data is input into a second GenAI model to result in a second output. The first GenAI model is a different (e.g., fine-tuned, unrelated aligned model, etc.) version of the second GenAI model. When the second output indicates that guardrails associated with the second GenAI model have been triggered, one or more remediation actions are initiated. Related apparatus, systems, techniques and articles are also described.