Large Foundation Model Misuse Prevention via Parallel Moderation
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Solution Overview
Problem
Large foundation models, such as LLMs, are vulnerable to misuse through 'jailbreaking' attacks, where malicious actors override policy filters, necessitating frequent and autonomous updates of content, quality, relevance, and style filters to ensure safety and compliance with legal and societal norms.
Innovation Solution
A defensive system comprising a question prompt (QP) module, a second large foundation model, and a memory module is deployed in parallel to the main model, acting as a wrapper to generate moderation outputs that identify and block harmful content, with the ability to update policies based on query and response pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If policy filters are frequently updated to counter jailbreaking attacks, then the reliability of content safety is improved, but the device complexity and operational burden increase
Solution Approach 1:
The system performs preliminary classification of user inputs against stored query-response pairs before the main LLM processes them. This advance filtering action identifies potentially harmful patterns early, allowing the system to block malicious requests before they reach the vulnerable foundation model, thus maintaining safety without requiring constant filter updates
Solution Approach 2:
The system creates a simplified copy of the safety checking function using stored query-response pairs that represent known harmful patterns. This copy acts as a lightweight filter that replicates the essential safety function without requiring the complexity of continuously updated sophisticated filters, reducing operational burden while maintaining reliability
2Ease of operation
If generic filters are used for content moderation, then the ease of operation is improved, but the adaptability to organization-specific policies decreases
Solution Approach 1:
The system implements local quality by storing and applying organization-specific query-response pairs that are tailored to specific domains, industries, or organizational policies. Each stored pair represents localized safety requirements for particular contexts, allowing the same system to adapt to different organizational needs without changing the overall architecture or operational complexity
Solution Approach 2:
The system achieves universality by using a single modular architecture that can handle both generic safety filtering and organization-specific policy enforcement. The stored query-response pairs serve multiple functions: they act as classification queries, safety filters, and policy enforcement mechanisms across different organizational contexts, eliminating the need for separate systems for generic and customized filtering
Data Source
AI summary
A system prevents misuse of a large language foundation model and includes a moderation module, a second large foundation model, and a memory module. The moderation module is configured to receive an input prompt and to generate a moderation output. The second large foundation model is configured to process the input prompt and the moderation output together to get a response. The response is communicated to at least one of an input filter or an output filter associated with the large foundation model to prevent misuse.