Dynamic LLM Guardrails for Security and Latency Control
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Solution Overview
Problem
Large language models (LLMs) pose security and privacy risks due to their training on sensitive data and vulnerability to attacks, with existing solutions being inefficient and difficult to implement.
Innovation Solution
A computer-implemented method using a set of security microservices that dynamically apply LLM input and output inspectors based on administrative policies, including filters for PII, bias, toxicity, and model drift, to validate and secure data flow, with real-time policy updates and encryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple LLM input inspectors are applied dynamically based on administrative policy, then security and compliance are improved, but processing latency increases
Solution Approach 1:
The system dynamically selects and applies inspectors based on the detected prompt intent and administrative policies. Different inspectors are activated depending on the classification result (e.g., PII inspector for personal information, toxicity inspector for harmful content), allowing the system to adapt the inspection depth to the specific situation and minimize unnecessary processing delays.
Solution Approach 2:
The system performs preliminary classification of the prompt intent before applying inspectors. By first understanding what type of prompt is being processed (e.g., information retrieval, creative writing, code generation), the system can pre-determine which inspectors are necessary, avoiding unnecessary inspection steps and reducing overall processing time.
2Reliability
If multiple LLM output inspectors are applied dynamically based on administrative policy, then data protection and compliance are improved, but processing latency increases
Solution Approach 1:
The output inspection process is also dynamic, with inspectors selected based on the original prompt intent and the generated output content. The system applies different output inspectors depending on what was requested (e.g., PII redaction for information retrieval prompts, toxicity filtering for creative prompts), optimizing the balance between protection and speed.
Solution Approach 2:
The system performs preliminary analysis of the prompt and output to determine which inspection steps are necessary. By understanding the context beforehand, the system can skip unnecessary output validation steps and focus only on relevant checks, reducing processing time while maintaining security.
3Reliability
If a comprehensive set of inspectors is applied to all data, then security coverage is improved, but system complexity increases
Solution Approach 1:
The inspection system is segmented into multiple specialized inspectors, each responsible for specific types of content (e.g., PII inspector, toxicity inspector, bias inspector, code inspector). This modular architecture allows the system to maintain comprehensive security coverage while reducing complexity by only activating the relevant inspector segments based on the prompt classification.
Solution Approach 2:
The dynamic intent-based classification system automatically determines which inspector segments should be active for each request. By matching the prompt intent to appropriate inspectors, the system provides comprehensive security coverage for all possible scenarios while avoiding the complexity of running all inspectors simultaneously.
Data Source
AI summary
The present disclosure includes computer-implemented methods of guardrails for securely using large language models (LLMs). The method comprises monitoring user data flow using an application programming interface (API) and receiving an administrative policy from an administration communication interface. The method involves dynamically applying a plurality of LLM input inspectors to LLM input data. The application of the plurality of LLM input inspectors is based on the administration policy. The dynamic application of the plurality of LLM input inspectors is in sequence for latency optimization. The plurality of LLM input inspectors serve as LLM input guardrails for a plurality of secure deployed large language models (LLMs). The plurality of LLM input inspectors are configured by the administrative policy and validate the LLM input data to validated LLM input data based on the administration policy. Additionally, the method comprises dynamically applying a plurality of LLM output inspectors to LLM output data.