Distributed Ledger LLM Security for Anomaly Blocking
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Solution Overview
Problem
Large language models (LLMs) generate content that may include hallucinations, inaccurate information, offensive material, or violate intellectual property, making it difficult to ensure compliance with policy standards and licensing requirements.
Innovation Solution
A trained LLM is used to oversee transactions with third-party LLMs, detecting and blocking anomalous interactions, and reporting misuse by analyzing transaction data on a distributed ledger, employing modules for anomaly detection and protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trained LLM is used to oversee and detect misuse in transactions, then the reliability of content safety is improved, but the device complexity increases due to additional monitoring and evaluation modules
Solution Approach 1:
A trained LLM serves as an intermediary security module between the distributed ledger and the third-party LLM service. This intermediary detects anomalies and blocks misuse without requiring complex centralized control systems, resolving the contradiction by using a specialized AI layer that simplifies the overall architecture while improving safety reliability
2Reliability
If anomaly detection modules are added to monitor LLM transactions, then the reliability of misuse detection is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-training the LLM on security protocols and policies before actual transactions occur. This pre-training enables the model to quickly identify and block anomalies during transaction processing without requiring real-time complex analysis, thus improving detection reliability while minimizing processing time delays
3Reliability
If a distributed ledger is used to track transactions, then the reliability of transaction tracking is improved, but the device complexity increases due to ledger integration requirements
Solution Approach 1:
The distributed ledger is integrated into an existing multi-functional platform that handles both transaction recording and AI model deployment. This universal approach allows the same infrastructure to serve multiple purposes including transaction tracking, model execution, and security monitoring, thereby improving reliability without proportionally increasing complexity
Data Source
AI summary
Disclosed are various embodiments for a distributed ledger enabled large language model security protocol. A large language model (LLM) can filter data generated by a distributed agent for a trace of a transaction with a third-party LLM. A trained LLM can analyze any found trace of a transaction and identify at least an anomaly related to the found trace. The trained LLM can block the transaction based on the anomaly.