LLM Dialogue System for Proprietary Threat Intelligence Access
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Solution Overview
Problem
Public-facing language models cannot access proprietary security and threat intelligence information stored in databases, limiting their ability to provide comprehensive security-related responses.
Innovation Solution
A language model-based dialogue system that interfaces with proprietary security databases, using adapted large language models (LLMs) to generate compatible database queries and prevent malicious access through XSS and prompt injection detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If public-facing language models are used to answer security questions, then the system can provide general-purpose responses, but the system cannot access proprietary threat intelligence information stored in security databases
Solution Approach 1:
The patent introduces an intermediary system that includes a database query generator and a response generator. The database query generator translates user questions into database queries to access proprietary threat intelligence information, while the response generator formulates natural language responses. This intermediary architecture enables the system to bridge the gap between public language models and proprietary databases, allowing access to restricted information without exposing the database directly to external users.
2Adaptability or versatility
If the language model is adapted through fine-tuning or prompt engineering to generate database queries, then the system can interface with proprietary databases, but the system complexity increases
Solution Approach 1:
The patent segments the system into distinct functional modules: a database query generator component that handles query translation, a database interface component that executes queries, and a response generator component that formulates answers. This segmentation allows each component to be independently developed, tested, and maintained. The database query generator can be fine-tuned or prompt-engineered separately from the response generator, reducing overall system complexity while maintaining adaptability.
3Reliability
If the dialogue system implements security measures to prevent malicious access, then the system can protect the database from XSS and prompt injection attacks, but the system complexity increases
Solution Approach 1:
The patent implements preliminary security validation by introducing a validation step that checks database queries for malicious patterns before they are executed. The system validates queries to detect cross-site scripting (XSS) attempts and prompt injection attacks, preventing malicious access before it can compromise the database. This preliminary action approach allows security measures to be integrated into the existing query workflow without requiring a complete system redesign.
Data Source
AI summary
An LLM is adapted to generate database queries that are compatible with a proprietary database of a security provider. Adapting the LLM includes evaluating performance of the LLM after initial prompt engineering/fine-tuning to ensure that generated database queries are valid (i.e., comport to the database schema and can be executed to return results). When the LLM performance is satisfactory, a dialogue system uses the LLM to generate database queries from user queries. The dialogue system determines intent of each user query, which informs whether the query is supported. Supported user queries are converted to database queries using the LLM and submitted to the database. The dialogue system leverages another language model to generate a summarized, natural language representation of the database query results and constructs a response from the summary. The dialogue system also checks for XSS and prompt injection before database queries are ultimately submitted to the database.