Context Concretizer for LLM Jailbreak Detection

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Solution Overview

Problem

Large Language Models (LLMs) are vulnerable to jailbreak attempts, which involve crafted queries designed to circumvent safeguards and obtain dangerous information, leading to risks such as hacking, data breaches, and flawed predictions, posing significant security and integrity challenges.

Innovation Solution

Implementing a context concretizer within LLMs that is specifically trained and configured for particular contexts, such as the financial industry, to identify and intercept jailbreak attempts, combined with an alignment award function to reinforce correct behavior and an update mechanism to adapt to changing threats.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If LLMs use general-purpose processing for all queries, then they maintain simplicity and broad applicability, but they become vulnerable to jailbreak attempts that circumvent safeguards

Engineering Contradiction:

Improvesecurity against jailbreak attemptsVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments query processing into two distinct pathways: a general-purpose processing path for normal queries and a context-specific processing path for queries matching predefined contexts. This segmentation allows the system to maintain simplicity for most queries while applying enhanced security measures only where needed, thereby improving reliability without proportionally increasing overall complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary context concretizer component that sits between the general LLM processing and the final output. This intermediary receives queries, determines their context, and routes them to appropriate processing paths. The concretizer acts as a mediator that adds security functionality without requiring complete restructuring of the underlying LLM architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If LLMs implement comprehensive safeguards to prevent malicious outputs, then security improves, but crafted queries can still circumvent these safeguards through jailbreak attempts

Engineering Contradiction:

Improvesecurity against malicious outputsVSAvoidability to handle diverse query types

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system applies different processing qualities to different query types based on their context. Queries matching predefined contexts (such as financial, medical, or legal domains) receive context-specific processing with enhanced security measures, while other queries receive standard processing. This local quality approach ensures robust security where needed without compromising the system's ability to handle diverse query types efficiently.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts its processing approach based on the incoming query's context. The context concretizer continuously determines whether a query matches predefined contexts and switches between general-purpose and context-specific processing modes accordingly. This dynamic adaptation allows the system to maintain high security for vulnerable contexts while preserving versatility for handling various query types.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If LLMs process all queries with context-specific analysis, then detection precision for jailbreak attempts improves, but processing time and computational resources increase

Engineering Contradiction:

Improvedetection precision for jailbreak attemptsVSAvoidquery processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies context-specific analysis selectively rather than to all queries. The context concretizer performs preliminary analysis to determine whether a query matches predefined contexts, and only then applies the more resource-intensive context-specific processing. This partial action approach maintains high detection precision for relevant queries while minimizing processing time for queries that don't require enhanced analysis.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary context determination before applying full context-specific processing. The context concretizer quickly assesses whether an incoming query matches predefined contexts and routes it accordingly. This preliminary action allows the system to prepare for potential jailbreak detection needs in advance, improving detection precision when required while avoiding unnecessary processing overhead for standard queries.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260080051A1Large language model context concretizer

Publication Date: 2026.03.19 WELLS FARGO BANK NA

AI summary

An example computer system for determining jailbreak attempts comprises: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: receive a query sequence from a client device; determine a context of the query sequence; responsive to a determination the context of the query sequence is the associated context: provide the query sequence to a context concretizer, wherein the context concretizer is configured to process query sequences that include an associated context; determine, by the context concretizer, whether the query sequence includes a jailbreak attempt for the associated context; and responsive to a second determination that the query sequence includes the jailbreak attempt, provide an error response to the client device.