Adversarial Prompt Firewall for LLM Hijacking Prevention
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Solution Overview
Problem
Large Language Models (LLMs) integrated with network monitoring systems are vulnerable to prompt injection attacks, which can lead to malicious actions such as exposing sensitive information or executing harmful code, due to their complex and unpredictable behavior.
Innovation Solution
Implement an adversarial training mechanism using a conversational agent and a second agent to search for potential malicious prompts, maintain a database of prompt signatures, and employ an adversarial prompt firewall to block or alert on suspicious inputs, thereby protecting the language model from hijacking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are integrated with network monitoring tools to enhance user interface capabilities, then user interaction and task automation are improved, but the system becomes vulnerable to prompt injection attacks that can expose sensitive information or execute malicious code
Solution Approach 1:
The patent introduces a prompt firewall as an intermediary component between the user interface and the LLM. This firewall intercepts and analyzes incoming prompts before they reach the language model, blocking malicious prompt injection attempts while allowing legitimate user interactions to pass through. The firewall acts as a mediator that filters harmful inputs without preventing useful functionality.
Solution Approach 2:
The system performs preliminary analysis of prompts using a trained language model that has been adversarially trained to recognize attack patterns. Before the main LLM processes user inputs, the prompt firewall pre-screenes them for potential threats by comparing against known attack signatures and patterns, preventing malicious prompts from reaching the vulnerable model.
2Measurement precision
If adversarial training is applied to train a language model for detecting malicious prompts, then detection accuracy is improved, but training time and computational resources are increased
Solution Approach 1:
The system performs adversarial training in advance during the model development phase, before deployment. By pre-training the language model with various attack patterns and scenarios, the system prepares the detection capabilities beforehand, so that during operation, the model can quickly and accurately identify malicious prompts without requiring real-time training.
Solution Approach 2:
The adversarial training process creates copies of legitimate prompts with malicious modifications to generate training data. Instead of requiring extensive manual creation of attack examples, the system generates synthetic training data by copying and transforming legitimate prompts into various attack variants, efficiently building a comprehensive training dataset.
Data Source
AI summary
In one implementation, a device obtains a prompt from a source for input to a language model. The device performs a search for similar prompts to that of the prompt in a prompt database. The device determines, based on results of the search, whether the prompt represents a potential attack. The device prevents a response to the prompt from being returned from the language model to the source, when the device determines that the prompt represents a potential attack.