AI Output Blocklist Filtering for Malicious Prompt Detection
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Solution Overview
Problem
Artificial intelligence models, particularly large language models, are vulnerable to cyberattacks that manipulate or modify inputs to produce unreliable or malicious outputs, leading to data leakage or unauthorized access.
Innovation Solution
Implementing a blocklist-based analysis engine to identify and mitigate malicious prompts and outputs by comparing them against a corpus of known malicious prompts and outputs, using techniques such as tokenization, vectorization, and similarity analysis to determine and prevent malicious or sensitive content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional input validation methods are used to protect AI models, then implementation simplicity is maintained, but detection precision of malicious prompts deteriorates
Solution Approach 1:
The patent introduces an analysis engine as an intermediary component between the prompt input and the AI model. This engine performs sophisticated similarity analysis using embeddings and blocklists, enabling high-precision detection of malicious prompts without requiring modification of the underlying AI model architecture.
Solution Approach 2:
The system performs preliminary analysis of prompts before they are processed by the AI model. By pre-computing embeddings and comparing them against blocklists of known malicious patterns, the system identifies and blocks harmful inputs in advance, preventing them from reaching the model.
2Reliability
If comprehensive similarity analysis is performed on all prompts, then detection reliability is improved, but processing time increases
Solution Approach 1:
The system applies partial analysis by focusing computational resources on the most critical aspects of prompt detection. Rather than analyzing every possible feature of each prompt in equal detail, the system uses embeddings to capture essential semantic information and compares only the most relevant features against the blocklist, achieving high reliability with reduced processing time.
3Measurement precision
If sophisticated analysis techniques are applied to detect malicious prompts, then detection precision is improved, but computational resources consumed increases
Solution Approach 1:
The system transforms prompts into embedding representations, changing the parameter space from raw text to dense vector representations. This transformation enables more efficient similarity computation using mathematical operations on vectors rather than exhaustive text comparison, achieving high detection precision with reduced computational overhead.
4Object-affected harmful factors
If blocklist-based filtering is implemented to prevent malicious outputs, then output security is improved, but model versatility deteriorates
Solution Approach 1:
The system replaces traditional mechanical keyword-matching filtering with semantic similarity analysis using embeddings. This substitution allows the system to understand the meaning and context of outputs, distinguishing between harmful content that should be blocked and benign content that merely shares superficial similarities with malicious patterns, thereby maintaining both security and versatility.
Data Source
AI summary
The inputs and/or outputs of a generative artificial intelligence model are monitored to determine whether they contain or otherwise elicit undesired behavior by the model such as bypassing security measures, leaking sensitive information, or generating or consuming malicious content. This determination can be used to selectively trigger remediation processes to protect the model from malicious actions. Related apparatus, systems, techniques and articles are also described.