Prompt Injection Variant Generation for LGM Defense Robustness
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Solution Overview
Problem
Existing generative AI models are vulnerable to prompt injection attacks, which manipulate them to generate unintended and potentially harmful outputs, and current defense mechanisms struggle to detect variant forms of these attacks effectively.
Innovation Solution
An attack defense system employing a two-phase framework generates and evaluates variant prompt injection attacks using a system-level prompt, iteratively improving detection and defense robustness against these attacks by learning from effectiveness scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current defense mechanisms are used to detect prompt injection attacks, then basic attack detection is possible, but variant forms of attacks cannot be detected effectively
Solution Approach 1:
The system performs preliminary actions by generating multiple variant forms of prompt injection attacks before actual detection is needed. These pre-generated variants serve as a foundation for building more robust detection capabilities that can handle unexpected attack variations.
Solution Approach 2:
The system employs feedback mechanisms by evaluating the effectiveness of generated attack variants and using this information to iteratively improve detection strategies. The feedback loop allows the system to learn from successful and unsuccessful attacks, enhancing its ability to detect variant forms in future interactions.
2Reliability
If generative AI models are made more robust against prompt injection attacks, then security improves, but model complexity increases
Solution Approach 1:
The system introduces an intermediary defense layer that sits between the prompt injection attack and the generative AI model. This intermediary component handles the complexity of attack detection and mitigation, allowing the core generative model to remain relatively simple while still achieving enhanced security through the added defensive layer.
3Measurement precision
If multiple variant attacks are generated and evaluated, then detection accuracy improves, but computational resources increase
Solution Approach 1:
The system applies partial action by generating and evaluating a selective subset of attack variants rather than exhaustively testing all possible variations. This approach achieves sufficient detection accuracy by focusing on the most promising or likely attack vectors, thereby reducing unnecessary computational resource consumption while maintaining effective security coverage.
Data Source
AI summary
This disclosure describes utilizing an attack defense system to improve the defense robustness of a targeted large generative model (LGM) by generating a set of variant prompt injection attacks that are successful against the targeted LGM, where the set of variants is based on a prompt injection attack (e.g., jailbreak) against the targeted LGM or another LGM. For example, the attack defense system utilizes a two-phase framework to generate variant prompt injection attacks and evaluate their attack effectiveness against a targeted LGM. The attack defense system achieves improved variant prompt injection attacks by repeating the two-phase framework and gaining insights from the effectiveness scores of previously generated variants. In addition to generating enhanced variants, the attack defense system generates diverse variants to safeguard the targeted LGM against a broader range of prompt injection attacks that employ more creative and complex styles.