Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization
In the rapidly evolving landscape of artificial intelligence, Vision-Language Models (VLMs) are gaining traction for their ability to synthesize images, generate captions, and retrieve information by harmonizing textual and visual data within a unified embedding space. However, this remarkable flexibility comes with a significant downside: the susceptibility to malicious prompts that can generate unsafe content. This vulnerability raises pressing safety concerns that the AI community must address.
Current strategies aimed at mitigating these risks primarily fall into two categories. The first involves blacklist filtering systems that identify known harmful prompts. Unfortunately, these filters are often easily circumvented, as malicious actors can simply modify prompts to evade detection. The second category includes heavy classifier-based systems that can be resource-intensive and may struggle to remain robust against embedding-level attacks. Given these limitations, there is an urgent need for innovative solutions that offer both efficiency and effectiveness in safeguarding VLMs.
Introducing HyPE and HyPS
To tackle these challenges, we propose a dual-component framework consisting of Hyperbolic Prompt Espial (HyPE) and Hyperbolic Prompt Sanitization (HyPS). Together, these components provide a comprehensive approach to detecting and neutralizing harmful prompts.
- Hyperbolic Prompt Espial (HyPE): This component functions as a lightweight anomaly detector. By leveraging the structured geometry of hyperbolic space, HyPE is capable of modeling benign prompts and identifying harmful ones as outliers. This geometric approach not only enhances detection accuracy but also minimizes the computational resources required for prompt analysis.
- Hyperbolic Prompt Sanitization (HyPS): Once harmful prompts are identified, HyPS employs explainable attribution methods to pinpoint and selectively modify the problematic words. This process neutralizes unsafe intent while preserving the overall semantics of user prompts, ensuring that the integrity of the original message is maintained.
Proven Effectiveness
Through extensive experiments conducted across various datasets and adversarial scenarios, our framework demonstrates a significant improvement over existing defenses. Both HyPE and HyPS consistently outperform prior approaches in terms of detection accuracy and robustness. The synergy between these two components results in an efficient, interpretable, and resilient strategy for protecting VLMs against the misuse of malicious prompts.
In conclusion, as AI technologies continue to advance, it is imperative to develop robust mechanisms that can defend against the emerging threats posed by harmful prompts. Our research into hyperbolic geometry offers a promising avenue for enhancing the safety and reliability of Vision-Language Models, paving the way for safer AI applications in diverse fields.