This research pioneers a signal processing framework for enhancing computational safety in generative AI systems, particularly for large language models and text-to-image diffusion models.

• Introduces novel safety-oriented signal processing techniques to detect and mitigate harmful AI outputs
• Provides analytical methods to identify jailbreak attempts and malicious prompts before execution
• Leverages signal characteristics to distinguish between human and AI-generated content
• Establishes a mathematical foundation for proactive AI safety measures

This approach is transformative for Security teams as it moves beyond restrictive guardrails to more sophisticated detection and prevention mechanisms, offering robust protection without compromising model utility.

Computational Safety for Generative AI: A Signal Processing Perspective