Large Language Models (LLMs) have rapidly advanced in reasoning capability and accessibility, driving their deployment across diverse applications. Yet this progress has also widened the surface for safety and security vulnerabilities. Adversaries can exploit prompt diversity, dialog memory, or multimodal inputs to induce unsafe or confidential outputs, while continual fine-tuning and third-party integration render static assurance infeasible. This paper introduces our ongoing national R&D project on developing the AutoPT Framework-an Autonomous Purple Teaming architecture that extends the collaborative principles of purple teaming toward self-adaptive, continuously verifiable LLM assurance. AutoPT unifies autonomous adversarial exploration and adaptive defensive reinforcement through two co-evolving agents. The red module, AutoPT-Red, employs coverage-guided fuzzing and internal measurement metrics to autonomously uncover vulnerabilities. The blue module, AutoPT-Blue, performs self-healing adaptation by updating guardrails and detecting integrity or confidentiality violations using embedding-based feedback. Preliminary case studies on jailbreak fuzzing and backdoor-poisoning defense validate the feasibility of this closed-loop, self-adapting architecture. As part of a broader national initiative, this work lays the conceptual and technical foundation for transitioning industrial purple teaming into a fully autonomous, scalable, and measurable assurance paradigm for generative AI systems.

In the Metaverse, continuous authentication is essential for verifying the ongoing connection between a user’s physical identity and avatar, ensuring secure access to various services. This process is crucial for confirming identities, maintaining security, and preventing unauthorized activities that could compromise legitimate services. However, traditional biometric-based authentication methods are susceptible to threats such as impersonation, replay attacks, and disguise, primarily due to the difficulty in directly using biometric information to represent the connection between virtual and physical identities. To address these challenges, some studies have proposed using blockchain schemes to mitigate security threats. Despite this, these approaches often encounter issues like insufficient network protection for authentication connections, prolonged data processing times, and latency. To overcome these limitations, we propose a secure continuous authentication framework that leverages standard protocols such as QUIC and JWT to verify user identities efficiently. Our approach employs embedding models on edge devices to generate and transmit biometric data. In contrast, a deep learning-based model on the server validates the user’s credentials, ensuring both high performance and availability. Experimental results show that our QUIC and JWT-based protocol delivers superior security and effectiveness compared to traditional biometric approaches and blockchain-based methods, achieving an AUC of 0.97, an EER of 3.77, and an F1 score of 0.96.