arXiv AI
Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-node Attack
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Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices
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arXiv Machine Learning
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Securing LLMs in the Wild: Privacy and Security Challenges at the Edge
arXiv:2607. 13088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms.
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DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization
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arXiv AI
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Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
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Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
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