6 papers
SWaRL: Safeguard Code Watermarking via Reinforcement Learning
Neusha Javidnia, Ruisi Zhang, Ashish Kundu +1
We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedding unique and verifiable signatures i…
AttestLLM: Efficient Attestation Framework for Billion-scale On-device LLMs
Ruisi Zhang, Yifei Zhao, Neusha Javidnia +2
As on-device LLMs(e.g., Apple on-device Intelligence) are widely adopted to reduce network dependency, improve privacy, and enhance responsiveness, verifying the legitimacy of mode…
CryptoGen: Secure Transformer Generation with Encrypted KV-Cache Reuse
Hedong Zhang, Neusha Javidnia, Shweta Pardeshi +2
The widespread deployment of cloud-hosted generative models raises a fundamental challenge: enabling efficient autoregressive generation while preserving the privacy of both user p…
Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report
Zhuoran Yang, Ed Li, Jianliang He +18
We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model (derived fro…
Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques
Neusha Javidnia, Bita Darvish Rouhani, Farinaz Koushanfar
Large language models (LLMs) have demonstrated exceptional capabilities in generating text, images, and video content. However, as context length grows, the computational cost of a…
Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign
Ruisi Zhang, Neusha Javidnia, Nojan Sheybani +1
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations a…