We propose patching for large language models (LLMs) like software versions, a lightweight and modular approach for addressing safety vulnerabilities. This "patch" introduces only 0.003% additional parameters, yet reliably steers model behavior toward that of a safer reference model across toxicity mitigation, bias reduction, and harmfulness refusal.
@misc{arif2025patching,title={Patching LLM Like Software: A Lightweight Method for Improving Safety Policy in Large Language Models},author={Arif, Huzaifa and Murugesan, Keerthiram and Ko, Ching-Yun and Chen, Pin-Yu and Das, Payel and Gittens, Alex},year={2025},month=nov,note={arXiv preprint},}
PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks
Huzaifa Arif, Keerthiram Murugesan, Payel Das, and 2 more authors
In IEEE Conference on Secure and Trustworthy Machine Learning, Apr 2025
We investigate data leakage vulnerabilities in residual neural networks during inference time and propose mitigation strategies.
@inproceedings{arif2025peel,title={PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks},author={Arif, Huzaifa and Murugesan, Keerthiram and Das, Payel and Gittens, Alex and Chen, Pin-Yu},booktitle={IEEE Conference on Secure and Trustworthy Machine Learning},year={2025},month=apr,}
Group Fair Federated Learning via Stochastic Kernel Regularization
Huzaifa Arif, Pin-Yu Chen, Keerthiram Murugesan, and 1 more author
Transactions on Machine Learning Research, Apr 2025
We develop a stochastic kernel regularization approach to achieve group fairness in federated learning settings.
@article{arif2025group,title={Group Fair Federated Learning via Stochastic Kernel Regularization},author={Arif, Huzaifa and Chen, Pin-Yu and Murugesan, Keerthiram and Gittens, Alex},journal={Transactions on Machine Learning Research},year={2025},month=apr,}
Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models
Huzaifa Arif, Pin-Yu Chen, Alex Gittens, and 2 more authors
In AAAI Workshop on AI to Accelerate Science and Engineering, Apr 2025
We expose vulnerabilities in weather prediction models through novel evasion attacks and propose defensive strategies.
@inproceedings{arif2025forecasting,title={Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models},author={Arif, Huzaifa and Chen, Pin-Yu and Gittens, Alex and Diffenderfer, James and Kailkhura, Bhavya},booktitle={AAAI Workshop on AI to Accelerate Science and Engineering},year={2025},}
2023
Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming
Huzaifa Arif, Alex Gittens, and Pin-Yu Chen
In IEEE Conference on Secure and Trustworthy Machine Learning, Feb 2023
We present a novel approach to improve the utility-privacy tradeoff in federated learning through model reprogramming techniques.
@inproceedings{arif2023reprogrammable,title={Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming},author={Arif, Huzaifa and Gittens, Alex and Chen, Pin-Yu},booktitle={IEEE Conference on Secure and Trustworthy Machine Learning},year={2023},month=feb,}
2022
DP-Compressed VFL is secure for Model Inversion Attacks
We analyze the security of differentially private compressed vertical federated learning against model inversion attacks.
@misc{arif2022dpcompressed,title={DP-Compressed VFL is secure for Model Inversion Attacks},author={Arif, Huzaifa and Patterson, Stacy},year={2022},month=apr,note={Preprint},}
