The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks

no code implementations • 24 Oct 2023 • Xiaoyi Chen, Siyuan Tang, Rui Zhu, Shijun Yan, Lei Jin, ZiHao Wang, Liya Su, Zhikun Zhang, XiaoFeng Wang, Haixu Tang

In our research, we propose a novel attack, Janus, which exploits the fine-tuning interface to recover forgotten PIIs from the pre-training data in LLMs.

In-Context Learning

Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

no code implementations • 29 Jan 2023 • Rui Zhu, Di Tang, Siyuan Tang, Guanhong Tao, Shiqing Ma, XiaoFeng Wang, Haixu Tang

Finally, we perform both theoretical and experimental analysis, showing that the GRASP enhancement does not reduce the effectiveness of the stealthy attacks against the backdoor detection methods based on weight analysis, as well as other backdoor mitigation methods without using detection.

Backdoor Attack

Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models

no code implementations • 9 Dec 2022 • Rui Zhu, Di Tang, Siyuan Tang, XiaoFeng Wang, Haixu Tang

Our idea is to retrain a given DNN model on randomly labeled clean data, to induce a CF on the model, leading to a sudden forget on both primary and backdoor tasks; then we recover the primary task by retraining the randomized model on correctly labeled clean data.

Continual Learning