8 citations · 8 across the 12 of their papers we have counts for
11 papers · 1 filter
Why Subliminal Learning Needs So Much Data: A Noisy Inverse View through Steering Vector Recovery
Luoyu Chen, Xiaoyu Ding, Weiqi Wang +4
Subliminal learning lets a student inherit a teacher's behavioral trait from semantically unrelated data, yet published demonstrations typically require tens of thousands of carrie…
Reactivating Alignment: Defending LLMs from Jailbreaks via Intention-Aware Input-Output Matching
Luoyu Chen, Weiqi Wang, Chenhan Zhang +2
Large language models (LLMs) remain vulnerable to jailbreak attacks that conceal harmful intent within complex adversarial prompts. Existing defenses primarily rely on input pertur…
Target-free Latent Safety Alignment
Luoyu Chen, Weiqi Wang, Chenhan Zhang +3
Large language models (LLMs) remain highly vulnerable to jailbreak attacks that induce harmful behaviors and circumvent safety alignment. To defend against such attacks, adversaria…
CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion
Chenhan Zhang, Ali Braytee, Madhushi Bandara +4
Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods suffer a sharp drop in mode…
Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications
Xin Hao, Massimo Piccardi, Chenhan Zhang +4
As modern wireless communication networks grow increasingly complex, network outages driven by the inconsistency between dynamic topologies and protocol configurations have become…
Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation
Luoyu Chen, Weiqi Wang, Zhiyi Tian +5
Jailbreak prompts can trigger harmful completions on aligned LLMs, In accordance, safety steering has been proposed: test-time activation interventions that steer jailbreak activat…
Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +2
Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or task-gradient reversal often d…
Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions
Xin Hao, Wei Ni, Chenhan Zhang +2
Sovereign network functions, e.g., routing protocols, are becoming increasingly complex and susceptible to failures arising from protocol configuration anomalies and anomalous conf…
GeoIB: Geometry-Aware Information Bottleneck via Statistical-Manifold Compression
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Information Bottleneck (IB) is widely used, but in deep learning, it is usually implemented through tractable surrogates, such as variational bounds or neural mutual information (M…
EVE: Efficient Verification of Data Erasure through Customized Perturbation in Approximate Unlearning
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +2
Verifying whether the machine unlearning process has been properly executed is critical but remains underexplored. Some existing approaches propose unlearning verification methods…
BlindU: Blind Machine Unlearning without Revealing Erasing Data
Weiqi Wang, Zhiyi Tian, Chenhan Zhang +1
Machine unlearning enables data holders to remove the contribution of their specified samples from trained models to protect their privacy. However, it is paradoxical that most unl…