Knowledge distillation (KD) enables efficient deployment of deep learning models. However, it can increase privacy risks by making the student more vulnerable to membership inference attacks (MIAs), as the student may inherit patterns specific to the teacher’s private training data. Existing KD-based methods against MI...
Hyeon-Jin Jeong, Seok-Won Hong, Jae-Hwan Jeong et al.· International Conference on...· 0 citations
GradLock is introduced, a novel training-time injection attack that stealthily injects sensitive training data directly into the model parameters and employs dynamic gradient locking to prevent payload degradation during the optimization process.
Jin-Seong Kim, Han-Ju Lee, Seok-Won Hong et al.· arXiv.org· 0 citations
Current safety guard models for Large Language Models (LLMs) often fail to block complex Jailbreaking attacks because they focus on the structure of a prompt rather than its true intent. To evaluate this problem, we introduce the Role-Goal-Format (RGF) framework, which analyzes how the combination of a user’s persona,...
Jin-Seong Kim, Han-Ju Lee, Hyeon-Jin Jeong et al.· International Conference on...· 0 citations
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