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Shi-Liang Xiao

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#machine learning Preprint Sep 2026

Breadth Beats Depth: Improving GCG-Based Jailbreak Optimization with Breadth-Oriented Suffix Search

BOSS is proposed, a plug-and-play framework that improves GCG-based jailbreak optimization through breadth-oriented suffix search and improves attack success rates across multiple GCG-based methods while reducing optimization time.

Shi-Liang Xiao, Jing-Song Wei, Yu-Zhi Liang et al. · 0 citations
#natural language process... Open access Aug 2026

OASIS: Optimizing Attacker Sequences for Hard-Label Black-Box Text Attacks

Experiments across multiple datasets, victim models, and large language models show that OASIS consistently outperforms strong standalone baselines and simple manually constructed chains, suggesting that attacker composition is not merely an implementation choice, but a practical optimization target for improving hard-...

Qian Chen, Shi-Liang Xiao, Yu-Zhi Liang · 0 citations
#machine learning Preprint Aug 2026

TACS: Trajectory-Aware Candidate Selection for LLM Jailbreak Suffix Optimization

TACS is proposed, a trajectory-aware candidate selection framework for jailbreak suffix optimization that augments per-step evaluation with a trajectory-aware proxy and stabilizes selection with reference-policy regularization and a discriminator-estimated chi-squared correction, encouraging choices that remain effecti...

Shi-Liang Xiao · 0 citations

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