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20212026
most citedBackdoor Defense via Decoupling the Training Process

42 citations · 139 across the 48 of their papers we have counts for

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11 papers · 1 filter

cs.CR2026

ASCENT: First-Order Optimal Fine-Tuning with Recalibration for Safety--Utility Co-Enhancement

Weiwei Qi, Chongyu Wang, Tianhang Zheng +5

Supervised fine-tuning can substantially improve the downstream utility of large language models (LLMs) but may compromise their safety. Existing safety-preserving methods constrai…

cs.CR2026

ActBench: Self-Evolving Benchmark of Behavioral Safety in Cowork Agents

Hongwei Yao, Yiming Liu, Meihui Chen +6

Cowork agents may complete benign tasks while disclosing protected data, manipulating unauthorized state, invocate unauthorized API. We define behavioral safety and introduce ActBe…

cs.CR2026

DARWIN: Evolving Jailbreak Adversary and Guardrail for LLM Safety Evaluation and Protection

Weiwei Qi, Zefeng Wu, Zhilin Guo +5

Most existing LLM safety evaluation and defense methods are static: jailbreak vulnerabilities are assessed with fixed attacks, and guardrails are trained on fixed malicious-prompt…

cs.CR2026

DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment

Zefeng Wu, Weiwei Qi, Jielong Chen +6

Fine-tuning large language models (LLMs) on domain-specific datasets has become a standard paradigm for adapting LLMs to specialized applications. However, recent work has shown th…

cs.CR2026

TRACE: Task-Aware Adaptive Self-Evolving Agentic Jailbreaking

Churui Zeng, Weiwei Qi, Kedong Xiu +6

The rise of LLM agents introduces a new threat by enabling planning, coding, and even end-to-end execution of expert-level attack workflows. However, this threat remains underexplo…

cs.CR2026

Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models

Weiwei Qi, Zefeng Wu, Tianhang Zheng +4

Ensuring Large Language Model (LLM) safety is crucial, yet the lack of a clear understanding about safety mechanisms hinders the development of precise and reliable methodologies f…

cs.CV2026

JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimization

Haolun Zheng, Yu He, Tailun Chen +6

Text-to-image (T2I) models such as Stable Diffusion and DALLE remain susceptible to generating harmful or Not-Safe-For-Work (NSFW) content under jailbreak attacks despite deployed…

cs.CL2026

Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective

Yukun Chen, Xinyu Zhang, Boyi Deng +6

As large language models (LLMs) are employed worldwide, existing evaluation paradigms for their multilingual capabilities primarily focus on factual task performance, neglecting th…

cs.CL2026

Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets

Yuchen Yang, Wenze Lin, Enhao Huang +6

Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in diverse applications. Fine-tuning, an important step for adapting LLMs to spec…

cs.SI2026

DualMind: Towards Understanding Cognitive-Affective Cascades in Public Opinion Dissemination via Multi-Agent Simulation

Enhao Huang, Tongtong Pan, Shuhuai Zhang +6

Forecasting public opinion during PR crises is challenging, as existing frameworks often overlook the interaction between transient affective responses and persistent cognitive bel…

cs.CR2026

SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrails

Zhiyi Mou, Jingyuan Yang, Zeheng Qian +6

While Large Language Models (LLMs) have powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe web real-time application. Cur…