1 citations · 2 across the 9 of their papers we have counts for
10 papers
Structured Security Auditing and Robustness Enhancement for Untrusted Agent Skills
Lijia Lv, Xuehai Tang, Jie Wen +2
Agent Skills package SKILL.md files, scripts, reference documents, and repository context into reusable capability units, turning pre-load auditing from single-prompt filtering int…
FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing
Peng Wang, Biyu Zhou, Xuehai Tang +2
Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grained fact access. To address t…
Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers
Liang Lin, Zhihao Xu, Xuehai Tang +5
The safety of large language models (LLMs) has garnered significant research attention. In this paper, we argue that previous empirical studies demonstrate LLMs exhibit a propensit…
Exploiting Synergistic Cognitive Biases to Bypass Safety in LLMs
Xikang Yang, Biyu Zhou, Xuehai Tang +2
Large Language Models (LLMs) demonstrate impressive capabilities across a wide range of tasks, yet their safety mechanisms remain susceptible to adversarial attacks that exploit co…
LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing
Peng Wang, Biyu Zhou, Xuehai Tang +2
Large Language Models often contain factually incorrect or outdated knowledge, giving rise to model editing methods for precise knowledge updates. However, current mainstream locat…
The Dark Side of Trust: Authority Citation-Driven Jailbreak Attacks on Large Language Models
Xikang Yang, Xuehai Tang, Jizhong Han +1
The widespread deployment of large language models (LLMs) across various domains has showcased their immense potential while exposing significant safety vulnerabilities. A major co…
AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting LLMs
Lijia Lv, Weigang Zhang, Xuehai Tang +4
Jailbreak vulnerabilities in Large Language Models (LLMs) refer to methods that extract malicious content from the model by carefully crafting prompts or suffixes, which has garner…
Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens
Xikang Yang, Xuehai Tang, Fuqing Zhu +2
Vision-language models (VLMs) seamlessly integrate visual and textual data to perform tasks such as image classification, caption generation, and visual question answering. However…
Chain of Attack: a Semantic-Driven Contextual Multi-Turn attacker for LLM
Xikang Yang, Xuehai Tang, Songlin Hu +1
Large language models (LLMs) have achieved remarkable performance in various natural language processing tasks, especially in dialogue systems. However, LLM may also pose security…
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Lingwei Wei, Dou Hu, Wei Zhou +5
Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summ…