6 citations · 15 across the 11 of their papers we have counts for
18 papers
Not Where You Think: Protecting Location Privacy Against Reasoning-Based Geolocation
Shanghao Shi, Jian Xiao, Hao Li +5
Recent large reasoning models can infer where ordinary photos were taken using only visual information, creating a serious location privacy risk even when metadata and obvious geog…
Mirage: Targeted Jailbreaks Against Post-Generation Safety Checkers in Multimodal Diffusion Models
Shanghao Shi, Chaoyu Zhang, Heng Jin +4
Post-generation safety checkers are widely employed in diffusion-model pipelines to prevent unsafe generated content from reaching users. In this paper, we show that these safety c…
Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience
Zixuan Wang, Hao Li, Fengyu Gao +5
Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vulnerable to prompt injection a…
Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning
Shanghao Shi, Xiao Wang, Chaoyu Zhang +6
The integration of external tools has substantially expanded the capabilities of large language model (LLM) agents, but it also introduces new attack surfaces beyond prompt injecti…
TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models
Zhen Guo, Shanghao Shi, Hao Li +3
Large Reasoning Models (LRMs) introduce a reasoning-level attack surface: adversaries can corrupt intermediate inferences while preserving a plausible trace and an apparently benig…
AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management
Ruoyao Wen, Hao Li, Chaowei Xiao +1
Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft. LLM agents maintain working me…
AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?
Hao Li, Ruoyao Wen, Shanghao Shi +3
AI agents that autonomously interact with external tools and environments have shown great promise across real-world applications. However, their reliance on external data exposes…
ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack
Hao Li, Yankai Yang, G. Edward Suh +2
Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are high…
Defenses Against Prompt Attacks Learn Surface Heuristics
Shawn Li, Chenxiao Yu, Zhiyu Ni +4
Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified instructions that define the intend…
Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring
Peichun Hua, Hao Li, Shanghao Shi +2
Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and effi…
CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks
Xu Zhang, Hao Li, Zhichao Lu
Multimodal Large Language Models (MLLMs) achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks. While existing work focuses on e…
DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents
Hao Li, Xiaogeng Liu, Hung-Chun Chiu +3
Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities. By interacting with external environments through…
InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models
Hao Li, Xiaogeng Liu
Prompt injection attacks pose a critical threat to large language models (LLMs), enabling goal hijacking and data leakage. Prompt guard models, though effective in defense, suffer…
Debiased Orthogonal Boundary-Driven Efficient Noise Mitigation
Hao Li, Jiayang Gu, Jingkuan Song +2
Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale…
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
Hao Li, Chenghao Yang, An Zhang +3
Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus…
Negative Pre-aware for Noisy Cross-modal Matching
Xu Zhang, Hao Li, Mang Ye
Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. Due to the cumulative and unavoidable negative impact of unreso…
On Generative Agents in Recommendation
An Zhang, Yuxin Chen, Leheng Sheng +2
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Add…
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Hao Li, Jingkuan Song, Lianli Gao +2
Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are ofte…