7 citations · 12 across the 12 of their papers we have counts for
13 papers
AutoRedClaw: Automated Red-Teaming for Claw-Style Agents
Hao Li, Tongyan Hu, Weidi Luo +8
Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw. These claw-style agents maintain configuration files (e.g., AGE…
Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework
Jiazhang Cai, Tao Wang, Ruidong Zhang +35
Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step error amplification, prompt b…
Agent MechSuits: Mechanistic Subspace Safety Steering for Multi-Turn CLI Agents
Weidi Luo, Qiming Zhang, Yihao Quan +5
Command-Line Interface (CLI) agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risk…
Cooking Up Risks: Benchmarking and Reducing Food Safety Risks in Large Language Models
Weidi Luo, Xiaofei Wen, Tenghao Huang +6
Food safety directly affects human health in daily life, as unsafe preparation or storage can lead to contamination and foodborne illness. PwC's 2025 consumer survey reports that 4…
Your Harness is Not Secure: Benchmarking Real-world Threat of Command Line Interface Agent
Weidi Luo, Qiming Zhang, Tianyu Lu +9
Command-line interface (CLI) agents powered by large language models (LLMs) can interpret natural-language requests, plan multi-step tasks, execute shell commands, and modify files…
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
Haoran Lu, Luyang Fang, Ruidong Zhang +47
Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment…
Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models
Weidi Luo, Tianyu Lu, Qiming Zhang +8
Recent advances in multi-modal large reasoning models (MLRMs) have shown significant ability to interpret complex visual content. While these models enable impressive reasoning cap…
AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection
Weidi Luo, Shenghong Dai, Xiaogeng Liu +4
The rapid advancements in Large Language Models (LLMs) have enabled their deployment as autonomous agents for handling complex tasks in dynamic environments. These LLMs demonstrate…
Disentangling Memory and Reasoning Ability in Large Language Models
Mingyu Jin, Weidi Luo, Sitao Cheng +5
Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM in…
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models
Weidi Luo, He Cao, Zijing Liu +5
With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key…
Visual-RolePlay: Universal Jailbreak Attack on MultiModal Large Language Models via Role-playing Image Character
Siyuan Ma, Weidi Luo, Yu Wang +1
With the advent and widespread deployment of Multimodal Large Language Models (MLLMs), ensuring their safety has become increasingly critical. To achieve this objective, it require…
JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks
Weidi Luo, Siyuan Ma, Xiaogeng Liu +2
With the rapid advancements in Multimodal Large Language Models (MLLMs), securing these models against malicious inputs while aligning them with human values has emerged as a criti…
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
Vardaan Pahuja, Weidi Luo, Yu Gu +7
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalizati…