collaborators

8 papers

cs.CR2026

The Boy Who Cried Wolf: Adversarial Misclassification of Safe Inputs as Unsafe in Multimodal Guardrails

Shuo Shi, Rui Yin, Naen Xu +7

Multimodal guard models have emerged as critical safety components for screening content in vision-language systems. While adversarial research has extensively studied jailbreaking…

cs.CR2026

Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors

Rui Yin, Tianxu Han, Naen Xu +8

Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain attack surface: adversaries can di…

cs.AI2026

ACIArena: Toward Unified Evaluation for Agent Cascading Injection

Hengyu An, Minxi Li, Jinghuai Zhang +6

Collaboration and information sharing empower Multi-Agent Systems (MAS) but also introduce a critical security risk known as Agent Cascading Injection (ACI). In such attacks, a com…

cs.CL2026

"I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?

Naen Xu, Jiayi Sheng, Changjiang Li +7

Puns are a common form of rhetorical wordplay that exploits polysemy and phonetic similarity to create humor. In multimodal puns, visual and textual elements synergize to ground th…

cs.CL2026

When Agents "Misremember" Collectively: Exploring the Mandela Effect in LLM-based Multi-Agent Systems

Naen Xu, Hengyu An, Shuo Shi +7

Recent advancements in large language models (LLMs) have significantly enhanced the capabilities of collaborative multi-agent systems, enabling them to address complex challenges.…

cs.CR2026

FraudShield: Knowledge Graph Empowered Defense for LLMs against Fraud Attacks

Naen Xu, Jinghuai Zhang, Ping He +6

Large language models (LLMs) have been widely integrated into critical automated workflows, including contract review and job application processes. However, LLMs are susceptible t…