8 papers
Controlling Output Rankings in Generative Engines for LLM-based Search
Haibo Jin, Ruoxi Chen, Peiyan Zhang +4
The way customers search for and choose products is changing with the rise of large language models (LLMs). LLM-based search, or generative engines, provides direct product recomme…
Now You Hear Me: Audio Narrative Attacks Against Large Audio-Language Models
Ye Yu, Haibo Jin, Yaoning Yu +2
Large audio-language models increasingly operate on raw speech inputs, enabling more seamless integration across domains such as voice assistants, education, and clinical triage. T…
Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization
Jiwei Guan, Haibo Jin, Haohan Wang
Recent advancements in Large Vision-Language Models (LVLMs) have shown groundbreaking capabilities across diverse multimodal tasks. However, these models remain vulnerable to adver…
GuardVal: Dynamic Large Language Model Jailbreak Evaluation for Comprehensive Safety Testing
Peiyan Zhang, Haibo Jin, Liying Kang +1
Jailbreak attacks reveal critical vulnerabilities in Large Language Models (LLMs) by causing them to generate harmful or unethical content. Evaluating these threats is particularly…
InfoFlood: Jailbreaking Large Language Models with Information Overload
Advait Yadav, Haibo Jin, Man Luo +2
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, their potential to generate harmful responses has raised significant societa…
From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models
Haibo Jin, Peiyan Zhang, Peiran Wang +2
Large foundation models (LFMs) are susceptible to two distinct vulnerabilities: hallucinations and jailbreak attacks. While typically studied in isolation, we observe that defenses…