2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CR2025
SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity
Pengfei Jing, Mengyun Tang, Xiaorong Shi +5
Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities and limitations across various applications, including natural language processing and code…
cs.CR2024★ 1 cited
Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models
Yang Bai, Ge Pei, Jindong Gu +2
Large language models (LLMs) have achieved remarkable performance on a wide range of tasks. However, recent studies have shown that LLMs can memorize training data and simple repea…
cs.CV2024★ 2 cited
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
Kuofeng Gao, Yang Bai, Jiawang Bai +2
Multi-modal Large Language Models (MLLMs) have recently achieved enhanced performance across various vision-language tasks including visual grounding capabilities. However, the adv…