2,560+ academic papers on AI security, attacks, and defenses
Showing 701–720 of 982 papers
Clear filtersHajun Kim, Hyunsik Na, Daeseon Choi
As the use of large language models (LLMs) continues to expand, ensuring their safety and robustness has become a critical challenge. In particular,...
Ajesh Koyatan Chathoth, Stephen Lee
Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition...
Yule Liu, Heyi Zhang, Jinyi Zheng +6 more
Membership inference attacks (MIAs) on large language models (LLMs) pose significant privacy risks across various stages of model training. Recent...
Pascal Zimmer, Ghassan Karame
In this paper, we present the first detailed analysis of how training hyperparameters -- such as learning rate, weight decay, momentum, and batch...
Fuyao Zhang, Jiaming Zhang, Che Wang +6 more
The reliance of mobile GUI agents on Multimodal Large Language Models (MLLMs) introduces a severe privacy vulnerability: screenshots containing...
Ayush Chaudhary, Sisir Doppalpudi
The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and...
Thomas Rivasseau
Current Large Language Model alignment research mostly focuses on improving model robustness against adversarial attacks and misbehavior by training...
Mukkesh Ganesh, Kaushik Iyer, Arun Baalaaji Sankar Ananthan
The Key Value(KV) cache is an important component for efficient inference in autoregressive Large Language Models (LLMs), but its role as a...
Yunhao Chen, Xin Wang, Juncheng Li +5 more
Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet they share a fundamental limitation:...
Haotian Jin, Yang Li, Haihui Fan +3 more
Backdoor attacks pose a serious threat to the security of large language models (LLMs), causing them to exhibit anomalous behavior under specific...
Samuel Nathanson, Rebecca Williams, Cynthia Matuszek
Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities...
Onkar Shelar, Travis Desell
Large Language Models remain vulnerable to adversarial prompts that elicit toxic content even after safety alignment. We present ToxSearch, a...
Jiaji Ma, Puja Trivedi, Danai Koutra
Text-attributed graphs (TAGs), which combine structural and textual node information, are ubiquitous across many domains. Recent work integrates...
Yuting Tan, Yi Huang, Zhuo Li
Backdoor attacks on large language models (LLMs) typically couple a secret trigger to an explicit malicious output. We show that this explicit...
Hasini Jayathilaka
Prompt injection attacks are an emerging threat to large language models (LLMs), enabling malicious users to manipulate outputs through carefully...
Rui Wang, Zeming Wei, Xiyue Zhang +1 more
Deep Neural Networks (DNNs) are known to be vulnerable to various adversarial perturbations. To address the safety concerns arising from these...
Gil Goren, Shahar Katz, Lior Wolf
Large Language Models (LLMs) are vulnerable to adversarial attacks that bypass safety guidelines and generate harmful content. Mitigating these...
Sajad U P
Phishing and related cyber threats are becoming more varied and technologically advanced. Among these, email-based phishing remains the most dominant...
Shaowei Guan, Yu Zhai, Zhengyu Zhang +2 more
Large Language Models (LLMs) are increasingly vulnerable to adversarial attacks that can subtly manipulate their outputs. While various defense...
Hao Li, Jiajun He, Guangshuo Wang +3 more
Retrieval-Augmented Generation (RAG) enhances large language models by integrating external knowledge, but reliance on proprietary or sensitive...
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