2,077+ academic papers on AI security, attacks, and defenses
Showing 381–400 of 715 papers
Clear filtersYunhao Yao, Zhiqiang Wang, Haoran Cheng +3 more
The evolution of Large Language Models (LLMs) into Agentic AI has established the Model Context Protocol (MCP) as the standard for connecting...
Shuxin Zhao, Bo Lang, Nan Xiao +1 more
Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their...
Sabrine Ennaji, Elhadj Benkhelifa, Luigi Vincenzo Mancini
Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs...
Karina Chichifoi, Fabio Merizzi, Michele Colajanni
Deep learning and federated learning (FL) are becoming powerful partners for next-generation weather forecasting. Deep learning enables...
Md. Hasib Ur Rahman
As Large Language Models (LLMs) become ubiquitous, the challenge of securing them against adversarial "jailbreaking" attacks has intensified. Current...
Yixin Tan, Zhe Yu, Jun Sakuma
Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications. However, its security...
Safwan Shaheer, G. M. Refatul Islam, Mohammad Rafid Hamid +3 more
Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work...
Peichun Hua, Hao Li, Shanghao Shi +2 more
Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both...
Jie Ma, Junqing Zhang, Guanxiong Shen +2 more
Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT)...
Jing Cui, Yufei Han, Jianbin Jiao +1 more
Backdoor attacks embed malicious behaviors into Large Language Models (LLMs), enabling adversaries to trigger harmful outputs or bypass safety...
Chaomeng Lu, Bert Lagaisse
Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness...
Devanshu Sahoo, Manish Prasad, Vasudev Majhi +5 more
Driven by surging submission volumes, scientific peer review has catalyzed two parallel trends: individual over-reliance on LLMs and institutional...
Devanshu Sahoo, Vasudev Majhi, Arjun Neekhra +3 more
The use of Large Language Models (LLMs) as automatic judges for code evaluation is becoming increasingly prevalent in academic environments. But...
Khurram Khalil, Khaza Anuarul Hoque
Generative Artificial Intelligence models, such as Large Language Models (LLMs) and Large Vision Models (VLMs), exhibit state-of-the-art performance...
Mohamed Afane, Abhishek Satyam, Ke Chen +3 more
Backdoor attacks create significant security threats to language models by embedding hidden triggers that manipulate model behavior during inference,...
Futa Waseda, Shojiro Yamabe, Daiki Shiono +2 more
Large vision-language models (LVLMs) are vulnerable to typographic attacks, where misleading text within an image overrides visual understanding....
Reachal Wang, Yuqi Jia, Neil Zhenqiang Gong
Prompt injection attacks aim to contaminate the input data of an LLM to mislead it into completing an attacker-chosen task instead of the intended...
Joshua Ward, Bochao Gu, Chi-Hua Wang +1 more
Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice, two...
Dyna Soumhane Ouchebara, Stéphane Dupont
The significant increase in software production, driven by the acceleration of development cycles over the past two decades, has led to a steady rise...
Yinan Zhong, Qianhao Miao, Yanjiao Chen +3 more
Large Language Models (LLMs) have been integrated into many applications (e.g., web agents) to perform more sophisticated tasks. However,...
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