2,529+ academic papers on AI security, attacks, and defenses
Showing 881–900 of 969 papers
Clear filtersMeng Tong, Yuntao Du, Kejiang Chen +2 more
Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks...
Zizhao Wang, Dingcheng Li, Vaishakh Keshava +4 more
Large Language Model (LLM) agents can leverage tools such as Google Search to complete complex tasks. However, this tool usage introduces the risk of...
Xiaogeng Liu, Chaowei Xiao
Recent advancements in jailbreaking large language models (LLMs), such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery....
Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi +2 more
The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving...
Kuofeng Gao, Yiming Li, Chao Du +4 more
Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are...
Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov +4 more
Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction...
Santhosh KumarRavindran
The rapid adoption of large language models (LLMs) in enterprise systems exposes vulnerabilities to prompt injection attacks, strategic deception,...
Buyun Liang, Liangzu Peng, Jinqi Luo +3 more
Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising...
Yu Cui, Sicheng Pan, Yifei Liu +2 more
Large language models (LLMs) have been widely deployed in Conversational AIs (CAIs), while exposing privacy and security threats. Recent research...
Yanjie Li, Yiming Cao, Dong Wang +1 more
Multimodal agents built on large vision-language models (LVLMs) are increasingly deployed in open-world settings but remain highly vulnerable to...
Xiangxiang Chen, Peixin Zhang, Jun Sun +2 more
Model quantization is a popular technique for deploying deep learning models on resource-constrained environments. However, it may also introduce...
Guangyu Shen, Siyuan Cheng, Xiangzhe Xu +4 more
Large Language Models (LLMs) can acquire deceptive behaviors through backdoor attacks, where the model executes prohibited actions whenever secret...
Yulin Chen, Haoran Li, Yuan Sui +2 more
With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However,...
Rabeya Amin Jhuma, Mostafa Mohaimen Akand Faisal
This study explored how in-context learning (ICL) in large language models can be disrupted by data poisoning attacks in the setting of public health...
Maraz Mia, Mir Mehedi A. Pritom
Explainable Artificial Intelligence (XAI) has aided machine learning (ML) researchers with the power of scrutinizing the decisions of the black-box...
Tanqiu Jiang, Min Bai, Nikolaos Pappas +2 more
Vision-language model (VLM)-based web agents increasingly power high-stakes selection tasks like content recommendation or product ranking by...
Fatmazohra Rezkellah, Ramzi Dakhmouche
With the increasing adoption of Large Language Models (LLMs), more customization is needed to ensure privacy-preserving and safe generation. We...
Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol +2 more
Large Language Models (LLMs) have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn...
Sanket Badhe
We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses...
Xinzhe Huang, Wenjing Hu, Tianhang Zheng +5 more
Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with...
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