2,077+ academic papers on AI security, attacks, and defenses
Showing 1701–1720 of 2,077 papers
Tenghui Huang, Jinbo Wen, Jiawen Kang +8 more
Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to...
Elias Hossain, Swayamjit Saha, Somshubhra Roy +1 more
Even when prompts and parameters are secured, transformer language models remain vulnerable because their key-value (KV) cache during inference...
Qiusi Zhan, Angeline Budiman-Chan, Abdelrahman Zayed +3 more
Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain...
Qiusi Zhan, Angeline Budiman-Chan, Abdelrahman Zayed +3 more
Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain...
Masahiro Kaneko, Zeerak Talat, Timothy Baldwin
Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the...
Masahiro Kaneko, Timothy Baldwin
Adversarial attacks by malicious users that threaten the safety of large language models (LLMs) can be viewed as attempts to infer a target property...
Mansi Phute, Matthew Hull, Haoran Wang +6 more
Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversarial...
Bo-Han Feng, Chien-Feng Liu, Yu-Hsuan Li Liang +9 more
Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While...
Navreet Kaur, Hoda Ayad, Hayoung Jung +3 more
Language model users often embed personal and social context in their questions. The asker's role -- implicit in how the question is framed --...
Yue Liu, Zhenchang Xing, Shidong Pan +1 more
In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex...
Amirkia Rafiei Oskooei, Mehmet S. Aktas
The proficiency of Large Language Models (LLMs) in processing structured data and adhering to syntactic rules is a capability that drives their...
Jie Zhang, Meng Ding, Yang Liu +2 more
We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural...
Asmita Mohanty, Gezheng Kang, Lei Gao +1 more
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based,...
Shivam Ratnakar, Sanjay Raghavendra
Integration of Large Language Models with search/retrieval engines has become ubiquitous, yet these systems harbor a critical vulnerability that...
Alireza Heshmati, Saman Soleimani Roudi, Sajjad Amini +2 more
Existing adversarial attacks often neglect perturbation sparsity, limiting their ability to model structural changes and to explain how deep neural...
Yiyang Huang, Liang Shi, Yitian Zhang +2 more
Large Vision-Language Models (LVLMs) excel in diverse cross-modal tasks. However, object hallucination, where models produce plausible but inaccurate...
Xiaofan Li, Xing Gao
The Model Context Protocol (MCP) is an emerging open standard that enables AI-powered applications to interact with external tools through structured...
Guangyu Lin, Li Lin, Christina P. Walker +2 more
The rapid proliferation of AI-generated content, driven by advances in generative adversarial networks, diffusion models, and multimodal large...
Dimitris Stefanopoulos, Andreas Voskou
This report presents the winning solution for Task 1 of Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics Discovery...
David Peer, Sebastian Stabinger
Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent...
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