2,077+ academic papers on AI security, attacks, and defenses
Showing 1821–1840 of 2,045 papers
Clear filtersInes Altemir Marinas, Anastasiia Kucherenko, Alexander Sternfeld +1 more
The performance of Large Language Models (LLMs) is determined by their training data. Despite the proliferation of open-weight LLMs, access to LLM...
Mikhail Terekhov, Alexander Panfilov, Daniil Dzenhaliou +4 more
AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a...
Yongding Tao, Tian Wang, Yihong Dong +4 more
Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples...
MingSheng Li, Guangze Zhao, Sichen Liu
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal perception and generation, yet their safety alignment remains a...
Yifan Zhu, Lijia Yu, Xiao-Shan Gao
In recent years, data poisoning attacks have been increasingly designed to appear harmless and even beneficial, often with the intention of verifying...
Dennis Rall, Bernhard Bauer, Mohit Mittal +1 more
Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflows like...
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity...
Milad Nasr, Nicholas Carlini, Chawin Sitawarin +11 more
How should we evaluate the robustness of language model defenses? Current defenses against jailbreaks and prompt injections (which aim to prevent an...
Xiaonan Si, Meilin Zhu, Simeng Qin +7 more
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but are vulnerable to corpus poisoning and...
Brandon Lit, Edward Crowder, Daniel Vogel +1 more
AI chatbots are an emerging security attack vector, vulnerable to threats such as prompt injection, and rogue chatbot creation. When deployed in...
Ragib Amin Nihal, Rui Wen, Kazuhiro Nakadai +1 more
Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints...
Vinay Bamil
Recent advances in large language models have enabled developers to generate software by conversing with artificial intelligence systems rather than...
Debeshee Das, Luca Beurer-Kellner, Marc Fischer +1 more
The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt...
Abhishek K. Mishra, Antoine Boutet, Lucas Magnana
Large Language Models (LLMs) are increasingly deployed across multilingual applications that handle sensitive data, yet their scale and linguistic...
Aofan Liu, Lulu Tang
Vision-Language Models (VLMs) have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However,...
Muxi Diao, Yutao Mou, Keqing He +6 more
The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on...
Jiyang Qiu, Xinbei Ma, Yunqing Xu +2 more
The rapid deployment of large language model (LLM)-based agents in real-world applications has raised serious concerns about their trustworthiness....
Stanisław Pawlak, Jan Dubiński, Daniel Marczak +1 more
Model merging (MM) recently emerged as an effective method for combining large deep learning models. However, it poses significant security risks....
Haoran Ou, Kangjie Chen, Xingshuo Han +4 more
Large Language Models (LLMs) have been augmented with web search to overcome the limitations of the static knowledge boundary by accessing up-to-date...
Kazuki Egashira, Robin Staab, Thibaud Gloaguen +2 more
Model pruning, i.e., removing a subset of model weights, has become a prominent approach to reducing the memory footprint of large language models...
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