2,104+ academic papers on AI security, attacks, and defenses
Showing 601–620 of 2,027 papers
Clear filtersCasey Ford, Madison Van Doren, Emily Dix
Multimodal large language models (MLLMs) are increasingly deployed in real-world systems, yet their safety under adversarial prompting remains...
Mengru Wang, Zhenqian Xu, Junfeng Fang +4 more
Large Language Models (LLMs) can acquire unintended biases from seemingly benign training data even without explicit cues or malicious content....
Yike Sun, Haotong Yang, Zhouchen Lin +1 more
Tokenization is fundamental to how language models represent and process text, yet the behavior of widely used BPE tokenizers has received far less...
Ariel Fogel, Omer Hofman, Eilon Cohen +1 more
Open-weight language models are increasingly used in production settings, raising new security challenges. One prominent threat in this context is...
Leo Schwinn, Moritz Ladenburger, Tim Beyer +3 more
Automated \enquote{LLM-as-a-Judge} frameworks have become the de facto standard for scalable evaluation across natural language processing. For...
Debargha Ganguly, Sreehari Sankar, Biyao Zhang +8 more
Current approaches to LLM safety fundamentally rely on a brittle cat-and-mouse game of identifying and blocking known threats via guardrails. We...
Jiacheng Liang, Yuhui Wang, Tanqiu Jiang +1 more
Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable...
Joachim Schaeffer, Arjun Khandelwal, Tyler Tracy
Future AI deployments will likely be monitored for malicious behaviour. The ability of these AIs to subvert monitors by adversarially selecting...
Jaehyun Kwak, Nam Cao, Boryeong Cho +3 more
Adversarial attacks against Large Vision-Language Models (LVLMs) are crucial for exposing safety vulnerabilities in modern multimodal systems. Recent...
Gautam Savaliya, Robert Aufschläger, Abhishek Subedi +2 more
Artificial intelligence systems introduce complex privacy risks throughout their lifecycle, especially when processing sensitive or high-dimensional...
Yanshu Wang, Shuaishuai Yang, Jingjing He +1 more
Large Language Models (LLMs) face increasing threats from jailbreak attacks that bypass safety alignment. While prompt-based defenses such as...
Youngji Roh, Hyunjin Cho, Jaehyung Kim
Large Language Models (LLMs) exhibit highly anisotropic internal representations, often characterized by massive activations, a phenomenon where a...
Zeming Wei, Qiaosheng Zhang, Xia Hu +1 more
Large Reasoning Models (LRMs) have achieved tremendous success with their chain-of-thought (CoT) reasoning, yet also face safety issues similar to...
Guang Yang, Xing Hu, Xiang Chen +1 more
Large language models (LLMs) for Verilog code generation are increasingly adopted in hardware design, yet remain vulnerable to backdoor attacks where...
Derin Gezgin, Amartya Das, Shinhae Kim +3 more
Recently Large Language Models (LLMs) have been used in security vulnerability detection tasks including generating proof-of-concept (PoC) exploits....
Hoang Long Do, Nasrin Sohrabi, Muneeb Ul Hassan
Large language models (LLMs) have been widely adopted in modern software development lifecycles, where they are increasingly used to automate and...
Shutong Fan, Lan Zhang, Xiaoyong Yuan
Most adversarial threats in artificial intelligence target the computational behavior of models rather than the humans who rely on them. Yet modern...
Bibhabasu Mandal, Sagnik Nandy
In sensitive applications involving relational datasets, protecting information about individual links from adversarial queries is of paramount...
Xilong Wang, Yinuo Liu, Zhun Wang +2 more
Prompt injection attacks manipulate webpage content to cause web agents to execute attacker-specified tasks instead of the user's intended ones....
Andrew Draganov, Tolga H. Dur, Anandmayi Bhongade +1 more
We present a data poisoning attack -- Phantom Transfer -- with the property that, even if you know precisely how the poison was placed into an...
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