2,077+ academic papers on AI security, attacks, and defenses
Showing 21–40 of 269 papers
Clear filtersHarry Owiredu-Ashley
Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to capture...
Yi Chen, Yun Bian, Haiquan Wang +2 more
The application of large language models to code generation has evolved from one-shot generation to iterative refinement, yet the evolution of...
Bo Jiang
Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and...
Sumit Ranjan, Sugandha Sharma, Ubaid Abbas +1 more
Voice interfaces are quickly becoming a common way for people to interact with AI systems. This also brings new security risks, such as prompt...
Xisen Jin, Michael Duan, Qin Lin +4 more
As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces...
Jinman Wu, Yi Xie, Shen Lin +2 more
Safety alignment is often conceptualized as a monolithic process wherein harmfulness detection automatically triggers refusal. However, the...
Ved Sriraman, Adam Block
Best-of-N (BoN) sampling is a widely used inference-time alignment method for language models, whereby N candidate responses are sampled from a...
Junchuan Zhao, Minh Duc Vu, Ye Wang
Neural codec language models enable high-quality discrete speech synthesis, yet their inference remains vulnerable to token-level artifacts and...
Trapoom Ukarapol, Nut Chukamphaeng, Kunat Pipatanakul +1 more
The safety evaluation of large language models (LLMs) remains largely centered on English, leaving non-English languages and culturally grounded...
Zeyu Zhang, Xiangxiang Dai, Ziyi Han +2 more
Large language models (LLMs) are typically governed by post-training alignment (e.g., RLHF or DPO), which yields a largely static policy during...
Brandon Yee, Krishna Sharma
MoltBook is a large-scale multi-agent coordination environment where over 770,000 autonomous LLM agents interact without human participation,...
Sami Abuzakuk, Lucas Crijns, Anne-Marie Kermarrec +2 more
Infrastructure as code (IaC) tools automate cloud provisioning but verifying that deployed systems remain consistent with the IaC specifications...
Nancy Lau, Louis Sloot, Jyoutir Raj +6 more
Large language models (LLMs) are increasingly being deployed as software engineering agents that autonomously contribute to repositories. A major...
Manisha Mukherjee, Vincent J. Hellendoorn
Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development, yet their limited transparency in...
Ming Wen, Kun Yang, Xin Chen +4 more
Multimodal Large Language Models (MLLMs) pose critical safety challenges, as they are susceptible not only to adversarial attacks such as...
Chang Xue, Fang Liu, Jiaye Wang +2 more
Decentralized financial platforms rely heavily on Web of Trust reputation systems to mitigate counterparty risk in the absence of centralized...
Xingyu Zhu, Kesen Zhao, Liang Yi +4 more
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language reasoning, yet they remain vulnerable to hallucination,...
Kunpeng Zhang, Dongwei Xiao, Daoyuan Wu +5 more
Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing...
Lan Zhang, Chengsi Liang, Zeming Zhuang +4 more
Semantic communication (SemCom) redefines wireless communication from reproducing symbols to transmitting task-relevant semantics. However, this...
Xuan Chen, Hao Liu, Tao Yuan +3 more
Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. While recent...
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