2,560+ academic papers on AI security, attacks, and defenses
Showing 341–360 of 1,220 papers
Clear filtersJiaxun Guo, Ziyuan Yang, Mengyu Sun +3 more
The rapid adoption of Large Language Models (LLMs) has transformed modern software development by enabling automated code generation at scale. While...
Arther Tian, Alex Ding, Frank Chen +2 more
Decentralized large language model (LLM) inference networks can pool heterogeneous compute to scale serving, but they require lightweight and...
Yizhe Xie, Congcong Zhu, Xinyue Zhang +5 more
Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative...
Maheep Chaudhary
Humans often become more self-aware under threat, yet can lose self-awareness when absorbed in a task; we hypothesize that language models exhibit...
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...
Neha Nagaraja, Hayretdin Bahsi
While incorporating LLMs into systems offers significant benefits in critical application areas such as healthcare, new security challenges emerge...
Difan Jiao, Di Wang, Lijie Hu
In-context learning enables large language models to perform novel tasks through few-shot demonstrations. However, demonstrations per se can...
Achyutha Menon, Magnus Saebo, Tyler Crosse +3 more
The accelerating adoption of language models (LMs) as agents for deployment in long-context tasks motivates a thorough understanding of goal drift:...
Aradhye Agarwal, Gurdit Siyan, Yash Pandya +3 more
Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon...
Romina Omidi, Yun Dong, Binghui Wang
Google's SynthID-Text, the first ever production-ready generative watermark system for large language model, designs a novel Tournament-based method...
Yuhang Li, Yajie Wang, Xiangyun Tang +3 more
Secure aggregation is a foundational building block of privacy-preserving learning, yet achieving robustness under adversarial behavior remains...
Pearl Mody, Mihir Panchal, Rishit Kar +2 more
Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many...
Edouard Lansiaux
Federated Learning (FL) enables collaborative training of medical AI models across hospitals without centralizing patient data. However, the exchange...
Junjie Chu, Xinyue Shen, Ye Leng +3 more
The rapid growth of research in LLM safety makes it hard to track all advances. Benchmarks are therefore crucial for capturing key trends and...
Shuyi Zhou, Zeen Song, Wenwen Qiang +4 more
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this...
Zixuan Xu, Tiancheng He, Huahui Yi +7 more
Vision-language models remain susceptible to multimodal jailbreaks and over-refusal because safety hinges on both visual evidence and user intent,...
Minseok Choi, Dongjin Kim, Seungbin Yang +5 more
With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety guardrails to moderate their...
Zhongxi Wang, Yueqian Lin, Jingyang Zhang +2 more
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to...
Bhanu Pallakonda, Mikkel Hindsbo, Sina Ehsani +1 more
The proliferation of open-weight Large Language Models (LLMs) has democratized agentic AI, yet fine-tuned weights are frequently shared and adopted...
Tatiana Chakravorti, Pranav Narayanan Venkit, Sourojit Ghosh +1 more
Generative AI tools are increasingly entering academic peer review workflows, raising questions about fairness, accountability, and the legitimacy of...
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