2,529+ academic papers on AI security, attacks, and defenses
Showing 61–80 of 440 papers
Clear filtersRicardo Bessa, Rui Claro, João Trindade +1 more
Large Language Models (LLMs) are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human...
Hanbo Huang, Xuan Gong, Yiran Zhang +2 more
Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to...
Ricardo Bessa, Rui Claro, João Trindade +1 more
The application of Machine Learning techniques in code generation is now a common practice for most developers. Tools such as ChatGPT from OpenAI...
Xiaomeng Hu, Yinger Zhang, Fei Huang +7 more
AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor...
Yuchen Chen, Yuan Xiao, Chunrong Fang +2 more
The proliferation of large language models for code (CodeLMs) and open-source contributions has heightened concerns over unauthorized use of source...
Wenhao Yuan, Chenchen Lin, Jian Chen +3 more
In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory....
Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1 more
In language model interpretability research, \textbf{circuit tracing} aims to identify which internal features causally contributed to a particular...
Yu Liang, Liangxin Liu, Longzheng Wang +5 more
Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering...
Yuanhang Li
Operating LEO mega-constellations requires translating high-level operator intents ("reroute financial traffic away from polar links under 80 ms")...
Geert Trooskens, Aaron Karlsberg, Anmol Sharma +6 more
We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows...
Zhuohao Yu, Zhiwei Steven Wu, Adam Block
Inference-time compute scaling has emerged as a powerful paradigm for improving language model performance on a wide range of tasks, but the question...
Jia Chengyu, AprilPyone MaungMaung, Huy H. Nguyen +2 more
Recent advances in vision-language models (VLMs) trained on web-scale image-text pairs have enabled impressive zero-shot transfer across a diverse...
Shuyao Gao, Minghao Huang
The deployment of Large Language Models (LLMs) has ignited concerns about technological unemployment. Existing task-based evaluations predominantly...
Matteo Migliarini, Joaquin Pereira Pizzini, Luca Moresca +3 more
Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk...
Yiheng Huang, Zhijia Zhao, Bihuan Chen +5 more
The model context protocol (MCP) standardizes how LLMs connect to external tools and data sources, enabling faster integration but introducing new...
Weidi Luo, Xiaofei Wen, Tenghao Huang +5 more
Large language models (LLMs) are increasingly deployed for everyday tasks, including food preparation and health-related guidance. However, food...
Kıvanç Kuzey Dikici, Serdar Kara, Semih Çağlar +2 more
As Large Language Models (LLMs) for code increasingly utilize massive, often non-permissively licensed datasets, evaluating data contamination...
Yanting Wang, Jinyuan Jia
Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building...
Yubo Li, Lu Zhang, Tianchong Jiang +2 more
Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a...
Yicheng Cai, Mitchell John DeStefano, Guodong Dong +5 more
As Large Language Models (LLMs) and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations,...
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