2,529+ academic papers on AI security, attacks, and defenses
Showing 121–140 of 312 papers
Clear filtersYoungji 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...
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...
Matthew P. Lad, Louisa Conwill, Megan Levis Scheirer
With the rapid growth of Large Language Models (LLMs), criticism of their societal impact has also grown. Work in Responsible AI (RAI) has focused on...
Patrick Cooper, Alireza Nadali, Ashutosh Trivedi +1 more
Large language models (LLMs) are known to exhibit brittle behavior under adversarial prompts and jailbreak attacks, even after extensive alignment...
Ching-Yun Ko, Pin-Yu Chen
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for...
Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1 more
Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved...
Eliron Rahimi, Elad Hirshel, Rom Himelstein +3 more
Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering parallel decoding and...
Xinyi Hou, Shenao Wang, Yifan Zhang +4 more
Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that...
Manveer Singh Tamber, Hosna Oyarhoseini, Jimmy Lin
Research on adversarial robustness in language models is currently fragmented across applications and attacks, obscuring shared vulnerabilities. In...
Haitham S. Al-Sinani, Chris J. Mitchell
Wireless ethical hacking relies heavily on skilled practitioners manually interpreting reconnaissance results and executing complex, time-sensitive...
Mingqian Feng, Xiaodong Liu, Weiwei Yang +3 more
Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates...
Amirhossein Taherpour, Xiaodong Wang
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face...
Mingyang Liao, Yichen Wan, shuchen wu +6 more
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak...
Wenhui Zhang, Huiyu Xu, Zhibo Wang +4 more
Recent advancements in multi-model AI systems have leveraged LLM routers to reduce computational cost while maintaining response quality by assigning...
Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim +1 more
Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to...
Arther Tian, Alex Ding, Frank Chen +2 more
Decentralized large language model inference networks require lightweight mechanisms to reward high quality outputs under heterogeneous latency and...
Jarrod Barnes
As large language models (LLMs) improve, so do their offensive applications: frontier agents now generate working exploits for under $50 in compute...
Onkar Shelar, Travis Desell
Evolutionary prompt search is a practical black-box approach for red teaming large language models (LLMs), but existing methods often collapse onto a...
Yizhong Ding
Webshells remain a primary foothold for attackers to compromise servers, particularly within PHP ecosystems. However, existing detection mechanisms...
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