2,583+ academic papers on AI security, attacks, and defenses
Showing 641–660 of 2,583 papers
Chiyuan He, Zihuan Qiu, Fanman Meng +4 more
Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without...
Chiyuan He, Zihuan Qiu, Fanman Meng +4 more
Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without...
Sarbartha Banerjee, Prateek Sahu, Anjo Vahldiek-Oberwagner +2 more
Rapid progress in generative AI has given rise to Compound AI systems - pipelines comprised of multiple large language models (LLM), software tools...
Junjie Chu, Yiting Qu, Ye Leng +4 more
Large Language Models (LLMs) are increasingly trained to align with human values, primarily focusing on task level, i.e., refusing to execute...
Kele Xu, Yifan Wang, Ming Feng +5 more
Human-computer interaction has traditionally relied on the acoustic channel, a dependency that introduces systemic vulnerabilities to environmental...
J Alex Corll
Prompt injection defenses are often framed as semantic understanding problems and delegated to increasingly large neural detectors. For the first...
Jiongchi Yu, Xiaolin Wen, Sizhe Cheng +3 more
Fuzz testing is one of the most effective techniques for detecting bugs and vulnerabilities in software. However, as the basis of fuzz testing,...
Frank Li
Tool-augmented LLM agents introduce security risks that extend beyond user-input filtering, including indirect prompt injection through fetched...
Chingkwun Lam, Jiaxin Li, Lingfei Zhang +1 more
Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong...
Xinhao Deng, Yixiang Zhang, Jiaqing Wu +15 more
Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks....
Lu Niu, Cheng Xue
Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and...
Qizhi Chen, Chao Qi, Yihong Huang +5 more
Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and...
Yan Tan, Xiangchen Meng, Zijun Jiang +1 more
Large language models (LLMs) have demonstrated impressive capabilities in generating software code for high-level programming languages such as...
Seung hee Choi, MinJu Jeon, Hyunwoo Oh +2 more
Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event...
Zhiyu Xue, Zimo Qi, Guangliang Liu +2 more
Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal...
Indranil Halder, Annesya Banerjee, Cengiz Pehlevan
Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial...
Raj Sanjay Shah, Jing Huang, Keerthiram Murugesan +2 more
Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the right to be...
Maximilian Wendlinger, Daniel Kowatsch, Konstantin Böttinger +1 more
Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners...
Xiangwen Wang, Ananth Balashankar, Varun Chandrasekaran
Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with...
Marc Damie, Murat Bilgehan Ertan, Domenico Essoussi +3 more
With their increasing capabilities, Large Language Models (LLMs) are now used across many industries. They have become useful tools for software...
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