2,077+ academic papers on AI security, attacks, and defenses
Showing 1881–1900 of 2,077 papers
Rishika Bhagwatkar, Kevin Kasa, Abhay Puri +5 more
AI agents are vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external content or tool outputs cause...
Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi +2 more
The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving...
Kuofeng Gao, Yiming Li, Chao Du +4 more
Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are...
Punya Syon Pandey, Hai Son Le, Devansh Bhardwaj +2 more
Large language models (LLMs) are increasingly deployed in contexts where their failures can have direct sociopolitical consequences. Yet, existing...
Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov +4 more
Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction...
Siwei Han, Kaiwen Xiong, Jiaqi Liu +9 more
As Large Language Model (LLM) agents increasingly gain self-evolutionary capabilities to adapt and refine their strategies through real-world...
Santhosh KumarRavindran
The rapid adoption of large language models (LLMs) in enterprise systems exposes vulnerabilities to prompt injection attacks, strategic deception,...
Shuai Zhao, Xinyi Wu, Shiqian Zhao +4 more
During fine-tuning, large language models (LLMs) are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability...
Buyun Liang, Liangzu Peng, Jinqi Luo +3 more
Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising...
Anindya Sundar Das, Kangjie Chen, Monowar Bhuyan
Pre-trained language models have achieved remarkable success across a wide range of natural language processing (NLP) tasks, particularly when...
Rui Wu, Yihao Quan, Zeru Shi +3 more
Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs...
Yu Cui, Sicheng Pan, Yifei Liu +2 more
Large language models (LLMs) have been widely deployed in Conversational AIs (CAIs), while exposing privacy and security threats. Recent research...
Yanjie Li, Yiming Cao, Dong Wang +1 more
Multimodal agents built on large vision-language models (LVLMs) are increasingly deployed in open-world settings but remain highly vulnerable to...
Xiangxiang Chen, Peixin Zhang, Jun Sun +2 more
Model quantization is a popular technique for deploying deep learning models on resource-constrained environments. However, it may also introduce...
Peichao Lai, Jinhui Zhuang, Kexuan Zhang +6 more
Automating the conversion of UI images into web code is a critical task for front-end development and rapid prototyping. Advances in multimodal large...
Rijha Safdar, Danyail Mateen, Syed Taha Ali +1 more
Artificial Intelligence (AI) and more specifically Large Language Models (LLMs) have demonstrated exceptional progress in multiple areas including...
Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam +5 more
Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by...
Guangyu Shen, Siyuan Cheng, Xiangzhe Xu +4 more
Large Language Models (LLMs) can acquire deceptive behaviors through backdoor attacks, where the model executes prohibited actions whenever secret...
Jehyeok Yeon, Isha Chaudhary, Gagandeep Singh
Large language models (LLMs) are increasingly deployed in agentic systems where they map user intents to relevant external tools to fulfill a task. A...
Chengxiao Wang, Isha Chaudhary, Qian Hu +3 more
Large Language Models (LLMs) can produce catastrophic responses in conversational settings that pose serious risks to public safety and security....
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