2,077+ academic papers on AI security, attacks, and defenses
Showing 721–740 of 2,077 papers
Sen Nie, Jie Zhang, Zhuo Wang +2 more
Vision-language models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, yet remain highly vulnerable to adversarial...
Chi Zhang, Wenxuan Ding, Jiale Liu +3 more
Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks. However, their...
Nirhoshan Sivaroopan, Kanchana Thilakarathna, Albert Zomaya +6 more
Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that...
Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3 more
Secure code review is critical at the pre-commit stage, where vulnerabilities must be caught early under tight latency and limited-context...
Satyapriya Krishna, Matteo Memelli, Tong Wang +5 more
Amazon published its Frontier Model Safety Framework (FMSF) as part of the Paris AI summit, following which we presented a report on Amazon's Premier...
Harsh Chaudhari, Ethan Rathbun, Hanna Foerster +5 more
Chain-of-Thought (CoT) reasoning has emerged as a powerful technique for enhancing large language models' capabilities by generating intermediate...
Henry Chen, Victor Aranda, Samarth Keshari +2 more
Prompt-based attack techniques are one of the primary challenges in securely deploying and protecting LLM-based AI systems. LLM inputs are an...
Zahra Hashemi, Zhiqiang Zhong, Jun Pang +1 more
The rapid evolution of large language models (LLMs) has fuelled enthusiasm about their role in advancing scientific discovery, with studies exploring...
Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Autonomous unmanned aerial vehicle (UAV) systems are increasingly deployed in safety-critical, networked environments where they must operate...
Dongrui Liu, Qihan Ren, Chen Qian +40 more
The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current...
Zihan Wu, Jie Xu, Yun Peng +2 more
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability...
Gabriel Lee Jun Rong, Christos Korgialas, Dion Jia Xu Ho +3 more
Existing automated attack suites operate as static ensembles with fixed sequences, lacking strategic adaptation and semantic awareness. This paper...
Geunsik Lim
As climate-related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective...
Mengyuan Jin, Zehui Liao, Yong Xia
Multimodal Large Language Models (MLLMs) have shown remarkable capability in assisting disease diagnosis in medical visual question answering (VQA)....
Krittin Pachtrachai, Petmongkon Pornpichitsuwan, Wachiravit Modecrua +1 more
Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented...
Dezhang Kong, Zhuxi Wu, Shiqi Liu +8 more
LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when...
Mohammad Fasha, Faisal Abul Rub, Nasim Matar +2 more
Large Language Models (LLMs) have emerged as a transformative and disruptive technology, enabling a wide range of applications in natural language...
Alexandra Chouldechova, A. Feder Cooper, Solon Barocas +3 more
We argue that conclusions drawn about relative system safety or attack method efficacy via AI red teaming are often not supported by evidence...
Thomas Heverin
Prompt injection evaluations typically treat refusal as a stable, binary indicator of safety. This study challenges that paradigm by modeling refusal...
Jiahe Guo, Xiangran Guo, Yulin Hu +8 more
Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized...
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