2,077+ academic papers on AI security, attacks, and defenses
Showing 101–120 of 341 papers
Clear filtersAndrew Seohwan Yu, Mohsen Hariri, Kunio Nakamura +3 more
Vision language models (VLMs) have shown significant promise in visual grounding for images as well as videos. In medical imaging research, VLMs...
Max Hellrigel-Holderbaum, Edward James Young
As AI systems advance in capabilities, measuring their safety and alignment to human values is becoming paramount. A fast-growing field of AI...
Xiaoya Lu, Yijin Zhou, Zeren Chen +6 more
Vision-Language Models (VLMs) empower embodied agents to execute complex instructions, yet they remain vulnerable to contextual safety risks where...
Suvadeep Hajra, Palash Nandi, Tanmoy Chakraborty
Safety tuning through supervised fine-tuning and reinforcement learning from human feedback has substantially improved the robustness of large...
Pengcheng Li, Jie Zhang, Tianwei Zhang +5 more
Safety alignment in large language models is typically evaluated under isolated queries, yet real-world use is inherently multi-turn. Although...
Ziling Zhou
AI agents dynamically acquire capabilities at runtime via MCP and A2A, yet no framework detects when capabilities change post-authorization. We term...
Ziling Zhou
AI agents dynamically acquire tools, orchestrate sub-agents, and transact across organizational boundaries, yet no existing security layer verifies...
Xiaowen Jiang, Andrei Semenov, Sebastian U. Stich
While spectral-based optimizers like Muon operate directly on the spectrum of updates, standard adaptive methods such as AdamW do not account for the...
Maël Jenny, Jérémie Dentan, Sonia Vanier +1 more
Most jailbreak techniques for Large Language Models (LLMs) primarily rely on prompt modifications, including paraphrasing, obfuscation, or...
Chongxin Li, Hanzhang Wang, Lian Duan
Safety prompts constitute an interpretable layer of defense against jailbreak attacks in vision-language models (VLMs); however, their efficacy is...
Ivan Lopez, Selin S. Everett, Bryan J. Bunning +10 more
Large language models (LLMs) are entering clinician workflows, yet evaluations rarely measure how clinician reasoning shapes model behavior during...
Yiling Tao, Xinran Zheng, Shuo Yang +2 more
While large language model-based agents demonstrate great potential in collaborative tasks, their interactivity also introduces security...
Zijian Ling, Pingyi Hu, Xiuyong Gao +6 more
Speech-driven large language models (LLMs) are increasingly accessed through speech interfaces, introducing new security risks via open acoustic...
Zakia Zaman, Praveen Gauravaram, Mahbub Hassan +2 more
The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard...
Brecht Verbeken, Joke Van den Broeck, Inge De Cleyn +4 more
Purpose: Higher education institutions face increasing pressure to audit course designs for generative AI (GenAI) integration. This paper presents an...
Arjun Chakraborty, Sandra Ho, Adam Cook +1 more
CTI-REALM (Cyber Threat Real World Evaluation and LLM Benchmarking) is a benchmark designed to evaluate AI agents' ability to interpret cyber threat...
Ziyu Liu, Shengyuan Ding, Xinyu Fang +7 more
Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured...
Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy
Large language models for code have achieved strong performance across diverse software analytics tasks, yet their real-world adoption remains...
Jianwei Li, Jung-Eun Kim
Backdoor attacks pose severe security threats to large language models (LLMs), where a model behaves normally under benign inputs but produces...
Chenlong Yin, Runpeng Geng, Yanting Wang +1 more
Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been...
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