2,529+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 142 papers
Clear filtersJohann Knechtel, Ozgur Sinanoglu, Ramesh Karri
The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor...
Monika Jotautaitė, Maria Angelica Martinez, Ollie Matthews +1 more
We introduce a red-teaming methodology that exposes harder-to-catch attacks for coding-agent monitors, suggesting that current practices may...
Bonan Ruan, Yeqi Fu, Chuqi Zhang +3 more
GitHub Continuous Integration (CI) workflows increasingly integrate Large Language Models (LLMs) to automate review, triage, content generation, and...
Feiyue Xu, Hongsheng Hu, Chaoxiang He +9 more
Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce...
Kaifeng He, Xiaojun Zhang, Peiliang Cai +7 more
Large language models (LLMs) frequently generate defective outputs in code generation tasks, ranging from logical bugs to security vulnerabilities....
Javad Forough, Marios Kogias, Hamed Haddadi
Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via...
Junjie Yu, Pengrui Lu, Weiye Si +75 more
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of...
Mohd Ruhul Ameen, Md Takrim Ul Alam, Akif Islam
Static Application Security Testing tools help developers find security vulnerabilities before release, but they often produce many false positives....
Luyao Xu, Xiang Chen
Autonomous agent frameworks built upon large language models (LLMs) are evolving into complex, tool-integrated, and continuously operating systems,...
Yuan Xin, Yixuan Weng, Minjun Zhu +6 more
As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial...
Harry Collins, Hartmut Grote, Paul Newbury +2 more
This paper is under review in AI and Ethics This study examines whether large language models (LLMs) can reliably answer scientific questions and...
Xiaohang Yu, Hejia Geng, William Knottenbelt
Agentic systems increasingly act with user secrets for APIs, messaging platforms, and cloud services. Today's bearer-secret interfaces implement...
Zihan Liu, Yizhen Wang, Rui Wang +2 more
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for...
Jiaqi Li, Yang Zhao, Bin Sun +3 more
Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering,...
Jialiang Wang, Yuchen Liu, Hang Xu +7 more
The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At...
Patrick Vossler, Jean Feng, Venkat Sivaraman +9 more
Hospital Quality Improvement (QI) plays a critical role in optimizing healthcare delivery by translating high-level hospital goals into actionable...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing...
Yuming Xu, Mingtao Zhang, Zhuohan Ge +5 more
Retrieval-augmented generation (RAG) significantly enhances large language models (LLMs) but introduces novel security risks through external...
Mehrdad Rostamzadeh, Sidhant Narula, Nahom Birhan +2 more
The Model Context Protocol (MCP) enables large language models (LLMs) to dynamically discover and invoke third-party tools, significantly expanding...
Nirajan Acharya, Gaurav Kumar Gupta
The Model Context Protocol (MCP), introduced by Anthropic in November 2024 and now governed by the Linux Foundation's Agentic AI Foundation, has...
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