AI Threat Alert indexes 3,023+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 441–460 of 946 papers
Clear filtersVickson Ferrel
As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers...
Jihoon Jeong
AI models of equivalent capability can exhibit fundamentally different behavioral patterns, yet no standardized instrument exists to measure these...
Yiheng Huang, Zhijia Zhao, Bihuan Chen +5 more
The model context protocol (MCP) standardizes how LLMs connect to external tools and data sources, enabling faster integration but introducing new...
Shams Tarek, Dipayan Saha, Khan Thamid Hasan +3 more
The increasing complexity of modern system-on-chip designs amplifies hardware security risks and makes manual security property specification a major...
Weidi Luo, Xiaofei Wen, Tenghao Huang +5 more
Large language models (LLMs) are increasingly deployed for everyday tasks, including food preparation and health-related guidance. However, food...
Bowen Wei, Yunbei Zhang, Jinhao Pan +5 more
Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak...
Manoj Parmar
World models -- learned internal simulators of environment dynamics -- are rapidly becoming foundational to autonomous decision-making in robotics,...
Kıvanç Kuzey Dikici, Serdar Kara, Semih Çağlar +2 more
As Large Language Models (LLMs) for code increasingly utilize massive, often non-permissively licensed datasets, evaluating data contamination...
Saeid Jamshidi, Negar Shahabi, Foutse Khomh +2 more
Software-Defined Networking (SDN) is increasingly adopted to secure Internet-of-Things (IoT) networks due to its centralized control and programmable...
Xiaoqi Li, Shipeng Ye, Wenkai Li +1 more
Smart contract vulnerabilities can cause substantial financial losses due to the immutability of code after deployment. While existing tools detect...
Yanting Wang, Jinyuan Jia
Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building...
Quanyan Zhu, Zhengye Han
This paper introduces a performative scenario optimization framework for decision-dependent chance-constrained problems. Unlike classical stochastic...
Edoardo Allegrini, Edoardo Di Paolo, Angelo Spognardi +1 more
BotVerse is a scalable, event-driven framework for high-fidelity social simulation using LLM-based agents. It addresses the ethical risks of studying...
Aiman Almasoud, Antony Anju, Marco Arazzi +6 more
LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated...
Yubo Li, Lu Zhang, Tianchong Jiang +2 more
Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a...
Yicheng Cai, Mitchell John DeStefano, Guodong Dong +5 more
As Large Language Models (LLMs) and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations,...
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3 more
Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to...
Zihao Xu, Xiao Cheng, Ruijie Meng +1 more
LLM API calls are becoming a ubiquitous program construct, yet they create a boundary that no existing program analysis can cross: runtime values...
Quan Zhang, Lianhang Fu, Lvsi Lian +5 more
Equipping LLM agents with real-world tools can substantially improve productivity. However, granting agents autonomy over tool use also transfers the...
Ruiyang Wang, Rong Pan, Zhengan Yao
Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies...
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 3,023+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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