AI Threat Alert indexes 3,771+ 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 861–880 of 891 papers
Clear filtersVinod Vaikuntanathan, Or Zamir
AI agents are increasingly deployed to interact with other agents on behalf of users and organizations. We ask whether two such agents, operated by...
Zhuohao Yu, Zhiwei Steven Wu, Adam Block
Inference-time compute scaling has emerged as a powerful paradigm for improving language model performance on a wide range of tasks, but the question...
Jia Chengyu, AprilPyone MaungMaung, Huy H. Nguyen +2 more
Recent advances in vision-language models (VLMs) trained on web-scale image-text pairs have enabled impressive zero-shot transfer across a diverse...
Shuyao Gao, Minghao Huang
The deployment of Large Language Models (LLMs) has ignited concerns about technological unemployment. Existing task-based evaluations predominantly...
Fariha Tanjim Shifat, Hariswar Baburaj, Ce Zhou +2 more
Large language models (LLMs) are increasingly embedded in open-source software (OSS) ecosystems, creating complex interactions among natural language...
Qiqing Huang, Xingyu Wang, Wanda Guo +2 more
Modern 5G user equipment (UE) processes Radio Resource Control (RRC) configuration messages during early control-plane exchanges, before...
Aobo Chen, Chenxu Zhao, Chenglin Miao +1 more
Large language models (LLMs) possess strong semantic understanding, driving significant progress in data mining applications. This is further...
Matteo Migliarini, Joaquin Pereira Pizzini, Luca Moresca +3 more
Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk...
Vickson 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...
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 3,771+ 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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