AI Threat Alert indexes 4,319+ 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 141–160 of 1,229 papers
Clear filtersYu Wang, Shuhao Li, Tao Yin +4 more
Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads...
Fahao Chen, Linkang Du, Jinhao Zhou +2 more
Sparse attention is widely used to accelerate long-context inference in modern large language models (LLMs), but its input-dependent execution...
Ahmed Nader Ahmed, Omar Moured, Mughni Irfan Mohammed Abdul +2 more
Long-horizon robotic tasks are vulnerable to unexpected environmental changes that can render planned actions ineffective or unsafe. To address this,...
Yurong Hao, Wen Zhou, Guowei Guan +3 more
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous...
Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez +4 more
Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is...
Hongxu Su, Jianzhu Yao, Xuechao Wang +1 more
Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into...
Zonghao Ying, Xiangfan Wu, Bo Yang +5 more
Indirect prompt injection embeds malicious instructions within external content retrieved by LLM-based agents, altering target behavior without user...
Issam Seddik, Mohamed El Amine Seddik
Securing modern AI systems against backdoor attacks remains an open challenge and requires fundamentally principled estimates of the adversary's...
Yihao Wang, Linhan Xia, Rui Liu +6 more
Large Language Model (LLM)-based agents increasingly self-evolve by editing a persistent skill document that encodes their workflow, tool-use rules,...
Hao Chen, Wenhui Dong, Ye Chen +12 more
LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community boundaries...
Hao Chen, Wenhui Dong, Ye Chen +12 more
LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community boundaries...
Mashal Zainab, Salijona Dyrmishi, Hamid Bostani +2 more
Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented...
Liang He, Sheng Wu, Haomiao Hao +3 more
Large language model (LLM) agents are increasingly evaluated on cybersecurity tasks such as vulnerability reproduction, exploitation, and patching....
Chanhee Park, Jeongho Yoon, Sungbin Han +2 more
Agentic tasks require a large language model to interact with the world, navigating information and gathering evidence across multiple steps with...
Dzung Pham, Dillon Sheils, Naina Singh +1 more
User conversations with large language models (LLMs) often contain highly sensitive personal information that can be exploited by LLM providers to...
Jie Wang, Yanbo Sun, Zheng Yan +2 more
Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have...
Patrick Kenney, Hadi Ahmadi, Denis Lusson +2 more
Tool-using large language model (LLM) agents turn credential hygiene from a storage problem into an execution-security problem. A key pasted into a...
David Schmotz, Derck Prinzhorn, Luca Beurer-Kellner +3 more
A central concern in AI safety is that agents may treat oversight as an obstacle when it conflicts with completing their goals. We study instrumental...
Luciano Maldonado
Large language models increasingly operate as persistent assistants in user-facing, shared-session, and tool-augmented settings. When users disclose...
Saim Rehman, Muhammad Shafique
Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial...
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 4,319+ 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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