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 221–240 of 926 papers
Clear filtersHaoyu Wang, Wei Zhao, Yedi Zhang +2 more
Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or...
Tobias Kaisar, Aritra Dhar
Skills extend an agent's capabilities by injecting instructions and information into the context, and are widely used by agents such as OpenClaw and...
Zawad Yalmie Sazid, Robert Abbas
Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and...
Shouli Wang, Yanfeng Jia, Zhihao Ou +6 more
During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high...
Shouli Wang, Yanfeng Jia, Zhihao Ou +6 more
During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high...
Jihun Han, Yejin Jang, Byung Il Kwak +1 more
LLM-based agents extend their capabilities through third-party skills that provide task-specific instructions, scripts, and tool-use procedures....
Soohan Lim, Hyundong Jin, Yo-Sub Han
Code watermarking supports provenance tracking for code generated by LLMs. Modifying token selection to embed watermarks as an LLM generates code can...
Wenxin Wu, Lingyong Yan, Lei Sha +2 more
LLM agents increasingly rely on reusable Skills for complex, multi-step tasks, creating a critical supply-chain attack surface where poisoned Skill...
Jiaqing Li, Shide Zhou, Zhibo Zhang +3 more
Machine unlearning has emerged as a crucial mechanism for removing hazardous knowledge and enforcing safety alignment in Large Language Models...
Yan Wang, Zhihao Zhang, Ke Chen +5 more
LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific...
Yaohua Liu, Yifan Guo, Jiaxin Gao
A key bottleneck in adversarial transfer is a trajectory-level geometric disconnect: ambient gradients often drift away from the intrinsic data...
Yu 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...
Yunjia Zheng, Bintang Dwi Marthen, Zachary Pan +5 more
Coding agents have become real users of high-performance computing (HPC) systems, yet today's HPC abstractions, interfaces, and policies remain...
Qiuyu Ren, Sudipta Paria, Aritra Dasgupta +1 more
Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations. Existing...
Ozgur Can Seckin, Shalmoli Ghosh, Alessandro Flammini +3 more
Large language models (LLMs) can influence people's beliefs, yet little is known about whether and how they can manipulate each other. To investigate...
Zhuo Liu, Moxin Li, Zhixin Ma +3 more
Large language model (LLM) agents are vulnerable to safety risks such as injected malicious instructions or misleading information, motivating...
Shuming Liu, Zhifang Zhang, Suqin Yuan +3 more
Vision-language models (VLMs) exhibit strong multimodal capabilities but remain vulnerable to backdoors implanted through poisoned fine-tuning data....
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...
Sayan Biswas, Jade Garcia Bourrée, Rachid Guerraoui +5 more
Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a...
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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