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 141–160 of 1,455 papers
Clear filtersEric Liang
Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native...
Yuchen Zhang, Ning Xi, Pengbin Feng +5 more
Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety...
Vincent Koc, Patrick Erichsen, Jacob Tomlinson +3 more
Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from...
Abdelrahman Abouelenein, Marwan Torki
It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising...
Seonwoo Kim, Jinwoo Kim, Daegyu Kang +2 more
Cyber threat intelligence (CTI) reports now serve as essential resources for capturing adversary tactics, techniques, and procedures observed in...
Thamilvendhan Munirathinam
Agent-memory frameworks - mem0, Letta/MemGPT, Cognee, Zep/Graphiti, MemoryOS, MemTensor - each ship their own SDK, storage layout, and operational...
Botao Amber Hu, Helena Rong, Max Van Kleek
As autonomous language model agents proliferate, forming an emerging agentic web with real-world consequences, what credibility signals can you use...
Shahinul Hoque, Jinghuai Zhang, Jinyuan Sun +1 more
Per-token billing is now the standard pricing model for commercial large language models (LLMs), so the honesty of reported token counts directly...
Mark Vero, Fabian Kaczmarczyck, Ivan Petrov +6 more
Honeypots are decoy systems mimicking real system components designed to defend against cyber attacks. Recently, LLMs increasingly serve as...
Leyi Qi, Yiming Li, Siyuan Liang +2 more
Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious...
Dongrui Liu, Yu Li, Zhonghao Yang +47 more
Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources....
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
LLM-based coding assistants are seeing rapid adoption, offering substantial gains in developer productivity. As organizations increasingly ship code...
Zhibo Zhang, Yuxi Li, Zhen Ouyang +2 more
Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization...
Qihan Deng, Minghua Zhang, Yang Yang +1 more
Pilot readback of Air Traffic Control (ATC) voice instructions is a primary safeguard against miscommunication in air transportation. However,...
Sivana Hamer, Pat Morrison, William Enck +6 more
Software supply chains, while providing immense economic and software development value, are only as strong as their weakest link. Over the past...
Jinze Gu, Qinghua Mao, Xi Lin +1 more
Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora,...
Yaoyu Zhao, Yichen Xu, Oliver Bračevac +3 more
LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The...
Jaydip Sen
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses...
Chao Ding, Mouxiao Bian, Tianbin Li +12 more
Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because...
Víctor Mayoral-Vilches
We present CAI Dataset, a fourteen-month corpus of cybersecurity LLM trajectories collected through the open-source CAI agent framework, built in...
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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