AI Threat Alert indexes 3,406+ 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 161–180 of 981 papers
Clear filtersAaditya Pai
Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and interleave...
Fouad Bousetouane
AI agents must be evaluated as behavioral systems, not as isolated response generators. They reason across turns, call tools, preserve context,...
Chen Chen, Xiang Gao, Xianshun Wang +6 more
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive...
Rowdy Chotkan, Bulat Nasrulin, Johan Pouwelse +1 more
Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine...
Bojie Li
A personalized AI agent needs a user memory: a persistent model of who the user is, built across many conversations and consulted on each new one....
Hankyul Baek, Jaewon Noh, Sang Seo +5 more
AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they...
Jiahao Zhang, Xiuyu Li, Suhang Wang
As Large Language Model (LLM) APIs become ubiquitous, users increasingly rely on black-box fingerprinting to verify that providers are serving the...
Ismail Hossain, Sai Puppala, Md Jahangir Alam +2 more
Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent...
Zihao Wang, Yiming Li, Yutong Wu +8 more
Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content...
Yunhan Wang, Jiaan Wang, Lianzhe Huang +2 more
Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing...
Andy Wang, Parv Mahajan, David Demitri Africa +3 more
Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model...
Timothy McAllister, Sina Abdidizaji, Ivan Garibay +1 more
As LLM-based multi-agent systems (MAS) are deployed in the wild, the resilience of their collaboration structures against adversarial compromise...
Weijie Chen, Alan B. McMillan
Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each...
Jin Xie, Songze Li
Large language model (LLM) agents increasingly act on a user's behalf -- reading personal files, calling tools, transacting with external services --...
Stipe Frkovic, Metod Jazbec, Dan Zhang +3 more
Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of...
Pinran Gao, Lingxiang Wang, Ying Zhang +1 more
The rapid integration of large language models (LLMs) into mobile applications has introduced a new class of credential security risk: leaked...
Malikeh Ehghaghi, Boglárka Ecsedi, Marsha Chechik +1 more
Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly...
Naihao Deng, Yilun Zhu, Naichen Shi +2 more
Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale...
Yuchen Chen, Weisong Sun, Haocheng Huang +11 more
Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their...
Yuchen Ling, Shengcheng Yu, Zhenyu Chen +1 more
Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory,...
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 3,406+ 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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