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 81–100 of 146 papers
Clear filtersJiahao 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...
Liran Tal, Johannes Kloos, Arsenii Rudich +2 more
We ran 300 repeated vulnerability-finding scans to measure how repeatable agentic large language model (LLM) security review is on the same...
Achraf Hsain, Sultan Almuhammadi
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata...
Jiaqi Luo, Jiarun Dai, Zhile Chen +8 more
Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines...
Xu Yang, Zhizhou Sha, Junbo Li +10 more
As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such...
Haowei Qian
As LLM agents proliferate in prediction markets and collective decision-making, they risk a cognitive monoculture: agents built on shared foundation...
Junfeng Guo Heng Huang
While real-world applications of reinforcement learning (RL) are becoming increasingly popular, the security of RL systems deserve more attention and...
Md Jafrin Hossain, Mohammad Arif Hossain, Weiqi Liu +1 more
Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly...
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...
Tarun Sharma
Retrieval-augmented generation (RAG) agents increasingly run with persistent memory that accumulates across user sessions. This creates a new attack...
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 --...
Xinhai Zou, Chang Zhao, Alireza Aghabagherloo +3 more
Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently...
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...
Mayank Raj, Nathaniel D. Bastian, Lance Fiondella +1 more
Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These...
Jun Wen Leong
We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when...
Lin Sun, Heming Zhang, Xiangzheng Zhang
Production LLM systems accumulate reusable operational experience, but the practical deployment issue is not merely whether such experience can help....
Hongzhou Rao, Zikan Dong, Yanjie Zhao +2 more
With the widespread adoption of Large Language Models (LLMs) in software engineering (SE) tasks such as code understanding, debugging, and...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert