AI Threat Alert indexes 3,371+ 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 21–40 of 970 papers
Clear filtersMuhammad Laiq
Timely identification of security-related bug reports is essential to minimize the window of vulnerabilities in software systems. Manually screening...
Benyamin Tafreshian, Prathamesh Dhake
Large language models (LLMs) are becoming increasingly integrated into mainstream development platforms and daily technological workflows, typically...
Petr Simecek, Elnaz Babayeva, Jiri Balhar +23 more
LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs...
Arnav Hiray, Agam Shah, Caleb Lu +3 more
We introduce CreditCardQA, the first financial literacy benchmark for numerical reasoning derived from real credit card agreements. The dataset...
Anthony Hughes, Nicole Xing, Collin Francel +2 more
As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that...
Lehan Wang, Boli Chen, Ruixue Ding +7 more
Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line...
Haichuan Hu, Chunrong Fang, Ye Shang +5 more
Automated Program Repair (APR) has benefited greatly from Large Language Models (LLMs), but existing LLM-based APR methods still struggle with...
Conor McCauley, Zeliang Kan, Jason Martin
When a language model receives conflicting instructions from different priority levels, which one does it actually follow? This question lies at the...
Conor McCauley, Zeliang Kan, Jason Martin
When a language model receives conflicting instructions from different priority levels, which one does it actually follow? This question lies at the...
Anwar Alajmi, Ayed Salman, Imtiaz Ahmad
The emergence of the recent outstanding capabilities of Arabic Language Models has opened doors for exposing their vulnerabilities. One of the major...
Korosh Vatanparvar, Ashutosh Joshi, Maria Xenochristou +11 more
Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf....
Abu Bakar Siddik
Cyber-capable AI agents combine language models with tools, memory, and execution en- vironments to perform multi-step offensive-security tasks....
Abhishek Kumar Singh, Shrey Nag, Sachita +2 more
India is a vast nation of over 1.4 billion people, varied by hundreds of diverse and locally specific traditions and cultures and 22 officially...
Haowen Dai, Zonghao Ying, Wenfeng Li +10 more
Multi-agent systems improve capability through task decomposition and role specialization, but these same mechanisms introduce an important safety...
Arseny Kravchenko, Vadim Liventsev, Innokentii Konstantinov +2 more
Autonomous LLM agents processing mixed-confidentiality data face severe security risks from prompt injection attacks and reasoning errors. While...
Ioannis Sarridis, Ioannis Kompatsiaris, Symeon Papadopoulos
Online platforms increasingly rely on automated age estimation systems to enforce minimum-age policies. Focusing on vision-based models designed for...
Andrea Ponte, Daniel Gibert, Matous Kozak +5 more
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since...
Mohan Manivannan, Dalal Alharthi
Cloud telemetry arrives at a scale that, paradoxically, makes intrusion understanding harder rather than easier. Attackers operate through legitimate...
Wenhao Lan, Shan Li, Xinhua Lai +3 more
Tool-using LLM agents process untrusted content, maintain memory, delegate across agents, and invoke side-effecting tools. Existing prompt-injection...
Gengyu Zhan
Large language models (LLMs) can summarize heterogeneous user evidence in natural language, but current LLM recommenders often collapse enduring...
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 3,371+ 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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