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 121–140 of 875 papers
Clear filtersYiheng Huang, Zhijia Zhao, Bihuan Chen +6 more
Open source software is vulnerable to supply-chain attacks through transitive dependencies, especially malicious code injected into NPM packages....
Zhenpeng Li
Large language model (LLM)-based intrusion detection systems (IDS) are increasingly studied for security monitoring, yet their robustness against...
Jarosław A. Miszczak
The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against...
Alexandra E. Michael, Franziska Roesner
As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination,...
Dima Galat, Marian-Andrei Rizoiu
We investigate which language model evasion attacks survive state-of-the-art adversarial fine-tuning, developing strategies that sweep the top 5...
Na Li, Boyu Kuang, Hongsheng Hu +4 more
To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real...
Muntaser Syed, Marius C. Silaghi, Sheikh Abujar +2 more
Sovereign AI has become a strategic priority across the Global South, with over \$200 billion in state-led commitments announced between 2024 and...
Michael O. Eniolade
Clinical AI models can expose patients to harm when adversarial vulnerabilities go undetected, yet formal security auditing requires statistical...
Yuxin Huang, Ziming Hong, Mingming Gong +3 more
Recent diffusion-based video generation models have enabled high-quality personalized video customization through both tuning-based pipelines, which...
Roman Prosvirnin, Victor Minchenkov, Alexey Soldatov +1 more
Jailbreak-robustness research typically evaluates safety through generated responses using an LLM-as-judge approach. Such evaluations, however, are...
Qiyuan Fan, Zhi Li, Junjie Li +3 more
Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities....
Mohammadreza Rashidi
Developers now draw code from two very different sources, the accumulated human answers on sites such as Stack Overflow and the output of large...
Yuhang Yan, Linchao Mou, Bokang Yang +1 more
Beyond perception, reasoning is essential in remote sensing for advanced interpretation, inference, and decision-making. Recent advances in large...
Huihao Jing, Wenbin Hu, Shaojin Chen +10 more
The capability of LLM agents to function as the ``brain'' of a system fundamentally expands the scope of analysis beyond a standalone model....
Weifeng Yuan, Wenbo Guo, Feng Dong +2 more
LLM agents acquire new capabilities by downloading skills from open registries. Instead of browsing these catalogs manually, developers typically ask...
Anna Gerchanovsky, Lujo Bauer, Michael K. Reiter
We report on the design and evaluation of MindReader, a tool that helps a user replace her password when she is required to do so. Left to their own...
Zedong Peng, Chenggang Wang, Shangyue Zhu
Large language models (LLMs) frequently generate code with security vulnerabilities, yet these weaknesses are rarely isolated: they often span...
Arastoo Zibaeirad, Marco Vieira, Thomas Zimmermann
Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe...
Yibo Hu, Ren Wang
As multi-agent, tool-using LLM systems are deployed, a common safety net is a runtime monitor that checks each message, tool call, or step on its...
Ahmed M. Elmisery
Adversary emulation plans describe multi-step attacker procedures using MITRE ATT&CK techniques, privilege requirements, and observable telemetry....
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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