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 361–380 of 407 papers
Clear filtersYiyang Huang, Liang Shi, Yitian Zhang +2 more
Large Vision-Language Models (LVLMs) excel in diverse cross-modal tasks. However, object hallucination, where models produce plausible but inaccurate...
Guangyu Lin, Li Lin, Christina P. Walker +2 more
The rapid proliferation of AI-generated content, driven by advances in generative adversarial networks, diffusion models, and multimodal large...
Yang Feng, Xudong Pan
Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language...
Eduard Andrei Cristea, Petter Molnes, Jingyue Li
Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools...
Yuexiao Liu, Lijun Li, Xingjun Wang +1 more
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have gained significant attention due to their objective and verifiable...
Muslim Chochlov, Gul Aftab Ahmed, James Vincent Patten +4 more
Source code clones pose risks ranging from intellectual property violations to unintended vulnerabilities. Effective and efficient scalable clone...
Ahmed Aly, Essam Mansour, Amr Youssef
Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these...
Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1 more
As Large Language Models (LLMs) gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern....
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3 more
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires...
Jiarui Li, Yuhan Chai, Lei Du +3 more
Rule-based network intrusion detection systems play a crucial role in the real-time detection of Web attacks. However, most existing works primarily...
Ruben Belo, Marta Guimaraes, Claudia Soares
Large Language Models are susceptible to jailbreak attacks that bypass built-in safety guardrails (e.g., by tricking the model with adversarial...
Siyuan Li, Aodu Wulianghai, Xi Lin +4 more
With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task...
Masoud Makrehchi
Artificial Intelligence (AI) has emerged as both a continuation of historical technological revolutions and a potential rupture with them. This paper...
Sifan Li, Hongkai Chen, Yujun Cai +4 more
Vision Language Models (VLMs) have achieved impressive progress in multimodal reasoning; yet, they remain vulnerable to hallucinations, where outputs...
Ozan K. Tonguz, Federico Taschin
One of the major problems in Machine Learning (ML) and Artificial Intelligence (AI) is the fact that the probability distribution of the test data in...
Han Zhu, Juntao Dai, Jiaming Ji +8 more
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are...
Jiahao Liu, Bonan Ruan, Xianglin Yang +5 more
LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of...
Zhuochen Yang, Kar Wai Fok, Vrizlynn L. L. Thing
Large language models have gained widespread attention recently, but their potential security vulnerabilities, especially privacy leakage, are also...
Yuyi Huang, Runzhe Zhan, Lidia S. Chao +2 more
As large language models (LLMs) are increasingly deployed for complex reasoning tasks, Long Chain-of-Thought (Long-CoT) prompting has emerged as a...
MingSheng Li, Guangze Zhao, Sichen Liu
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal perception and generation, yet their safety alignment remains a...
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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AI Threat Alert