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 109 papers
Clear filtersSaeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +3 more
The Model Context Protocol (MCP) enables Large Language Models to integrate external tools through structured descriptors, increasing autonomy in...
Yuhang Huang, Junchao Li, Boyang Ma +6 more
Embodied AI systems integrate language models with real world sensing, mobility, and cloud connected mobile apps. Yet while model jailbreaks have...
M Zeeshan, Saud Satti
Multimodal Artificial Intelligence (AI) systems, particularly Vision-Language Models (VLMs), have become integral to critical applications ranging...
Ruichao Liang, Le Yin, Jing Chen +5 more
LLM-based multi-agent systems (MASs) have reshaped the digital landscape with their emergent coordination and problem-solving capabilities. However,...
Badhan Chandra Das, Md Tasnim Jawad, Md Jueal Mia +2 more
Large Vision Language Models (LVLMs) demonstrate strong capabilities in multimodal reasoning and many real-world applications, such as visual...
Siyang Cheng, Gaotian Liu, Rui Mei +7 more
The rapid adoption of large language models (LLMs) has brought both transformative applications and new security risks, including jailbreak attacks...
Shuyuan Liu, Jiawei Chen, Xiao Yang +2 more
With the widespread application of large language models (LLMs) in various fields, the security challenges they face have become increasingly...
Seif Ikbarieh, Kshitiz Aryal, Maanak Gupta
The rapid expansion of the Internet of Things (IoT) is reshaping communication and operational practices across industries, but it also broadens the...
Xu Liu, Yan Chen, Kan Ling +4 more
The widespread deployment of Large Language Models (LLMs) as public-facing web services and APIs has made their security a core concern for the web...
Minseok Kim, Hankook Lee, Hyungjoon Koo
Large language models (LLMs) are reshaping numerous facets of our daily lives, leading widespread adoption as web-based services. Despite their...
Seif Ikbarieh, Maanak Gupta, Elmahedi Mahalal
The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and...
Md. Mehedi Hasan, Ziaur Rahman, Rafid Mostafiz +1 more
This paper presents a real-time modular defense system named Sentra-Guard. The system detects and mitigates jailbreak and prompt injection attacks...
Sidhant Narula, Javad Rafiei Asl, Mohammad Ghasemigol +2 more
Large Language Models (LLMs) remain vulnerable to multi-turn jailbreak attacks. We introduce HarmNet, a modular framework comprising ThoughtNet, a...
Zijie Xu, Minfeng Qi, Shiqing Wu +4 more
Multi-agent systems powered by large language models are advancing rapidly, yet the tension between mutual trust and security remains underexplored....
Qilin Liao, Anamika Lochab, Ruqi Zhang
Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplored...
Kate Glazko, Jennifer Mankoff
Generative AI risks such as bias and lack of representation impact people who do not interact directly with GAI systems, but whose content does:...
ChenYu Wu, Yi Wang, Yang Liao
Large language models (LLMs) are increasingly vulnerable to multi-turn jailbreak attacks, where adversaries iteratively elicit harmful behaviors that...
Zixuan Liu, Yi Zhao, Zhuotao Liu +4 more
Machine Learning (ML)-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by...
Caelin Kaplan, Alexander Warnecke, Neil Archibald
AI models are being increasingly integrated into real-world systems, raising significant concerns about their safety and security. Consequently, AI...
Zicheng Liu, Lige Huang, Jie Zhang +3 more
The increasing autonomy of Large Language Models (LLMs) necessitates a rigorous evaluation of their potential to aid in cyber offense. Existing...
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