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 941–960 of 1,175 papers
Clear filtersJanet Jenq, Hongda Shen
Multimodal product retrieval systems in e-commerce platforms rely on effectively combining visual and textual signals to improve search relevance and...
Mohammad Karami, Mohammad Reza Nemati, Aidin Kazemi +3 more
Artificial intelligence (AI) has shown great potential in medical imaging, particularly for brain tumor detection using Magnetic Resonance Imaging...
Raunak Somani, Aswani Kumar Cherukuri
This paper studies the integration off Large Language Models into cybersecurity tools and protocols. The main issue discussed in this paper is how...
Pedro Pereira, José Gouveia, João Vitorino +2 more
Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses...
Hongwei Yao, Yun Xia, Shuo Shao +3 more
Large language models (LLMs) increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While...
Geoff McDonald, Jonathan Bar Or
Large Language Models (LLMs) are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications,...
Botao 'Amber' Hu, Helena Rong
As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human...
Yize Liu, Yunyun Hou, Aina Sui
Large Language Models (LLMs) have been widely deployed across various applications, yet their potential security and ethical risks have raised...
Amy Chang, Nicholas Conley, Harish Santhanalakshmi Ganesan +1 more
Open-weight models provide researchers and developers with accessible foundations for diverse downstream applications. We tested the safety and...
Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena +3 more
Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks like...
Chloe Loughridge, Paul Colognese, Avery Griffin +3 more
As AI deployments become more complex and high-stakes, it becomes increasingly important to be able to estimate their risk. AI control is one...
W. K. M Mithsara, Ning Yang, Ahmed Imteaj +2 more
The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and...
Roy Rinberg, Adam Karvonen, Alexander Hoover +2 more
As large AI models become increasingly valuable assets, the risk of model weight exfiltration from inference servers grows accordingly. An attacker...
Aashray Reddy, Andrew Zagula, Nicholas Saban
Large Language Models (LLMs) remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs. Yet most evaluations focus...
Chen-Wei Chang, Shailik Sarkar, Hossein Salemi +7 more
Scam detection remains a critical challenge in cybersecurity as adversaries craft messages that evade automated filters. We propose a Hierarchical...
Daniyal Ganiuly, Assel Smaiyl
Large Language Models (LLMs) are increasingly used in intelligent systems that perform reasoning, summarization, and code generation. Their ability...
Hamin Koo, Minseon Kim, Jaehyung Kim
Identifying the vulnerabilities of large language models (LLMs) is crucial for improving their safety by addressing inherent weaknesses. Jailbreaks,...
Xin Liu, Aoyang Zhou, Aoyang Zhou
Visual-Language Pre-training (VLP) models have achieved significant performance across various downstream tasks. However, they remain vulnerable to...
Berk Atil, Rebecca J. Passonneau, Fred Morstatter
Large language models (LLMs) undergo safety alignment after training and tuning, yet recent work shows that safety can be bypassed through jailbreak...
Kasimir Schulz, Amelia Kawasaki, Leo Ring
Large language models (LLMs) are widely deployed across various applications, often with safeguards to prevent the generation of harmful or...
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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