AI Threat Alert indexes 3,771+ 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 3201–3220 of 3,771 papers
Viet Nguyen, Vishal M. Patel
Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised significant...
Yigitcan Kaya, Anton Landerer, Stijn Pletinckx +3 more
Prompt injection attacks pose a critical threat to large language models (LLMs), with prior work focusing on cutting-edge LLM applications like...
Amr Gomaa, Ahmed Salem, Sahar Abdelnabi
As language models evolve into autonomous agents that act and communicate on behalf of users, ensuring safety in multi-agent ecosystems becomes a...
Janet Jenq, Hongda Shen
Multimodal product retrieval systems in e-commerce platforms rely on effectively combining visual and textual signals to improve search relevance and...
Ishan Kavathekar, Hemang Jain, Ameya Rathod +2 more
Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents through tool use, planning, and decision-making abilities,...
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...
Hadi Reisizadeh, Jiajun Ruan, Yiwei Chen +3 more
Unlearning in large language models (LLMs) is critical for regulatory compliance and for building ethical generative AI systems that avoid producing...
Cyril Vallez, Alexander Sternfeld, Andrei Kucharavy +1 more
As the role of Large Language Models (LLM)-based coding assistants in software development becomes more critical, so does the role of the bugs they...
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...
Tim Beyer, Jonas Dornbusch, Jakob Steimle +3 more
The rapid expansion of research on Large Language Model (LLM) safety and robustness has produced a fragmented and oftentimes buggy ecosystem of...
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...
Oshando Johnson, Alexandra Fomina, Ranjith Krishnamurthy +3 more
The prevalence of security vulnerabilities has prompted companies to adopt static application security testing (SAST) tools for vulnerability...
Hirohane Takagi, Gouki Minegishi, Shota Kizawa +2 more
Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms...
Hao Zhu, Jia Li, Cuiyun Gao +7 more
Large language models (LLMs) have achieved remarkable progress in code understanding tasks. However, they demonstrate limited performance in...
Shiyin Lin
Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness,...
Mohammad Atif Quamar, Mohammad Areeb, Mikhail Kuznetsov +2 more
Aligning large language models (LLMs) with human values is crucial for safe deployment. Inference-time techniques offer granular control over...
Geoff McDonald, Jonathan Bar Or
Large Language Models (LLMs) are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications,...
Wendong Xu, Chujie Chen, He Xiao +8 more
Large Language Model (LLM) inference services demand exceptionally high availability and low latency, yet multi-GPU Tensor Parallelism (TP) makes...
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...
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 3,771+ 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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