AI Threat Alert indexes 4,319+ 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 61–80 of 82 papers
Clear filtersXiangyu Zhang, Yucheng Su, Lingling Fan +2 more
Modern Android apps extensively rely on native libraries, bridging Java/Kotlin and C/C++ through the Java Native Interface (JNI). This cross-language...
Zixuan Yu, Zhenheng Tang, Xiaowen Chu +1 more
As Large Language Models (LLMs) evolve into autonomous Deep Search and Deep Research Agents capable of executing long-horizon, multi-step tasks,...
Hongyu Li, Xinlong Huang, Zhan Yu +13 more
Current LLM safety benchmarks predominantly rely on binary safe or unsafe labels, lacking explanatory depth and suffering from low consistency across...
Eric Liang, Alan Luo, Kaiwen Yuan
Recent advancements in Vision-Language Models (VLMs) enable end-to-end visual grounding via language prompts and generative decoding, demonstrating...
Guoqiang Hua, Sakshi Udeshi, Matheus Eduardo Garbelini +2 more
In this paper, we present 5uGUE, a large language model (LLM)-driven hardware-in-the-loop agent, which enables the wireless fuzzing of 5G-enabled...
Zhiyue Xu, Xukai Wang, Zhiyuan Huang +3 more
Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled...
Liping Yang, Yongqi Leng, Jingting Zheng +3 more
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing...
Sadia Asif, Israel Antonio Rosales Laguan, Shumaila Asif +3 more
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative...
Yixin Wu, Rachel Hong, Jamie Morgenstern +1 more
Machine learning benchmarks act as guideposts for model development and spur the creation of training datasets intended to improve benchmark scores....
Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea
Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions,...
Qi Pang, Hanshen Xiao, Andre Manoel +4 more
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently...
Rui Melo, Andre Catarino, Claudia Mamede +2 more
Large language models can detect vulnerable code, yet the form of vulnerability encoding in their representations remains unclear. We analyze 8 code...
Hao Li, Tongyan Hu, Weidi Luo +8 more
Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw. These claw-style agents maintain...
Shruthi Gowda, Nikita Jain, Elahe Arani +1 more
Deep neural networks have achieved remarkable success on vision benchmarks, yet they continue to struggle with many generalization challenges....
Osama Shabih, Dayam Nadeem, Shahab Saquib Sohail +1 more
Large Language Models (LLMs) have significantly advanced natural language processing and artificial intelligence applications through their...
Leon Tu Nguyen, Rohan Menon, Rafael Tappe Maestro +2 more
Jailbreak methods are commonly compared using Attack Success Rate (ASR), but these comparisons depend on how responses are generated and judged. In...
Xiaoyu Wu, Zhaoxin Yu, Xinglin Xiao +1 more
Advancing physics reasoning in large language models requires scalable training data that connect physical laws, concrete problem scenarios, and...
Dixi Yao, Tahseen Rabbani, Tian Li
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it...
Hasan Demirkiran, Jens Ernstberger
AI agents take actions through tools. Some actions are sensitive, so guards that supervise them must themselves be evaluated reliably. Yet the data...
Zhiyuan Wang, Yuezhi Zhou, Ziqi Zhang +3 more
On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models...
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 4,319+ 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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