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 301–320 of 562 papers
Clear filtersArjun Chakraborty, Sandra Ho, Adam Cook +1 more
CTI-REALM (Cyber Threat Real World Evaluation and LLM Benchmarking) is a benchmark designed to evaluate AI agents' ability to interpret cyber threat...
Ziyu Liu, Shengyuan Ding, Xinyu Fang +7 more
Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured...
Zhifang Zhang, Bojun Yang, Shuo He +5 more
Despite the strong multimodal performance, large vision-language models (LVLMs) are vulnerable during fine-tuning to backdoor attacks, where...
Siddharth Srikanth, Freddie Liang, Sophie Hsu +9 more
Vision-Language-Action (VLA) models have significant potential to enable general-purpose robotic systems for a range of vision-language tasks....
Ninghui Li, Kaiyuan Zhang, Kyle Polley +1 more
This article, a lightly adapted version of Perplexity's response to NIST/CAISI Request for Information 2025-0035, details our observations and...
Chiyuan He, Zihuan Qiu, Fanman Meng +4 more
Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without...
Chiyuan He, Zihuan Qiu, Fanman Meng +4 more
Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without...
Junjie Chu, Yiting Qu, Ye Leng +4 more
Large Language Models (LLMs) are increasingly trained to align with human values, primarily focusing on task level, i.e., refusing to execute...
Qizhi Chen, Chao Qi, Yihong Huang +5 more
Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and...
Yan Tan, Xiangchen Meng, Zijun Jiang +1 more
Large language models (LLMs) have demonstrated impressive capabilities in generating software code for high-level programming languages such as...
Seung hee Choi, MinJu Jeon, Hyunwoo Oh +2 more
Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event...
Maximilian Wendlinger, Daniel Kowatsch, Konstantin Böttinger +1 more
Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners...
Marc Damie, Murat Bilgehan Ertan, Domenico Essoussi +3 more
With their increasing capabilities, Large Language Models (LLMs) are now used across many industries. They have become useful tools for software...
Chuan Guo, Juan Felipe Ceron Uribe, Sicheng Zhu +10 more
Instruction hierarchy (IH) defines how LLMs prioritize system, developer, user, and tool instructions under conflict, providing a concrete,...
Manit Baser, Alperen Yildiz, Dinil Mon Divakaran +1 more
The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing...
Amir Al-Maamari
Large Language Models (LLMs) show promise for Automated Program Repair (APR), yet their effectiveness on security vulnerabilities remains poorly...
Zhishu Liu, Kaishen Yuan, Bo Zhao +2 more
Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding...
Chenxi Li, Xianggan Liu, Dake Shen +9 more
Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that...
Yige Li, Wei Zhao, Zhe Li +6 more
Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same...
Yuxu Ge
Autonomous agents powered by large language models introduce a class of execution-layer vulnerabilities -- prompt injection, retrieval poisoning, and...
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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