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 181–200 of 748 papers
Clear filtersYingying Zhao, Chengyin Hu, Qike Zhang +7 more
Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies...
Jianhao Chen, Haoyang Chen, Hanjie Zhao +2 more
The rapid evolution of Vision-Language Models (VLMs) has catalyzed unprecedented capabilities in artificial intelligence; however, this continuous...
Junyu Ren, Xingjian Pan, Wensheng Gan +1 more
Prompt injection has emerged as a critical security threat to large language models (LLMs), yet existing studies predominantly focus on...
Ravikumar Balakrishnan, Sanket Mendapara, Ankit Garg
We study typographic prompt injection attacks on vision-language models (VLMs), where adversarial text is rendered as images to bypass safety...
Yulin Chen, Tri Cao, Haoran Li +7 more
Web agents powered by vision-language models (VLMs) enable autonomous interaction with web environments by perceiving and acting on both visual and...
Qingchao Shen, Zibo Xiao, Lili Huang +3 more
Large Language Models (LLMs) are increasingly deployed across diverse domains, yet their vulnerability to jailbreak attacks, where adversarial inputs...
Dominik Blain
We present COBALT-TLA, a neuro-symbolic verification loop that pairs an LLM with TLC, the TLA+ model checker, in an automated REPL. The LLM generates...
Gamze Kirman Tokgoz, Onat Gungor, Tajana Rosing +1 more
Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of...
Navid Azimi, Aditya Prakash, Yao Wang +1 more
Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications....
Yuanbo Xie, Yingjie Zhang, Yulin Li +5 more
Retrieval-Augmented Generation (RAG) systems augment large language models with external knowledge, yet introduce a critical security vulnerability:...
Hanzhi Liu, Chaofan Shou, Hongbo Wen +3 more
Large language model (LLM) agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers....
Wenpeng Xing, Moran Fang, Guangtai Wang +2 more
While Large Language Models (LLMs) have achieved remarkable performance, they remain vulnerable to jailbreak attacks that circumvent safety...
Wenkui Yang, Chao Jin, Haisu Zhu +7 more
Existing red-teaming studies on GUI agents have important limitations. Adversarial perturbations typically require white-box access, which is...
Cheng Liu, Xiaolei Liu, Xingyu Li +2 more
Existing jailbreak defense paradigms primarily rely on static detection of prompts, outputs, or internal states, often neglecting the dynamic...
Zhiheng Li, Zongyang Ma, Yuntong Pan +8 more
Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a...
Zhiheng Li, Zongyang Ma, Yuntong Pan +8 more
Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a...
Ziye Wang, Guanyu Wang, Kailong Wang
Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs), but simultaneously exposes a critical vulnerability to...
Yizhe Zeng, Wei Zhang, Yunpeng Li +3 more
While Chain-of-Thought (CoT) prompting has become a standard paradigm for eliciting complex reasoning capabilities in Large Language Models, it...
Adrian Shuai Li, Md Ajwad Akil, Elisa Bertino
Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied...
Manish Bhatt, Sarthak Munshi, Vineeth Sai Narajala +4 more
We prove that no continuous, utility-preserving wrapper defense-a function $D: X\to X$ that preprocesses inputs before the model sees them-can make...
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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