AI Threat Alert indexes 3,795+ 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 1761–1780 of 3,795 papers
Zikang Ding, Junhao Li, Suling Wu +3 more
Model watermarking utilizes internal representations to protect the ownership of large language models (LLMs). However, these features inevitably...
Dimitris Mitropoulos, Nikolaos Alexopoulos, Georgios Alexopoulos +1 more
Security code reviews increasingly rely on systems integrating Large Language Models (LLMs), ranging from interactive assistants to autonomous agents...
Mohammadhossein Homaei, Iman Khazrak, Rubén Molano +2 more
Industrial Cyber-Physical Systems (ICPS) face growing threats from cyber-attacks that exploit sensor and control vulnerabilities. Digital Twin (DT)...
Jiahao Zhang, Yilong Wang, Suhang Wang
Graph neural networks (GNNs) are widely used for learning from graph-structured data in domains such as social networks, recommender systems, and...
Md Takrim Ul Alam, Akif Islam, Mohd Ruhul Ameen +2 more
Large language models (LLMs) deployed behind APIs and retrieval-augmented generation (RAG) stacks are vulnerable to prompt injection attacks that may...
Alvin Rajkomar, Pavan Sudarshan, Angela Lai +1 more
Background: Clinical trials rely on transparent inclusion criteria to ensure generalizability. In contrast, benchmarks validating health-related...
Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick
As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities,...
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6 more
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts,...
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6 more
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts,...
Iakovos-Christos Zarkadis, Christos Douligeris
Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time...
Haocheng Li, Juepeng Zheng, Shuangxi Miao +4 more
Multimodal remote sensing semantic segmentation enhances scene interpretation by exploiting complementary physical cues from heterogeneous data....
Wanjun Du, Zifeng Yuan, Tingting Chen +3 more
Existing vision-language models (VLMs) have demonstrated impressive performance in reasoning-based segmentation. However, current benchmarks are...
Kun Wang, Meng Chen, Junhao Wang +6 more
With the widespread deployment of deep-learning-based speech models in security-critical applications, backdoor attacks have emerged as a serious...
Yuntong Zhang, Sungmin Kang, Ruijie Meng +2 more
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end. In the front...
Saikat Maiti
Autonomous AI agents powered by large language models are being deployed in production with capabilities including shell execution, file system...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Zhihua Wei, Qiang Li, Jian Ruan +4 more
Large vision-language models (VLMs) often exhibit weakened safety alignment with the integration of the visual modality. Even when text prompts...
Chengwei Wei, Jung-jae Kim, Longyin Zhang +2 more
Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software...
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 3,795+ 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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