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 2681–2700 of 3,023 papers
Ahmed Aly, Essam Mansour, Amr Youssef
Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these...
Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1 more
As Large Language Models (LLMs) gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern....
Deyue Zhang, Dongdong Yang, Junjie Mu +6 more
Multimodal large language models (MLLMs) exhibit remarkable capabilities but remain susceptible to jailbreak attacks exploiting cross-modal...
ChenYu Wu, Yi Wang, Yang Liao
Large language models (LLMs) are increasingly vulnerable to multi-turn jailbreak attacks, where adversaries iteratively elicit harmful behaviors that...
Zixuan Liu, Yi Zhao, Zhuotao Liu +4 more
Machine Learning (ML)-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by...
Bin Liu, Yanjie Zhao, Guoai Xu +1 more
Large language model (LLM) agents have demonstrated remarkable capabilities in software engineering and cybersecurity tasks, including code...
Evangelos Lamprou, Julian Dai, Grigoris Ntousakis +2 more
Software supply-chain attacks are an important and ongoing concern in the open source software ecosystem. These attacks maintain the standard...
Xiaoyu Xue, Yuni Lai, Chenxi Huang +4 more
The emergence of graph foundation models (GFMs), particularly those incorporating language models (LMs), has revolutionized graph learning and...
Andrew Zhao, Reshmi Ghosh, Vitor Carvalho +4 more
Large language model (LLM) systems increasingly power everyday AI applications such as chatbots, computer-use assistants, and autonomous robots,...
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3 more
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires...
Yingguang Yang, Xianghua Zeng, Qi Wu +5 more
Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has...
Fanchao Meng, Jiaping Gui, Yunbo Li +1 more
Modern Network Intrusion Detection Systems generate vast volumes of low-level alerts, yet these outputs remain semantically fragmented, requiring...
Trilok Padhi, Pinxian Lu, Abdulkadir Erol +5 more
Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm. Prior...
Edoardo Allegrini, Ananth Shreekumar, Z. Berkay Celik
Agentic AI systems, which leverage multiple autonomous agents and Large Language Models (LLMs), are increasingly used to address complex, multi-step...
Matan Levi, Daniel Ohayon, Ariel Blobstein +3 more
Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality,...
Jianzhu Yao, Hongxu Su, Taobo Liao +4 more
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little...
Qiushi Wu, Yue Xiao, Dhilung Kirat +3 more
Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project...
Abdulrahman Alhaidari, Balaji Palanisamy, Prashant Krishnamurthy
Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses...
Wei Zou, Yupei Liu, Yanting Wang +3 more
LLM-integrated applications are vulnerable to prompt injection attacks, where an attacker contaminates the input to inject malicious instructions,...
Yibo Peng, James Song, Lei Li +6 more
Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively...
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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AI Threat Alert