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 321–340 of 562 papers
Clear filtersZheng Yu, Wenxuan Shi, Xinqian Sun +3 more
Automated Vulnerability Repair (AVR) systems, especially those leveraging large language models (LLMs), have demonstrated promising results in...
Zheng Yu, Wenxuan Shi, Xinqian Sun +3 more
Automated Vulnerability Repair (AVR) systems, especially those leveraging large language models (LLMs), have demonstrated promising results in...
Yanbang Sun, Quan Luo, Yuelin Wang +6 more
Network protocols are the foundation of modern communication, yet their implementations often contain semantic vulnerabilities stemming from...
Amirpasha Mozaffari, Amanda Duarte, Lina Teckentrup +8 more
The rapid adoption of AI in Earth system science promises unprecedented speed and fidelity in the generation of climate information. However, this...
Xiaoguang Li, Hanyi Wang, Yaowei Huang +6 more
Shuffler-based differential privacy (shuffle-DP) is a privacy paradigm providing high utility by involving a shuffler to permute noisy report from...
Yuchen Shi, Huajie Chen, Heng Xu +6 more
Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources....
Kelly L Vomo-Donfack, Adryel Hoszu, Grégory Ginot +1 more
Federated learning (FL) faces two structural tensions: gradient sharing enables data-reconstruction attacks, while non-IID client distributions...
Jiaxun Guo, Ziyuan Yang, Mengyu Sun +3 more
The rapid adoption of Large Language Models (LLMs) has transformed modern software development by enabling automated code generation at scale. While...
Maheep Chaudhary
Humans often become more self-aware under threat, yet can lose self-awareness when absorbed in a task; we hypothesize that language models exhibit...
Aradhye Agarwal, Gurdit Siyan, Yash Pandya +3 more
Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon...
Yuhang Li, Yajie Wang, Xiangyun Tang +3 more
Secure aggregation is a foundational building block of privacy-preserving learning, yet achieving robustness under adversarial behavior remains...
Pearl Mody, Mihir Panchal, Rishit Kar +2 more
Large language model (LLM) agents are increasingly deployed in long running workflows, where they must preserve user and task state across many...
Junjie Chu, Xinyue Shen, Ye Leng +3 more
The rapid growth of research in LLM safety makes it hard to track all advances. Benchmarks are therefore crucial for capturing key trends and...
Hongduan Tian, Xiao Feng, Ziyuan Zhao +3 more
Large language models (LLMs) have recently demonstrated impressive capabilities in reasoning tasks. Currently, mainstream LLM reasoning frameworks...
Minseok Choi, Dongjin Kim, Seungbin Yang +5 more
With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety guardrails to moderate their...
Zhongxi Wang, Yueqian Lin, Jingyang Zhang +2 more
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to...
Rong Fu, Yiqing Lyu, Chunlei Meng +9 more
Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt...
Xiangyang Zhu, Yuan Tian, Qi Jia +14 more
The success of large language models (LLMs) in scientific domains has heightened safety concerns, prompting numerous benchmarks to evaluate their...
Yu Lin, Qizhi Zhang, Wenqiang Ruan +6 more
The rapid development of large language models (LLMs) has driven the widespread adoption of cloud-based LLM inference services, while also bringing...
Rahul Marchand, Art O Cathain, Jerome Wynne +5 more
Large language models (LLMs) increasingly act as autonomous agents, using tools to execute code, read and write files, and access networks, creating...
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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