AI Threat Alert indexes 3,371+ 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 161–180 of 471 papers
Clear filtersZiyang Liu
Hosted-LLM providers have a silent-substitution incentive: advertise a stronger model while serving cheaper replies. Probe-after-return schemes such...
Hu Wei
AI agent systems increasingly rely on reusable non-LLM engineering infrastructure that packages tool mediation, context handling, delegation, safety...
Dongcheng Zhang, Yiqing Jiang
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a...
Ting Zhang, Yikun Li, Chengran Yang +15 more
Software vulnerabilities remain one of the most persistent threats to modern digital infrastructure. While static application security testing (SAST)...
Hailin Liu, Eugene Ilyushin, Jie Ni +1 more
Large language model (LLM) agents are vulnerable to prompt-injection attacks that propagate through multi-step workflows, tool interactions, and...
Aram Ebtekar, Michael K. Cohen
Reinforcement learners can attain high reward through novel unintended strategies. We study a Bayesian mitigation for general environments: we expand...
Xiaohua Wang, Muzhao Tian, Yuqi Zeng +20 more
Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and...
Sujan Ghimire, Parsa Mirfasihi, Muhtasim Alam Chowdhury +6 more
The globalization of integrated circuit (IC) design and manufacturing has increased the exposure of hardware intellectual property (IP) to untrusted...
Willy Carlos Tchuitcheu, Tan Lu, Ann Dooms
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We...
Georgianna, Lin, Rencong Jiang +2 more
Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management,...
Adam Stein, Davis Brown, Hamed Hassani +2 more
To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare,...
Junxiao Yang, Haoran Liu, Jinzhe Tu +9 more
Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried...
Ningyan Zhu, Huacan Wang, Jie Zhou +8 more
The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating...
Xuwei Ding, Skylar Zhai, Linxin Song +6 more
Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to...
Kevin Lira, Baldoino Fonseca, Davy Baía +2 more
Large Language Models (LLMs) have been a promising way for automated vulnerability detection. However, most prior studies have explored the use of...
Weiwei Qi, Zefeng Wu, Tianhang Zheng +4 more
Ensuring Large Language Model (LLM) safety is crucial, yet the lack of a clear understanding about safety mechanisms hinders the development of...
Ponnampalam Pirapuraj, Tamal Mondal, Sharanya Gupta +3 more
Application Programming Interfaces (APIs) are crucial to software development, enabling integration of existing systems with new applications by...
Rui Zhang, Hongwei Li, Yun Shen +6 more
The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve...
Nikolaos D. Tantaroudas, Ilias Karachalios, Andrew J. McCracken
The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor...
Shunan Zhu, Jiawei Chen, Yonghao Yu +1 more
As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving...
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 3,371+ 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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert