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 101–120 of 1,050 papers
Clear filtersAditya Nawal, Manit Baser, Mohan Gurusamy
AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating...
Mohammadreza Hallajiyan, Xueren Ge, Athish Pranav Dharmalingam +4 more
The growing integration of artificial intelligence (AI) and machine learning (ML) in medical systems requires effective measures to address emerging...
Mohan Zhang, Yuqi Jia, Zhen Tan +4 more
LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or...
Ziyang You, Huilong He, Xiaoke Yang +1 more
Cryptographic watermarking is a leading defense for attributing text generated by large language models (LLMs). Existing schemes, including KGW,...
Matteo Gioele Collu, Riccardo Conte, Alberto Giaretta +4 more
In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained...
Avidan Shah, Jannik Brinkmann, Rico Angell
As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial...
Chenxi Wang, Ruiyang Huang, Jiayan Sun +2 more
Latent-based multi-agent systems replace parts of explicit inter-agent communication with hidden representations, offering a new direction for...
Yongxiang Li, Moxin Li, Zhixin Ma +4 more
Large Language Model (LLM) agents remain vulnerable to safety threats from the external environment, where attackers inject adversarial content into...
Ruoqi Guo, Yi Liu, Gelei Deng +7 more
Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from...
Yu Yin, Shuai Wang, Bevan Koopman +1 more
Recent generative engine optimisation (GEO) research has shown that prompt-injection attacks can push a target product to the top of an LLM's...
Yuan Tian, Bing Hu, Fang Wu +3 more
Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly...
Qiyuan Wang, Yao Li, Raymond K. W. Wong
Despite recent progress in backdoor attacks, existing methods remain susceptible to post-training defenses that erase the backdoor through...
Abile Jean, Kuniyilh S
Cyber-Physical Systems (CPS) integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids,...
Khang Tran, Yazan Boshmaf, Issa Khalil +3 more
Code Large Language Models (CLLMs) serve as the core of modern code agents, enabling developers to automate complex software development tasks. In...
Snehasis Mukhopadhyay
Jailbreak attacks on multimodal AI systems remain underexplored, even though unsafe image generation can have more severe consequences than unsafe...
Dongyoon Hahm, Dylan Hadfield-Menell, Kimin Lee
Reinforcement Learning from Human Feedback (RLHF) is the standard method to align Large Language Models (LLMs) with human preferences. In this work,...
Akindoyin Akinrele, Shreyank N Gowda
Prompt injection poses a critical threat to the safe deployment of large language models, yet existing detection approaches are typically evaluated...
Zedian Shao, Charles Fleming, Teodora Baluta
Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely...
Kevin Kuo, Chhavi Yadav, Virginia Smith
Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an...
Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh +1 more
Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have...
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