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 1–20 of 307 papers
Clear filtersJuncheng Dong, Ding Tong, Ishan Gupta +1 more
Recommendation systems thrive on personalization, where ''correctness'' is rarely a binary truth but a matter of subjective human preference. As...
Abdulkadir Külçe, Alihan Esen, Cağla Fikir +4 more
This paper presents ECHO (Enhanced Care \& Health Observer), a locally-deployable conversational health assistant for long-term chronic care...
Buzhao Liu, Xinhang Ma, Yevgeniy Vorobeychik
The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete...
Alina Klerings, Jannik Brinkmann, Heiner Stuckenschmidt +1 more
Large language models typically undergo post-training to align them with safety policies but there exist many sophisticated jailbreaks that sidestep...
Ethen Santana, Gabriel Gyaase, Hao Zheng
Hardware designs, like software, are susceptible to bugs that can introduce security vulnerabilities and create opportunities for malicious...
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi +6 more
Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated...
Nizhang Li, Zonghao Ying, Xiangfan Wu +7 more
External skills extend the capabilities of large language model agents, but also introduce an execution-time attack surface: a skill that appears...
Yiming Chen, Kemou Li, Haiwei Wu +1 more
Backdoor attacks in multimodal contrastive learning (MCL) have garnered growing attention in recent years, as many downstream tasks critically depend...
Francis Heylighen
AI systems based on Large Language Models (LLMs) have prompted fears that they may harbor hidden goals, seek to dominate or eliminate humanity, or...
Pingyu Wu, Lingyao Zhu, Weiming Zhang +1 more
Large language model safeguards decide whether to answer before seeing how an answer will be used. This creates a basic problem for dual-use tasks:...
Xiangyu Yin, Tora Bodin, Rohan Menon +1 more
Inference time defences against vision language model jailbreaks often subtract a calibrated direction from the residual stream at a chosen decoder...
Yongjian Guo, Wanlun Ma, Lingyu Shen +2 more
Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers...
Rui Yang, Michael Fu, Kla Tantithamthavorn +2 more
Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing...
Tong Zhang, Zexin Li, Simin Chen +1 more
Jailbreak defenses are essential for protecting large language models (LLMs), but they can also introduce secondary costs that weaken model utility....
Mohammed Aldeen, Muhammad Sami Irfan, Sagar Dasgupta +3 more
Autonomous vehicles (AVs) depend on Global Navigation Satellite Systems (GNSS) for localization and navigation, making them vulnerable to spoofing...
Zhetong Zhang, Honghao Fu, Miao Xu +2 more
As Video Large Language Models are increasingly deployed in real-world applications, ensuring their safety alignment has become critical....
Yuxi Li, Zhibo Zhang, Kailong Wang +3 more
Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall...
Chenyu Zhou, Yabin Peng, Wei Huang +3 more
Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches...
Behzad Ousat, Nikita Turkmen, Lalchandra Rampersaud +2 more
LLM-based browser agents are rapidly changing the threat landscape for web security. Unlike traditional automation frameworks that execute predefined...
Xingfu Zhou, Pengfei Wang, Yuan Zhou +2 more
Agentic retrieval-augmented generation (RAG) systems increasingly retrieve external evidence and orchestrate tools for knowledge-intensive...
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.
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