AI Threat Alert indexes 3,397+ 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 441–460 of 1,749 papers
Clear filtersZewen Liu
Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence. Recent work shows that agent memories degrade...
Jaehyuk Jang, Minseok Seo. Seungju Cho, Kangwook Ko +1 more
Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time...
Junquan Deng, Zhiyu Fan, Ruijie Meng
Recent advances in large language models (LLMs) have enabled vibe coding, an emerging software development paradigm in which users create...
Junquan Deng, Zhiyu Fan, Ruijie Meng
Recent advances in large language models (LLMs) have enabled vibe coding, an emerging software development paradigm in which users create...
Ruixiao Lin, Xinhao Deng, Qingming Li +12 more
Self-evolving LLM agent systems, which autonomously update their model parameters, memory, tools, and architectures, introduce a qualitatively new...
Linghan Chen, Kaiyan Ji, Minyu Guo
Many recent vision-language-action (VLA) policies adopt an imagine-then-act design. A world-action model (WAM) first imagines a short future as a...
Aymen Bouferroum, Ildi Alla, Valeria Loscri +2 more
Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things (IIoT) networks. Existing detection and...
Yanhang Li, Zhichao Fan, Zexin Zhuang
Hidden-state probing -- a linear classifier on a frozen vision-language model's internal activations -- has emerged as an attractive evaluation tool...
Parth Bramhecha, Smit Deshmukh, Sairaj Bodhale +2 more
As Large Language Models (LLMs) achieve widespread integration across diverse linguistic landscapes, ensuring their safety and alignment with...
Mingyuan Fan, Cen Chen
The proliferation of IoT devices has fueled distributed edge systems to collect vast amounts of sensitive data, creating fertile ground for on-device...
Xingwei Zhong, Varun Sharma, Kar Wai Fok +1 more
Vision language models (VLMs) employ both visual and textual modalities to enable advanced vision-language inference. However, incorporating visual...
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +2 more
Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising paradigm for privacy-sensitive...
Shivam Ratnakar, Kartikeya Vats
Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque. In this work, we...
Weidi Luo, Qiming Zhang, Yihao Quan +5 more
Coding agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and...
Md Anas Biswas
Prompt-injection detectors are deployed as guards: a model scores an input and a downstream system trusts or blocks it on that score. I study the...
Varun Gadey, Zijie Liu, Alexandra Dmitrienko
Automated vulnerability repair has emerged as a promising direction to mitigate the growing number of software vulnerabilities. Recent advances in...
Yuhang Jiang, Xiaojing Chen
Input Diversity (DI), which applies random resizing and padding at each attack iteration, is a near-default ingredient of transfer-based adversarial...
Sihui Dai, Mann Patel
Prior work has shown that in-context demonstrations can jailbreak language models, but it remains unclear how models interpret different types of...
Reza Soosahabi, Vivek Namsani
Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with...
Hanwool Lee, Dasol Choi, Bokyeong Kim +2 more
Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under...
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 3,397+ 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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