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 441–460 of 521 papers
Clear filtersAndré V. Duarte, Xuying li, Bin Zeng +3 more
If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling...
Simon Yu, Peilin Yu, Hongbo Zheng +3 more
We present VISAT, a novel open dataset and benchmarking suite for evaluating model robustness in the task of traffic sign recognition with the...
Zheng Zhang, Guanlong Wu, Sen Deng +2 more
In the rapidly expanding landscape of Large Language Model (LLM) applications, real-time output streaming has become the dominant interaction...
Juan Ren, Mark Dras, Usman Naseem
Agentic methods have emerged as a powerful and autonomous paradigm that enhances reasoning, collaboration, and adaptive control, enabling systems to...
Yifan Wu, Xuewei Feng, Yuxiang Yang +1 more
As the core of the Internet infrastructure, the TCP/IP protocol stack undertakes the task of network data transmission. However, due to the...
María Sanz-Gómez, Víctor Mayoral-Vilches, Francesco Balassone +3 more
Cybersecurity spans multiple interconnected domains, complicating the development of meaningful, labor-relevant benchmarks. Existing benchmarks...
Vladyslav Larin, Ihor Naumenko, Aleksei Ivashov +2 more
As centralized AI hits compute ceilings and diminishing returns from ever-larger training runs, meeting demand requires an inference layer that...
Hiromu Takahashi, Shotaro Ishihara
We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against...
Armin Gerami, Kazem Faghih, Ramani Duraiswami
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by connecting them to external knowledge, improving accuracy and reducing...
Julia Bazinska, Max Mathys, Francesco Casucci +4 more
AI agents powered by large language models (LLMs) are being deployed at scale, yet we lack a systematic understanding of how the choice of backbone...
Hao Zheng, Zirui Pang, Ling li +5 more
Advances in Multimodal Large Language Models (MLLMs) intensify concerns about data privacy, making Machine Unlearning (MU), the selective removal of...
Mojtaba Eshghie, Gabriele Morello, Matteo Lauretano +2 more
Smart contract vulnerabilities cost billions of dollars annually, yet existing automated analysis tools fail to generate deployable defenses. We...
Christoph Bühler, Matteo Biagiola, Luca Di Grazia +1 more
Large Language Models (LLMs) have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model...
Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa +2 more
Frontier Large Language Models (LLMs) pose unprecedented dual-use risks through the potential proliferation of chemical, biological, radiological,...
Chengcan Wu, Zhixin Zhang, Mingqian Xu +2 more
Large Language Model (LLM)-based Multi-Agent Systems (MAS) have become a popular paradigm of AI applications. However, trustworthiness issues in MAS...
Alexander Nemecek, Zebin Yun, Zahra Rahmani +4 more
As large language models (LLMs) become progressively more embedded in clinical decision-support, documentation, and patient-information systems,...
Marco Alecci, Jordan Samhi, Tegawendé F. Bissyandé +1 more
Mobile apps often embed authentication secrets, such as API keys, tokens, and client IDs, to integrate with cloud services. However, developers often...
Giovanni De Muri, Mark Vero, Robin Staab +1 more
LLMs are often used by downstream users as teacher models for knowledge distillation, compressing their capabilities into memory-efficient models....
Yixuan Liu, Xinlei Li, Yi Li
Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token...
Shivam Ratnakar, Sanjay Raghavendra
Integration of Large Language Models with search/retrieval engines has become ubiquitous, yet these systems harbor a critical vulnerability that...
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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