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 2621–2640 of 3,023 papers
Sidhant Narula, Javad Rafiei Asl, Mohammad Ghasemigol +2 more
Large Language Models (LLMs) remain vulnerable to multi-turn jailbreak attacks. We introduce HarmNet, a modular framework comprising ThoughtNet, a...
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...
Zijie Xu, Minfeng Qi, Shiqing Wu +4 more
Multi-agent systems powered by large language models are advancing rapidly, yet the tension between mutual trust and security remains underexplored....
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....
Osama Al Haddad, Muhammad Ikram, Ejaz Ahmed +1 more
Security analysts face increasing pressure to triage large and complex vulnerability backlogs. Large Language Models (LLMs) offer a potential aid by...
Oleksandr Adamov, Anders Carlsson
This paper explores the challenges of cyberattack attribution, specifically APTs, applying the case study approach for the WhisperGate cyber...
Yasser Hamidullah, Koel Dutta Chowdhury, Yusser Al Ghussin +4 more
Hallucination, where models generate fluent text unsupported by visual evidence, remains a major flaw in vision-language models and is particularly...
Yixuan Liu, Xinlei Li, Yi Li
Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token...
Gyeonghoon Park, Jaehan Kim, Jinu Choi +1 more
Host-based cryptomining malware, commonly known as cryptojackers, have gained notoriety for their stealth and the significant financial losses they...
Zheng Zhang, Jiarui He, Yuchen Cai +4 more
As large language model (LLM) agents increasingly automate complex web tasks, they boost productivity while simultaneously introducing new security...
Lei Li, Xiao Zhou, Yingying Zhang +1 more
Medical question answering (QA) requires extensive access to domain-specific knowledge. A promising direction is to enhance large language models...
Jiahao Shi, Tianyi Zhang
Despite recent advances, Large Language Models (LLMs) still generate vulnerable code. Retrieval-Augmented Generation (RAG) has the potential to...
Pranshav Gajjar, Molham Khoja, Abiodun Ganiyu +4 more
The impending adoption of Open Radio Access Network (O-RAN) is fueling innovation in the RAN towards data-driven operation. Unlike traditional RAN...
Chengquan Guo, Yuzhou Nie, Chulin Xie +3 more
As large language models (LLMs) are increasingly used for code generation, concerns over the security risks have grown substantially. Early research...
Isaac Wu, Michael Maslowski
As large language models (LLMs) become integrated into various sensitive applications, prompt injection, the use of prompting to induce harmful...
Roberto Brusnicki, David Pop, Yuan Gao +2 more
Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution scenarios with semantic anomalies. While Vision...
Neeladri Bhuiya, Madhav Aggarwal, Diptanshu Purwar
Large Language Models (LLMs) are improving at an exceptional rate. With the advent of agentic workflows, multi-turn dialogue has become the de facto...
Qilin Liao, Anamika Lochab, Ruqi Zhang
Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplored...
Xu Zhang, Hao Li, Zhichao Lu
Multimodal Large Language Models (MLLMs) achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks....
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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