AI Threat Alert indexes 3,406+ 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 861–880 of 1,756 papers
Clear filtersZiyu Liu, Tao Li, Tianjie Ni +5 more
Backdoor vulnerabilities widely exist in the fine-tuning of large language models(LLMs). Most backdoor poisoning methods operate mainly at the token...
Mengying Zhang, Derui Wang, Ruoxi Sun +3 more
Advanced model dememorization methods, including availability poisoning (unlearnability) and machine unlearning, are emerging as key safeguards...
Fanpu Cao, Xin Zou, Xuming Hu +1 more
Multimodal large language models (MLLMs) have become a key interface for visual reasoning and grounded question answering, yet they remain vulnerable...
Pranshav Gajjar, Vijay K Shah
This position paper argues that to achieve Level 5 autonomous 6G networks, the next generation of Artificial Intelligence in Radio Access Networks...
Fanxiao Li, Jiaying Wu, Tingchao Fu +3 more
Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner--executor architectures, where planners convert prompts...
Khondaker Tasnia Hoque, Toukir Ahammed
Flaky tests, which exhibit non-deterministic pass/fail behavior for the same version of code, pose significant challenges to reliable regression...
Zi Liang, Ronghua Li, Yanyun Wang +2 more
Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of...
Zhaorui Li, Chengyu Song
Recent frontier large language models (LLMs) have shown strong performance in identifying security vulnerabilities in large, mature open-source...
Elham Pourabbas Vafa, Sayak Saha Roy, Shirin Nilizadeh
We demonstrate how publicly available social-media data and generative AI (GenAI) can be misused to automate and scale highly personalized,...
John T. Halloran
Large language model (LLM) alignment algorithms typically consist of post-training over preference pairs. While such algorithms are widely used to...
Ali Karakoc, H. Birkan Yilmaz
SQL injection (SQLi) attacks are still one of the serious attacks ranked in the Open Worldwide Application Security Project (OWASP) Top 10 threats....
Stefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu
The irreversible nature of blockchain transactions makes the identification of smart contract vulnerabilities an essential requirement for secure...
Christian Moya, Alex Semendinger, Guang Lin +1 more
Preference learning methods such as Direct Preference Optimization (DPO) are known to induce reliance on spurious correlations, leading to sycophancy...
Fatima Z. Abacha, Sin G. Teo, Yuanxiang Wu +2 more
Federated Learning remains highly susceptible to backdoor attacks--malicious clients inject targeted behaviours into the global model. Existing...
Youssef Zaazou, Mark Thomas
Vision-language models (VLMs), such as CLIP and SigLIP 2, are widely used for image classification, yet their vision encoders remain vulnerable to...
Nikita Kezins, Urbas Ekka, Pascal Berrang +1 more
Guardrail Classifiers defend production language models against harmful behavior, but although results seem promising in testing, they provide no...
Joel Rorseth, Parke Godfrey, Lukasz Golab +2 more
This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models...
Pedro Conde, Henrique Branquinho, Valerio Mazzone +3 more
AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will...
Saba Pourhanifeh, AbdulAziz AbdulGhaffar, Ashraf Matrawy
Large Language Models(LLMs) are increasingly explored for cybersecurity applications such as vulnerability detection. In the domain of threat...
Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri
The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor...
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 3,406+ 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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