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 2641–2660 of 3,023 papers
Vincenzo Carletti, Pasquale Foggia, Carlo Mazzocca +2 more
Federated Learning (FL) enables collaborative training of Machine Learning (ML) models across multiple clients while preserving their privacy. Rather...
Yushi Yang, Shreyansh Padarha, Andrew Lee +1 more
Agentic reinforcement learning (RL) trains large language models to autonomously call tools during reasoning, with search as the most common...
Xinkai Wang, Beibei Li, Zerui Shao +3 more
Multimodal large language models (MLLMs) have become integral to a wide range of real-world applications by jointly reasoning over text and visual...
Rishi Jha, Harold Triedman, Justin Wagle +1 more
Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system...
Giulia Giusti
The concept of linearity plays a central role in both mathematics and computer science, with distinct yet complementary meanings. In mathematics,...
Runlin Lei, Lu Yi, Mingguo He +4 more
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a...
Tenghui Huang, Jinbo Wen, Jiawen Kang +8 more
Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to...
Elias Hossain, Swayamjit Saha, Somshubhra Roy +1 more
Even when prompts and parameters are secured, transformer language models remain vulnerable because their key-value (KV) cache during inference...
Qiusi Zhan, Angeline Budiman-Chan, Abdelrahman Zayed +3 more
Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain...
Qiusi Zhan, Angeline Budiman-Chan, Abdelrahman Zayed +3 more
Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain...
Masahiro Kaneko, Zeerak Talat, Timothy Baldwin
Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the...
Masahiro Kaneko, Timothy Baldwin
Adversarial attacks by malicious users that threaten the safety of large language models (LLMs) can be viewed as attempts to infer a target property...
Mansi Phute, Matthew Hull, Haoran Wang +6 more
Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversarial...
Bo-Han Feng, Chien-Feng Liu, Yu-Hsuan Li Liang +9 more
Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While...
Navreet Kaur, Hoda Ayad, Hayoung Jung +3 more
Language model users often embed personal and social context in their questions. The asker's role -- implicit in how the question is framed --...
Yue Liu, Zhenchang Xing, Shidong Pan +1 more
In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex...
Amirkia Rafiei Oskooei, Mehmet S. Aktas
The proficiency of Large Language Models (LLMs) in processing structured data and adhering to syntactic rules is a capability that drives their...
Jie Zhang, Meng Ding, Yang Liu +2 more
We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural...
Asmita Mohanty, Gezheng Kang, Lei Gao +1 more
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based,...
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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