AI Threat Alert indexes 3,771+ 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 801–820 of 891 papers
Clear filtersXiangRui Zhang, Qiang Li, Haining Wang
Binary analysis increasingly relies on large language models (LLMs) to perform semantic reasoning over complex program behaviors. However, existing...
Xuanli He, Bilgehan Sel, Faizan Ali +3 more
Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and...
Firas Ben Hmida, Philemon Hailemariam, Kashif Ali Khan +1 more
Deep neural networks (DNNs) remain largely opaque at inference time, limiting our ability to detect and diagnose malicious input manipulations such...
Pavel Chizhov, Egor Bogomolov, Ivan P. Yamshchikov
Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves...
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3 more
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing...
Xixun Lin, Yang Liu, Yancheng Chen +9 more
The performance of large language model (LLM) agents depends critically on the execution harness, the system layer that orchestrates tool use,...
Xiaohua Wang, Muzhao Tian, Yuqi Zeng +20 more
Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and...
Sujan Ghimire, Parsa Mirfasihi, Muhtasim Alam Chowdhury +6 more
The globalization of integrated circuit (IC) design and manufacturing has increased the exposure of hardware intellectual property (IP) to untrusted...
Prajas Wadekar, Venkata Sai Pranav Bachina, Kunal Bhosikar +2 more
3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this...
Joel Fokou
Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise...
Shaopeng Fu, Di Wang
Adversarial training (AT) is an effective defense for large language models (LLMs) against jailbreak attacks, but performing AT on LLMs is costly. To...
Anasuya Chattopadhyay, Daniel Reti, Hans D. Schotten
Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However,...
Vladimir A. Mazin, Mikhail A. Zorin, Dmitrii S. Korzh +3 more
Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale...
Miit Daga, Swarna Priya Ramu
Organisations increasingly outsource privacy-sensitive data transformations to cloud providers, yet no practical mechanism lets the data owner verify...
Rui Yin, Tianxu Han, Naen Xu +8 more
Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain...
Pei-Yu Tseng, Lan Zhang, ZihDwo Yeh +3 more
Cyber Threat Intelligence (CTI) reports contain Indicators of Compromise (IOCs) that are critical for security operations. To operationalize these...
Shangkun Che, Silin Du, Ge Gao
The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes....
Hongru Song, Yu-An Liu, Ruqing Zhang +4 more
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack...
Anes Abdennebi, Nadjia Kara, Laaziz Lahlou
The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance,...
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 3,771+ 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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