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 381–400 of 1,455 papers
Clear filtersSujan 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,...
Willy Carlos Tchuitcheu, Tan Lu, Ann Dooms
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We...
Adam Stein, Davis Brown, Hamed Hassani +2 more
To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare,...
Ricardo Bessa, Rui Claro, João Trindade +1 more
Large Language Models (LLMs) are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human...
Junxiao Yang, Haoran Liu, Jinzhe Tu +9 more
Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried...
Hanbo Huang, Xuan Gong, Yiran Zhang +2 more
Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to...
Ricardo Bessa, Rui Claro, João Trindade +1 more
The application of Machine Learning techniques in code generation is now a common practice for most developers. Tools such as ChatGPT from OpenAI...
Yiran Ling, Wenxuan Li, Siying Dong +5 more
Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show...
Shuhao Zhang, Yuli Chen, Jiale Han +2 more
Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text....
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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AI Threat Alert