AI Threat Alert indexes 3,082+ 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 2041–2060 of 3,082 papers
Karolina Korgul, Yushi Yang, Arkadiusz Drohomirecki +7 more
Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their reliance...
Kerem Zaman, Shashank Srivastava
Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We argue...
Soham Padia, Dhananjay Vaidya, Ramchandra Mangrulkar
Securing blockchain-enabled IoT networks against sophisticated adversarial attacks remains a critical challenge. This paper presents a trust-based...
Woorim Han, Yeongjun Kwak, Miseon Yu +4 more
Learning-based automated vulnerability repair (AVR) techniques that utilize fine-tuned language models have shown promise in generating vulnerability...
Zongmin Zhang, Zhen Sun, Yifan Liao +5 more
Prompt-driven Video Segmentation Foundation Models (VSFMs) such as SAM2 are increasingly deployed in applications like autonomous driving and digital...
Vahideh Zolfaghari
Background Large language models (LLMs) are increasingly deployed in medical consultations, yet their safety under realistic user pressures remains...
Jiayu Hu, Beibei Li, Jiangwei Xia +3 more
While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they...
Marc S. Montalvo, Hamed Yaghoobian
Recent advances in large language models (LLMs) are transforming data-intensive domains, with finance representing a high-stakes environment where...
Chinmay Pushkar, Sanchit Kabra, Dhruv Kumar +1 more
Large Language Models (LLMs) have demonstrated significant potential in automated software security, particularly in vulnerability detection....
Mengqi He, Xinyu Tian, Xin Shen +4 more
Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, a measure of model uncertainty,...
Tsogt-Ochir Enkhbayar
Warning-framed content in training data (e.g., "DO NOT USE - this code is vulnerable") does not, it turns out, teach language models to avoid the...
Eranga Bandara, Tharaka Hewa, Ross Gore +12 more
Agentic AI represents a major shift in how autonomous systems reason, plan, and execute multi-step tasks through the coordination of Large Language...
Tian Li, Bo Lin, Shangwen Wang +1 more
Retrieval-Augmented Code Generation (RACG) is increasingly adopted to enhance Large Language Models for software development, yet its security...
Duo Chai, Zizhen Liu, Shuhuai Wang +4 more
Large language models (LLMs) are highly compute- and memory-intensive, posing significant demands on high-performance GPUs. At the same time,...
Long Zhang, Wei-neng Chen
The increasing integration of Large Language Models (LLMs) into decision-making frameworks has exposed significant vulnerabilities to social...
Tianwei Lan, Farid Naït-Abdesselam
The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning (ML) techniques for...
Haoyang Li, Mingjin Li, Jinxin Zuo +5 more
LLM-based code agents(e.g., ChatGPT Codex) are increasingly deployed as detector for code review and security auditing tasks. Although CoT-enhanced...
Xinjie Xu, Shuyu Cheng, Dongwei Xu +2 more
In hard-label black-box adversarial attacks, where only the top-1 predicted label is accessible, the prohibitive query complexity poses a major...
Mohammed Latif Siddiq, Natalie Sekerak, Antonio Karam +3 more
Large language models (LLMs) are increasingly used in software development, but their level of software security expertise remains unclear. This work...
Yifan Huang, Xiaojun Jia, Wenbo Guo +4 more
Large language models (LLMs) have revolutionized software development through AI-assisted coding tools, enabling developers with limited programming...
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 3,082+ 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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