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 701–720 of 759 papers
Clear filtersAbdelaziz Bounhar, Rania Hossam Elmohamady Elbadry, Hadi Abdine +3 more
Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine-tuning for alignment and...
Mohammed Himayath Ali, Mohammed Aqib Abdullah, Mohammed Mudassir Uddin +1 more
Large Language Models have emerged as transformative tools for Security Operations Centers, enabling automated log analysis, phishing triage, and...
Ruiqi Li, Zhiqiang Wang, Yunhao Yao +1 more
To standardize interactions between LLM-based agents and their environments, the Model Context Protocol (MCP) was proposed and has since been widely...
Xinyi Wu, Geng Hong, Yueyue Chen +5 more
Web agents, powered by large language models (LLMs), are increasingly deployed to automate complex web interactions. The rise of open-source...
Shawn Li, Chenxiao Yu, Zhiyu Ni +4 more
Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified...
Muhammad Wahid Akram, Keshav Sood, Muneeb Ul Hassan +1 more
Phishing with Quick Response (QR) codes is termed as Quishing. The attackers exploit this method to manipulate individuals into revealing their...
Quan Minh Nguyen, Min-Seon Kim, Hoang M. Ngo +3 more
Membership inference attack (MIA) poses a significant privacy threat in federated learning (FL) as it allows adversaries to determine whether a...
Hongjun An, Yiliang Song, Jiangan Chen +3 more
Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and...
Chao Liu, Ngai-Man Cheung
3D Vision-Language Models (VLMs), such as PointLLM and GPT4Point, have shown strong reasoning and generalization abilities in 3D understanding tasks....
Víctor Mayoral-Vilches, María Sanz-Gómez, Francesco Balassone +6 more
AI-driven penetration testing now executes thousands of actions per hour but still lacks the strategic intuition humans apply in competitive...
Ahmad Alobaid, Martí Jordà Roca, Carlos Castillo +1 more
The availability of Large Language Models (LLMs) has led to a new generation of powerful chatbots that can be developed at relatively low cost. As...
Zenghao Duan, Zhiyi Yin, Zhichao Shi +8 more
Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content, restricting their safe deployment....
Balachandra Devarangadi Sunil, Isheeta Sinha, Piyush Maheshwari +3 more
Large language model agents equipped with persistent memory are vulnerable to memory poisoning attacks, where adversaries inject malicious...
Songze Li, Ruishi He, Xiaojun Jia +2 more
Large Language Models (LLMs) face a significant threat from multi-turn jailbreak attacks, where adversaries progressively steer conversations to...
Badhan Chandra Das, Md Tasnim Jawad, Joaquin Molto +2 more
In recent years, the security vulnerabilities of Multi-modal Large Language Models (MLLMs) have become a serious concern in the Generative Artificial...
Mizuki Sakai, Mizuki Yokoyama, Wakaba Tateishi +1 more
Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that...
Zhiyuan Chang, Mingyang Li, Yuekai Huang +6 more
Large language model (LLM)-integrated applications have become increasingly prevalent, yet face critical security vulnerabilities from prompt...
Hoagy Cunningham, Jerry Wei, Zihan Wang +26 more
We introduce enhanced Constitutional Classifiers that deliver production-grade jailbreak robustness with dramatically reduced computational costs and...
Mohamed Nabeel, Oleksii Starov
According to Gartner, more than 70% of organizations will have integrated AI models into their workflows by the end of 2025. In order to reduce cost...
San Kim, Gary Geunbae Lee
Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning, which enables broad...
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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