AI Threat Alert indexes 3,397+ 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 721–740 of 1,146 papers
Clear filtersShaznin Sultana, Sadia Afreen, Nasir U. Eisty
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring...
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...
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...
Hongyan Chang, Ergute Bao, Xinjian Luo +1 more
Large language models (LLMs) increasingly rely on retrieving information from external corpora. This creates a new attack surface: indirect prompt...
Harshil Parmar, Pushti Vyas, Prayers Khristi +1 more
As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in...
Masahiro Kaneko
The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential...
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...
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...
Junda Lin, Zhaomeng Zhou, Zhi Zheng +4 more
LLM agents operating in open environments face escalating risks from indirect prompt injection, particularly within the tool stream where manipulated...
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...
Jingxiao Yang, Ping He, Tianyu Du +2 more
Recent advances in software vulnerability detection have been driven by Language Model (LM)-based approaches. However, these models remain vulnerable...
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...
Zhaoqi Wang, Zijian Zhang, Daqing He +5 more
Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to...
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...
Keerthi Kumar. M, Swarun Kumar Joginpelly, Sunil Khemka +2 more
Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various...
Qiang Yu, Xinran Cheng, Chuanyi Liu
As LLM agents transition from digital assistants to physical controllers in autonomous systems and robotics, they face an escalating threat from...
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 3,397+ 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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