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 201–220 of 1,175 papers
Clear filtersZiyu Liu, Tao Li, Tianjie Ni +5 more
Backdoor vulnerabilities widely exist in the fine-tuning of large language models(LLMs). Most backdoor poisoning methods operate mainly at the token...
Pranshav Gajjar, Vijay K Shah
This position paper argues that to achieve Level 5 autonomous 6G networks, the next generation of Artificial Intelligence in Radio Access Networks...
Zi Liang, Ronghua Li, Yanyun Wang +2 more
Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of...
Elham Pourabbas Vafa, Sayak Saha Roy, Shirin Nilizadeh
We demonstrate how publicly available social-media data and generative AI (GenAI) can be misused to automate and scale highly personalized,...
Ali Karakoc, H. Birkan Yilmaz
SQL injection (SQLi) attacks are still one of the serious attacks ranked in the Open Worldwide Application Security Project (OWASP) Top 10 threats....
Youssef Zaazou, Mark Thomas
Vision-language models (VLMs), such as CLIP and SigLIP 2, are widely used for image classification, yet their vision encoders remain vulnerable to...
Mengqi He, Xinyu Tian, Xin Shen +6 more
Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability,...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2 more
This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs...
Desen Sun, Jason Hon, Howe Wang +3 more
With the rapid advancement of generative AI, users increasingly rely on image-generation models for image design and creation. To achieve faithful...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +1 more
This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from...
Peiru Yang, Haoran Zheng, Tong Ju +6 more
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal...
Farzad Nourmohammadzadeh Motlagh, Mehrdad Hajizadeh, Mehryar Majd +3 more
Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that...
Yue Li, Xiao Li, Hao Wu +5 more
Large Language Models (LLMs) are increasingly used for automated software development, making their ability to preserve secure coding practices...
Huilin Zhou, Jian Zhao, Yilu Zhong +7 more
Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing...
Ben Kereopa-Yorke, Guillermo Diaz, Holly Wright +3 more
We define Oracle Poisoning, an attack class in which an adversary corrupts a structured knowledge graph that AI agents query at runtime via tool-use...
Li Lixing
Modern large language models (LLMs) rely on system prompts to establish behavioral constraints and safety rules. Standard causal self-attention...
Yiyong Liu, Chia-Yi Hsu, Chun-Ying Huang +3 more
LLM-powered coding agents increasingly make software supply chain decisions. They generate imports, recommend packages, and write installation...
Isaac David, Arthur Gervais
Security updates create a short but important window in which defenders and attackers can compare vulnerable and patched software. Yet in many...
Zeyuan Chen, Yihan Ma, Xinyue Shen +2 more
Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data...
Huiyu Xu, Zhibo Wang, Wenhui Zhang +4 more
Modern LLM agents solve complex tasks by operating in iterative execution loops, where they repeatedly reason, act, and self-evaluate progress to...
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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