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 181–200 of 1,050 papers
Clear filtersAli 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....
Nikita Kezins, Urbas Ekka, Pascal Berrang +1 more
Guardrail Classifiers defend production language models against harmful behavior, but although results seem promising in testing, they provide no...
Chiyu Zhang, Huiqin Yang, Bendong Jiang +8 more
The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content...
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,...
Tim Van hamme, Thomas Vissers, Javier Carnerero-Cano +4 more
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different...
Sultan Zavrak
The Model Context Protocol (MCP) has become a widely adopted interface for LLM agents to invoke external tools, yet learned monitoring of MCP...
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...
Hongwei Yao, Yiming Liu, Yiling He +1 more
Agentic language-model systems increasingly rely on mutable execution contexts, including files, memory, tools, skills, and auxiliary artifacts,...
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...
Monika Jotautaitė, Maria Angelica Martinez, Ollie Matthews +1 more
We introduce a red-teaming methodology that exposes harder-to-catch attacks for coding-agent monitors, suggesting that current practices may...
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...
Wenxin Tang, Xiang Zhang, Junliang Liu +11 more
Automated vulnerability detection is a fundamental task in software security, yet existing learning-based methods still struggle to capture the...
Shai Feldman, Yaniv Romano
Evaluating and predicting the performance of large language models (LLMs) in multi-turn conversational settings is critical yet computationally...
Mohammad Mamun, Mohamed Gaber, Scott Buffett +1 more
Language Model Agents (LMAs) are emerging as a powerful primitive for augmenting red-team operations. They can support attack planning, adversary...
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...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert