AI Threat Alert indexes 3,055+ 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 2301–2320 of 3,055 papers
Momoko Shiraishi, Yinzhi Cao, Takahiro Shinagawa
Command-line interface (CLI) fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of...
Jakub Hoscilowicz, Artur Janicki
We introduce the Adversarial Confusion Attack, a new class of threats against multimodal large language models (MLLMs). Unlike jailbreaks or targeted...
Sidahmed Benabderrahmane, James Cheney, Talal Rahwan
Advanced Persistent Threats (APTs) pose a significant challenge in cybersecurity due to their stealthy and long-term nature. Modern supervised...
Xuebo Qiu, Mingqi Lv, Yimei Zhang +4 more
Provenance-based threat hunting identifies Advanced Persistent Threats (APTs) on endpoints by correlating attack patterns described in Cyber Threat...
Sen Nie, Jie Zhang, Jianxin Yan +2 more
Adversarial attacks have evolved from simply disrupting predictions on conventional task-specific models to the more complex goal of manipulating...
David Amebley, Sayanton Dibbo
In the age of agentic AI, the growing deployment of multi-modal models (MMs) has introduced new attack vectors that can leak sensitive training data...
Abhijeet Pathak, Suvadra Barua, Dinesh Gudimetla +4 more
Large language models (LLMs) and autonomous coding agents are increasingly used to generate software across a wide range of domains. Yet a core...
Steven Peh
Large Language Models (LLMs) remain vulnerable to prompt injection attacks, representing the most significant security threat in production...
Angelo Gaspar Diniz Nogueira, Kayua Oleques Paim, Hendrio Bragança +2 more
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the...
Axel Constant, Mahault Albarracin, Karl J. Friston
This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the...
Yingjia Shang, Yi Liu, Huimin Wang +4 more
With the rapid advancement of retrieval-augmented vision-language models, multimodal medical retrieval-augmented generation (MMed-RAG) systems are...
Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Amin Ahsan Ali +3 more
Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and...
Xurui Li, Kaisong Song, Rui Zhu +2 more
Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks. Existing...
Yu Cui, Yifei Liu, Hang Fu +4 more
Research on the safety evaluation of large language models (LLMs) has become extensive, driven by jailbreak studies that elicit unsafe responses....
Rong Feng, Suman Saha
Obfuscation poses a persistent challenge for software engineering tasks such as program comprehension, maintenance, testing, and vulnerability...
Andrew Maranhão Ventura D'addario
The integration of Large Language Models (LLMs) into healthcare demands a safety paradigm rooted in \textit{primum non nocere}. However, current...
Junbo Zhang, Ran Chen, Qianli Zhou +2 more
Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety...
Muhammad Usman Shahid, Chuadhry Mujeeb Ahmed, Rajiv Ranjan
The security of code generated by large language models (LLMs) is a significant concern, as studies indicate that such code often contains...
Yixin Wu, Rui Wen, Chi Cui +2 more
Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack...
Ryan Wong, Hosea David Yu Fei Ng, Dhananjai Sharma +2 more
Large Language Models (LLMs) remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work...
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 3,055+ 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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