AI Threat Alert indexes 4,319+ 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 1781–1800 of 2,152 papers
Clear filtersYongyu Wang
Graph Neural Networks (GNNs) have emerged as a dominant paradigm for learning on graph-structured data, thanks to their ability to jointly exploit...
Yining Yuan, Yifei Wang, Yichang Xu +3 more
This paper presents LLMBugScanner, a large language model (LLM) based framework for smart contract vulnerability detection using fine-tuning and...
Kai Williams, Rohan Subramani, Francis Rhys Ward
Frontier AI developers may fail to align or control highly-capable AI agents. In many cases, it could be useful to have emergency shutdown mechanisms...
Henry Onyeka, Emmanuel Samson, Liang Hong +3 more
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated...
Aayush Garg, Zanis Ali Khan, Renzo Degiovanni +1 more
Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising...
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5 more
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a...
Tong Wu, Weibin Wu, Zibin Zheng
Equipped with various tools and knowledge, GPTs, one kind of customized AI agents based on OpenAI's large language models, have illustrated great...
Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov +2 more
Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the...
Anudeex Shetty
Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language understanding and generation. Based on these LLMs,...
Zeng Wang, Minghao Shao, Akashdeep Saha +4 more
Graph neural networks (GNNs) have shown promise in hardware security by learning structural motifs from netlist graphs. However, this reliance on...
Abeer Matar A. Almalky, Ziyan Wang, Mohaiminul Al Nahian +2 more
In recent years, large language models (LLMs) have achieved substantial advancements and are increasingly integrated into critical applications...
Mohaiminul Al Nahian, Abeer Matar A. Almalky, Gamana Aragonda +6 more
Adversarial weight perturbation has emerged as a concerning threat to LLMs that either use training privileges or system-level access to inject...
Shaona Ghosh, Barnaby Simkin, Kyriacos Shiarlis +9 more
This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and...
Gauri Pradhan, Joonas Jälkö, Santiago Zanella-Bèguelin +1 more
Training machine learning models with differential privacy (DP) limits an adversary's ability to infer sensitive information about the training data....
Rebeka Toth, Tamas Bisztray, Richard Dubniczky
Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models (LLMs) to craft highly...
Rebeka Toth, Tamas Bisztray, Nils Gruschka
In this paper, we introduce a metadata-enriched generation framework (PhishFuzzer) that seeds real emails into Large Language Models (LLMs) to...
Di Zhu, Chen Xie, Ziwei Wang +1 more
New York City reports over one hundred thousand motor vehicle collisions each year, creating substantial injury and public health burden. We present...
Herman Errico, Jiquan Ngiam, Shanita Sojan
The Model Context Protocol (MCP) replaces static, developer-controlled API integrations with more dynamic, user-driven agent systems, which also...
Jaehwan Park, Kyungchan Lim, Seonhye Park +1 more
The advent of Artificial Intelligence (AI), particularly large language models (LLMs), has revolutionized software development by enabling developers...
Wei He, Kai Han, Hang Zhou +4 more
The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic...
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 4,319+ 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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