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 421–440 of 2,152 papers
Clear filtersSuchit Gupte, Xueru Zhang, Mohammad Mahdi Khalili
Sparse autoencoders (SAEs) are widely used to interpret the internal representations of large language models (LLMs), yet their reliability under...
Ping Wang, Xiangguo Sun, Bingbing Xu +2 more
Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a...
Xiaodong Wu, Zhimin Zhao, Qi Li +4 more
A coding agent edits files and executes shell commands with its developer's privileges, allowing malicious requests to translate directly into...
Xiaodong Wu, Yu Shi, Qi Li +5 more
Self-evolving LLM coding agents write their own tools by imitating retrieved skills from shared skill libraries. We identify a vulnerability in this...
Roberto Riaño, Gorka Abad, Stjepan Picek +1 more
Spiking Neural Networks (SNNs) are increasingly distributed as pretrained checkpoints and reused as backbones for new tasks. However, current SNN...
Zhiyuan Xu, Muhammad Firhard Roslan, Joseph Gardiner +2 more
Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated...
Longzhu He, Zelang Wen, Chaozhuo Li +1 more
Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving...
Yanbo Dai, Zhenlan Ji, Zongjie Li +1 more
Tool-using LLM agents extend security risks beyond generated text to actions that affect external systems. Under multi-turn decomposition attacks, a...
XiuYu Zhang, Bonan Ruan, Junfeng Fang +3 more
Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them. Interpretability methods...
Jinpu Jiang, Xuan Wu, Wenhao Song +6 more
Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for Question Answering (QA) by integrating external information into...
Pei-Sze Tan, Tasuku Igarashi, Isao Echizen
Agentic AI assistants are increasingly used in everyday life. However, they may also be misused to support harmful manipulation in interpersonal...
Dev Mehta, Lily Dukette, William Folan +4 more
The move of LLM inference to edge AI accelerators introduces new physical vulnerabilities. During execution, model parameters and intermediate...
Ze Sheng, Aleksandar Kezic, Zhicheng Chen +1 more
Evaluating the ability of large language models (LLMs) to discover software bugs is increasingly important. Existing benchmarks typically evaluate...
Zhonghao Zhan, Hamed Haddadi
Self-evolving Skill harnesses (AutoSkills, Hermes Agent) generate more advisory orchestration automatically; their reported gains are efficiency, not...
Zhijie Zheng, Yu Li, Chen Qian +5 more
LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file...
Yichao Gao, Yumo Zhang, Yunhao Yao +4 more
LLM agents integrated with external resources gain complex task capabilities, yet the unified natural-language context channel makes them vulnerable...
Lin-Fa Lee, YI-YU Chang, Kuo-Hui Yeh
The emerging W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating agent...
Yueyang Quan, Anjun Gao, Yufei Xia +2 more
Retrieval-augmented generation (RAG) improves the factuality of large language models by grounding responses in external documents, but it also...
Anjun Gao, Yueyang Quan, Yufei Xia +2 more
Safety alignment in large language models (LLMs) remains brittle against a growing spectrum of attacks. Jailbreak attacks bypass safety mechanisms...
Avital Aviv, Parth A. Gandh, Ron Bitton +1 more
The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments...
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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