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 41–60 of 3,023 papers
Khanak Khandelwal
Scaling adversarial evaluation of large language models requires both a method for generating hard inputs and a reliable way to confirm that...
Anastasiia Kucherenko, François Brouchoud, Dimitri Percia David +1 more
While the validity of LLMs' use in the legal context remains subject to ethical and legal debate, legal professionals are already experimenting with...
Ling Li, Bowen Liu, Zinuo Zhan +5 more
Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual...
Arda Eren, Micheal Cheung, Youqian Zhang +2 more
Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other...
Dario Pasquini, Michal Bazyli, Taras Fedynyshyn +1 more
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However,...
Shiding Zhu, Yudi Qi, Yajie Wang +6 more
Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing...
Juho Park, Hyunmin Choi, Kevin Nam
AI security agents increasingly rely on Retrieval-Augmented Generation (RAG) to use external security knowledge for vulnerability analysis and...
Hojun Choi, Seulbin Hwang, Dae Jung Kim +3 more
Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving. Despite recent progress,...
Yedidel Louck
LLM agents increasingly rely on persistent long-term memory, which creates a critical vulnerability that we study here: memory poisoning. An...
Hyunji Nam, Keertana Chidambaram, Dorottya Demszky +1 more
While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode...
Onkar Shelar, Travis Desell
Large Language Models remain vulnerable to adversarial prompts that elicit harmful responses, and scaling red-teaming to cover a broad range of...
Wonjun Kang, Kevin Galim, Seunghyuk Oh +9 more
On-policy distillation (OPD) trains a student on its own rollouts guided by teacher feedback and is becoming increasingly important for large...
Jingke Wang, Zhenru Zhao, Shuangming Lei +8 more
Vision-Language-Action driving models convert a pretrained Vision-Language Model into a driving policy, allowing them to use world knowledge and...
Eric Xing, Mingkai Deng, Jinyu Hou
What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'',...
Yarin Yerushalmi Levi, Roy Betser, Amit Giloni +5 more
Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors...
Yifei Zhao, Qian Lou, Mengxin Zheng
Vision-language models (VLMs) are increasingly used as perception-reasoning backbones for embodied intelligence in safety-critical physical systems,...
Annika Marie Schoene, Cansu Canca, Gautham Vijay Kumar +1 more
General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate...
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1 more
A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to...
Matan Ben-Tov, Mahmood Sharif
Discrete text-trigger optimization -- searching for text sequences that, when ingested by a model, steer it toward a specified objective -- underpins...
JiongHan Wang, WenChao Huang
As a prevalent analytical technique for stateful protocol implementations, state machine learning suffers from a core bottleneck stemming from...
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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