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 141–160 of 272 papers
Clear filtersCarlos Hinojosa, Clemens Grange, Bernard Ghanem
Vision-language models (VLMs) are increasingly deployed in real-world and embodied settings where safety decisions depend on visual context. However,...
Roberto Morabito, Mallik Tatipamula
The Internet has evolved by progressively expanding what humanity connects: first computers, then people, and later billions of devices through the...
Ce Zhang, Jinxi He, Junyi He +2 more
Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability...
Zhenheng Tang, Xiang Liu, Qian Wang +3 more
As Large Language Models (LLMs) become more powerful and autonomous, they increasingly face conflicts and dilemmas in many scenarios. We first...
Vanshaj Khattar, Md Rafi ur Rashid, Moumita Choudhury +4 more
Test-time training (TTT) has recently emerged as a promising method to improve the reasoning abilities of large language models (LLMs), in which the...
Yewon Han, Yumin Seol, EunGyung Kong +2 more
Existing jailbreak defence frameworks for Large Vision-Language Models often suffer from a safety utility tradeoff, where strengthening safety...
Max Hellrigel-Holderbaum, Edward James Young
As AI systems advance in capabilities, measuring their safety and alignment to human values is becoming paramount. A fast-growing field of AI...
Suvadeep Hajra, Palash Nandi, Tanmoy Chakraborty
Safety tuning through supervised fine-tuning and reinforcement learning from human feedback has substantially improved the robustness of large...
Pengcheng Li, Jie Zhang, Tianwei Zhang +5 more
Safety alignment in large language models is typically evaluated under isolated queries, yet real-world use is inherently multi-turn. Although...
Matthew Butler, Yi Fan, Christos Faloutsos
The proposed method (FraudFox) provides solutions to adversarial attacks in a resource constrained environment. We focus on questions like the...
Zonghao Ying, Xiao Yang, Siyang Wu +7 more
The rapid evolution of Large Language Models (LLMs) into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape....
Xinhao Deng, Yixiang Zhang, Jiaqing Wu +15 more
Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks....
Lu Niu, Cheng Xue
Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and...
Zhiyu Xue, Zimo Qi, Guangliang Liu +2 more
Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal...
Ali Eslami, Jiangbo Yu
This paper develops a control-theoretic framework for analyzing agentic systems embedded within feedback control loops, where an AI agent may adapt...
Harry Owiredu-Ashley
Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to capture...
Yi Chen, Yun Bian, Haiquan Wang +2 more
The application of large language models to code generation has evolved from one-shot generation to iterative refinement, yet the evolution of...
Bo Jiang
Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and...
Sumit Ranjan, Sugandha Sharma, Ubaid Abbas +1 more
Voice interfaces are quickly becoming a common way for people to interact with AI systems. This also brings new security risks, such as prompt...
Xisen Jin, Michael Duan, Qin Lin +4 more
As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces...
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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