AI Threat Alert indexes 3,771+ 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 221–240 of 3,771 papers
Zhipeng Xia, Haotian Xu, Siyu Yun +4 more
LLM training is increasingly vulnerable to silent data corruption (SDC), yet existing protection methods largely treat Transformer computations...
Jiaxuan Li, Jiahao Zhang, Duc Minh Vo +3 more
Vision-language models (VLMs) can comply with harmful requests delivered through images, even when their LLM backbones would refuse the same content...
Yuna Park, Hwang Youn Kim, Yujin Kim +3 more
Existing jailbreak evaluations typically characterize robustness using a single attack success rate (ASR) measured in a default configuration (the...
Tanise Ceron, Joachim Baumann, Elisa Bassignana +3 more
Language models are increasingly mediating information access to end users, urging a systematic evaluation of their responses for a fair and reliable...
Md Mokarram Chowdhury, Ernie Chang, Yang Li
Large language models are trained to follow instructions while refusing harmful requests. Jailbreaks exploit this balance to elicit content a model...
Shiqian Zhao, Yangfan Zhou, Xinfeng Li +5 more
Recently, large language model (LLM) agents, such as Codex, Claude Code, and OpenClaw, have become capable of planning and executing long-horizon...
Doyun Kim, Chanwoo Kim, Sugyeong Eo +2 more
LLM-based agent systems increasingly adopt skill-based architectures to reduce repetitive reasoning costs and improve stable, efficient task...
Yangmin Huang, Shu Quan, He Geng +5 more
Medical knowledge changes continually, making large language models vulnerable to relying on outdated yet clinically plausible information. We study...
Yizhe Zeng, Chenxu Niu, Wei Zhang +7 more
Backdoor attacks pose a serious threat to large language models (LLMs), but existing defenses remain fragmented, failing to pro?vide unified defense...
Lifei Liu, Haoran Yu
Multi-agent applications delegate work across independently operated deployers. After an incident, a verifier must answer two questions: which...
Yunseok Lee, Yunji Kim, Woojin Lee
As LLM agents take real-world actions through tools, indirect prompt injection (IPI) has emerged as a serious threat. The standard metric, Attack...
Chuanchao Zang, Jianing Wang, Wenyu Chen +6 more
LLM agents commonly use knowledge-based tools and access their underlying files, databases, and search indexes through tool invocation. This...
Seojin Lee, Hwanhee Lee
Korean introduces an additional typographical perturbation level not captured by ordinary character-level edit models: because syllable blocks are...
Chen Xiong, Zhiyuan He, Pin-Yu Chen +2 more
Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and...
Chuanchao Zang, Zijian Cao, Xiangtao Meng +6 more
Long-term memory is becoming a core capability of LLM agents, enabling personalization and long-horizon interaction. However, memory mechanisms that...
Dishu Yang, Jingjing Liu, Jize Li
Preference optimization is widely used to align large language models with human preferences, but preference-data composition may also influence...
Xinyue Zhao, Ruiyi Zhang, Liqin Ye +3 more
Nowcasting headline macroeconomic indicators, i.e., estimating an indicator's value for the current reference period before its official release, is...
Ashok Subbabhatta Gopalakrishna
Multi-agent AI platforms move quickly from staging to production, but the way agents establish trust remains rudimentary: an agent either transmits...
Wujie Xiong, Rabimba Karanjai, Yang Lu +2 more
Large language model agents place outputs from external skills into their execution context, allowing attacker-controlled data to influence later...
Hao Yan, Ziyu Yao
Large language models (LLMs) frequently generate source code containing vulnerabilities, yet little work studies the internal mechanisms that...
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 3,771+ 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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