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 281–300 of 4,319 papers
Giovana Kerche Bonás, Thales Sales Almeida, Marcelo da Silva Reis +2 more
Recent literature shows that unsafe tool calls can often accompany textual refusals in language models. To investigate this hidden vulnerability, we...
Thuy Dung Nguyen, Jingyi Guo, Rana Seda Dubauskas +3 more
LLM-based agents increasingly execute long-horizon, end-to-end workflows involving shell commands, code modification, dependency installation, and...
Shaoyuan Xie, Zihan Zhang, Jingxuan Wang +6 more
Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and...
Alicia Colmenero-Fernández
Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through...
Jiahong Qian
Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures...
Tianshi Wang, Jingsong Wang, Fengling Li +3 more
Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does...
Mohsen Iranmanesh, Sina Moradi Sabet, Sina Marefat +7 more
Large language models are increasingly deployed as autonomous agents for automated program repair, patching software vulnerabilities through...
Zahraa Al Sahili, Ioannis Patras, Matthew Purver
Large language models (LLMs) are increasingly deployed across languages, yet their safety and reasoning capabilities remain predominantly evaluated...
Yixin Tan, Chia-Yi Hsu, Jiayang Liu +2 more
Automated jailbreak attacks rely on evaluation feedback to optimize adversarial prompts, but existing methods often lack fine-grained diagnostic...
Miaobo Hu, Shuhao Hu, Xiaobo Guo +4 more
We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read...
Florian Spelter, Johannes F. Loevenich, Tobias Hürten +4 more
Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on...
Adel Khorramrouz, Yi-Chia Wang, Sharon Levy
Social psychology has long described how people can be persuaded to interpret a harmful act as moral. Recent research suggests that Large Language...
Wei-Liang Liu, Cheng-Yen Tsai
Model signing authenticates the full-precision weights W an LLM is released with, yet users run a quantized artifact Q whose producing transformation...
Sarthak Gupta, Priyanka Tembey
Several recent studies propose detecting prompt injections from a language model's internal state. However, these methods do not measure a single...
Chenxu Wang, Xiaoyu Wen, Han Qi +4 more
Large language model (LLM) agents accomplish complex tasks through multi-turn interactions with external tools. However, malicious content embedded...
Min-Young Yu
Runtime policy verifiers can prevent agents from executing disallowed actions, but their decisions are typically used only for enforcement and...
Liam Davis, Romain Emil-Popescu, Suman Jana
Jailbreak attacks against large language models (LLMs) are evaluated using attack success rate (ASR), which depends on correctly identifying...
Kshitij Mishra, Salem Lahlou, Nils Lukas
Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that...
Yifan Lan, Zhongye Liu, Yurui Chang +4 more
Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores....
Bangya Liu, Jianhong Tu, Yueyang Pan +18 more
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current benchmarks mostly...
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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