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 81–100 of 489 papers
Clear filtersShuoran Jiang, Wentao Zhu, Yang Xiang +8 more
Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity. In contrast, continual...
Junhoo Lee, Baekseung Kim, Seungyeon Kim +1 more
Traditional backdoor attacks inject artificial trigger patterns into training data, causing models to misclassify triggered inputs while maintaining...
Ravan Nazaraliyev, Yicheng Zhang, Nael Abu-Ghazaleh
NVIDIA's Grace Hopper (GH200) superchip represents a new generation of heterogeneous computing, tightly integrating a 72-core Arm-based CPU with a...
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...
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...
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...
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...
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...
Nutan Kumar Naik, Vijay Prasad Poudel, Aditya Kumar Saroj +2 more
Benchmarks report what an Artificial Intelligence (AI) agent can do on a task suite, but not whether one released version has been engineered with...
Inderjeet Singh
Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize...
Wenbin Chen, Jiexin Wang, Guanting Wu +2 more
The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to...
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +6 more
Browser agents need to make decisions and execute actions on behalf of users while consuming untrusted webpages, creating opportunities for malicious...
Arya Fayyazi, Mehdi Kamal, Massoud Pedram
Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact...
Zhixiong Zhuang, Jin Jia, Zhiwei Shang +1 more
Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a...
Shihab Ahmed, Md Sajidul Islam Sajid, Teryl Taylor +2 more
Malware defenses often remove or isolate suspicious programs as quickly as possible. While effective for containment, this approach can also waste an...
Saisab Sadhu, Shreeyans Arora, Pratinav Seth
Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision...
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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