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 261–280 of 1,455 papers
Clear filtersYilin Zhang, Yingkai Hua, Chunyu Wei +2 more
Vision-language model (VLM) based web agents demonstrate impressive autonomous GUI interaction but remain vulnerable to deceptive interface elements....
Isaac David, Arthur Gervais
Security updates create a short but important window in which defenders and attackers can compare vulnerable and patched software. Yet in many...
Di Lu, Bo Zhang, Xiyuan Li +5 more
Self-hosted computer-use agents (SHCUAs), such as OpenClaw, combine natural-language interaction with direct access to host-side resources, including...
Chengjie Wang, Jingzheng Wu, Xiang Ling +2 more
Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third-party...
Leo Linqian Gan, Jeffery Wu, Longyuan Ge +6 more
Autonomous LLM agents face a critical security risk known as workflow hijacking, where attackers subtly alter tool and skill invocations. Existing...
Guoxin Lu, Letian Sha, Qing Wang +4 more
The safety alignment of Large Language Models (LLMs) remains vulnerable to Harmful Fine-tuning (HFT). While existing defenses impose constraints on...
Qinfeng Li, Yuntai Bao, Jianghui Hu +5 more
LLM agents rely on prompts to implement task-specific capabilities based on foundation LLMs, making agent prompts valuable intellectual property....
Bonan Ruan, Yeqi Fu, Chuqi Zhang +3 more
GitHub Continuous Integration (CI) workflows increasingly integrate Large Language Models (LLMs) to automate review, triage, content generation, and...
Siyuan Li, Aodu Wulianghai, Xi Lin +6 more
The increasing prevalence of Large Language Models (LLMs) in content creation has made distinguishing human-written textual content from...
Christopher G. Pedraza Pohlenz, Hassan Jalil Hadi, Ali Hassan +1 more
LLMs are increasingly explored for malware analysis; however, current LLM-based malware attribution remains limited by unsupported indicators and...
Xiaomin Li, Andrzej Banburski-Fahey, Jaron Lanier
Auditing language-model outputs often requires more than judging correctness: an auditor may need to identify which source document most likely...
Dasol Choi, Eugenia Kim, Jaewon Noh +14 more
Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover,...
Samuel Korn
Retrieval-Augmented Generation (RAG) systems are vulnerable to knowledge base poisoning, yet existing attacks have been evaluated almost exclusively...
Xinjie Shen, Rongzhe Wei, Peizhi Niu +6 more
Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful...
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3 more
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors...
Marco Rando, Samuel Vaiter
Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit...
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera +2 more
The open-source ecosystem has accelerated the democratization of Large Language Models (LLMs) through the public distribution of specialized Low-Rank...
Chenglin Yang
Modern AI agents execute real-world side effects through tool calls such as file operations, shell commands, HTTP requests, and database queries. A...
Jan Dolejš, Martin Jureček, Róbert Lórencz
Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work...
Jie Zhang, Pura Peetathawatchai, Florian Tramèr +1 more
Vision-language models (VLMs) are increasingly deployed as trusted authorities -- fact-checking images on social media, comparing products, and...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert