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 388 papers
Clear filtersAmira Guesmi, Muhammad Shafique
Vision-language models (VLMs) have recently shown remarkable capabilities in visual understanding and generation, but remain vulnerable to...
Ruyi Zhang, Heng Gao, Songlei Jian +2 more
Backdoor attacks compromise model reliability by using triggers to manipulate outputs. Trigger inversion can accurately locate these triggers via a...
Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy
Large language models for code have achieved strong performance across diverse software analytics tasks, yet their real-world adoption remains...
Jianwei Li, Jung-Eun Kim
Backdoor attacks pose severe security threats to large language models (LLMs), where a model behaves normally under benign inputs but produces...
Xiangkui Cao, Jie Zhang, Meina Kan +2 more
Large Vision-Language Models (LVLMs) have shown remarkable potential across a wide array of vision-language tasks, leading to their adoption in...
Haodong Zhao, Jinming Hu, Yijie Bai +6 more
Federated Language Model (FedLM) allows a collaborative learning without sharing raw data, yet it introduces a critical vulnerability, as every...
Pratyay Kumar, Miguel Antonio Guirao Aguilera, Srikathyayani Srikanteswara +2 more
Model Context Protocol (MCP) servers have rapidly emerged over the past year as a widely adopted way to enable Large Language Model (LLM) agents to...
Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, Peter Kairouz
As AI assistants become widely used, privacy-aware platforms like Anthropic's Clio have been introduced to generate insights from real-world AI use....
Jia Hu, Youcheng Sun, Pierre Olivier
Software compartmentalization breaks down an application into compartments isolated from each other: an attacker taking over a compartment will be...
Ali Raza, Gurang Gupta, Nikolay Matyunin +1 more
Warning: This article includes red-teaming experiments, which contain examples of compromised LLM responses that may be offensive or upsetting. Large...
Shaswata Mitra, Raj Patel, Sudip Mittal +2 more
Multi-agent systems (MAS) powered by LLMs promise adaptive, reasoning-driven enterprise workflows, yet granting agents autonomous control over tools,...
Alexander Erlei, Lukas Meub
As AI agents increasingly act on behalf of human stakeholders in economic settings, understanding their behavior in complex market environments...
Eduard Hirsch, Kristina Raab, Tobias J. Bauer +1 more
IT systems are facing an increasing number of security threats, including advanced persistent attacks and future quantum-computing vulnerabilities....
Donghwa Kang, Hojun Choe, Doohyun Kim +2 more
Deploying deep neural networks (DNNs) on edge devices exposes valuable intellectual property to model-stealing attacks. While TEE-shielded DNN...
Anatoly Belikov, Ilya Fedotov
Large Language Models (LLMs) are increasingly served on shared accelerators where an adversary with read access to device memory can observe KV...
Geraldin Nanfack, Eugene Belilovsky, Elvis Dohmatob
Safety-aligned language models refuse harmful requests through learned refusal behaviors encoded in their internal representations. Recent...
Yizhe Xie, Congcong Zhu, Xinyue Zhang +5 more
Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative...
Achyutha Menon, Magnus Saebo, Tyler Crosse +3 more
The accelerating adoption of language models (LMs) as agents for deployment in long-context tasks motivates a thorough understanding of goal drift:...
Edouard Lansiaux
Federated Learning (FL) enables collaborative training of medical AI models across hospitals without centralizing patient data. However, the exchange...
Shuyi Zhou, Zeen Song, Wenwen Qiang +4 more
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this...
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