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 61–80 of 3,771 papers
Bijied Brahimi, Vincent Cohadon, Gabriel Glazman +3 more
Static malware detection for Windows Portable Executable files demands a careful balance between detection effectiveness, computational efficiency,...
Qi Rong Sua, Junhao Dong, Nguyen Duc Thai +3 more
Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to...
Hyeongjun Choi, Wonyoung Jung, Haehoon Seo +1 more
Large language models are being integrated into malware triage workflows as reasoning components that summarize static evidence and produce...
Mengxiao Wang, Nitesh Saxena
Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely...
Alex Remedios, Simon Storf, Fabien Roger +1 more
To keep coding agents from going off the rails, production systems now review each proposed action with a blocking monitor that can reject it before...
Matteo Golinelli, Idilio Drago, Matteo Boffa +2 more
AI agents for security inspect web pages, source code, logs, configuration files, and command outputs. These environments may contain deceptive...
Elizabeth Pavlova, Hidenori Tanaka
Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid...
Muhammad Abdullah Sohail
The Model Context Protocol (MCP) has emerged as the dominant interface for connecting autonomous agents to external data sources and execution...
Guosen Wu, Huizhen Huang, Guoxiong Long +2 more
Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss...
Jean-Charles Noirot Ferrand, David Adei, Anders Møller +1 more
Malicious npm package detection tools now leverage LLMs' semantic understanding of source code to detect malicious intent at scale. This capability...
Elia Nikolaou, Magnus Wiik Eckhoff, Robert Flood +3 more
LLM-based agents generate and execute multi-step plans that invoke external tools which can access private data or execute commands. In this setting,...
Youssef Hamdi Zafan Ibrahim, Muhammad Ikram, Mohammed Khalaf Salama
Running large language models (LLMs) locally is often considered more private than cloud-hosted inference because user prompts remain on the device....
Youjia Wang, Lin Xu, Yang Sun +3 more
Safety alignment teaches large language models (LLMs) to recognize harmful requests and reject risky instructions. Yet aligned models can fail when...
Hasnain Irshad, Anam Mughees, Neelam Mughees +2 more
Agentic browsers can execute security-sensitive actions under a user's authenticated session, making indirect prompt injection and deceptive...
Makram Chehayeb, Walid Fahs, Amina Rizk +2 more
The availability and consistency of online services remain vulnerable due to Distributed Denial of Service (DDoS) attacks. These attacks are evolving...
Murali Ediga, Sudipta Chattopadhyay
The Model Context Protocol (MCP) enables LLMs to invoke external tools, but every tool interaction exposes the model to attacker-controlled text...
Rushabh Vipulkumar Patel, Dipo Dunsin, Mohammed Almaiah +1 more
AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous,...
Franziska Roesner, Tadayoshi Kohno
Thompson's "Reflections on Trusting Trust" showed that a compiler can be poisoned to reinsert its own backdoor, so that even recompiling clean source...
Wuyang Dai, Moses Openja, Jiho Shin +2 more
Large language model (LLM)-based agents are increasingly used across software engineering, web automation, research, and productivity applications....
Tanzim Hossain Safin, Sharif Noor Zisad, Swakkhar Shatabda +1 more
Multi-agent LLM systems increasingly automate tasks involving agents with different levels of privilege, creating a security risk in which a...
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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AI Threat Alert