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 601–620 of 4,319 papers
Florian Krone, Elena Hoemann, Sven Hallerbach
Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small...
Akihisha Fujiyama, Niwase Shamim
AI is increasingly used to automate computer security testing, and the tools must decide for themselves whether an attack succeeded. A finding that a...
Heewon Baek, Alsharif Abuadbba, Kristen Moore +2 more
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other...
Rohit Patel, Susil Kumar Mohanty, Jeenal Chaudhary
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators...
Ben Shapira, Roi Cohen, Shang-Tse Chen +1 more
With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models...
Frank E. Bobe, Gregory D. Vetaw, Darshan W. Bryner +2 more
Activation steering modifies LLM behavior at inference time, but identifying where and how strongly to steer remains manual. We introduce Deep Noir,...
Chao Huang, Meng Tong, Kejiang Chen
While multimodal large language models (MLLMs) enable a wide range of image-text reasoning tasks, recent incidents indicate that they are vulnerable...
Leilei Chen, Lan Zhang, Chen Tang +5 more
In pay-per-token LLM services, the more a model says, the more users pay. Dishonest providers can covertly manipulate generation to inflate output...
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....
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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