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 121–140 of 3,771 papers
Rui Cao, Shaojing Fan, Liming Fang +3 more
Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying...
Toshif Khan, Muhammad Abusaqer
Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative...
Mohammad Farhad, Shuvalaxmi Dass
Large language models (LLMs) are increasingly used to generate and assist with software development, yet existing vulnerability datasets largely...
Asif Pinjari, Mithun Paul Saint-Germain
When an indirect prompt injection succeeds against an LLM agent, the compromise is visible in the agent's own behavior: a benign prefix of tool...
Zehua Zhang, Jie Hu, Pratham Hegde +13 more
Conventional vulnerability analysis relies on either system access or dynamic interaction, all of which may be unavailable to third-party analysts...
Victoria Lovelace, Cameron Berryman, Yuhan You +3 more
Large language model (LLM) agents are increasingly applied to penetration testing, but we still know little about what they can do or how they fail....
Jessica Pourleyli, Maitreyee Das Urmi, Glaucia Melo
Advances in large language models (LLMs) fuel the quest for scalable methods to assess the security of generated and security-sensitive software....
Anna Gazani, Spyridon Kounoupidis, Panagiotis Katsaros +3 more
The integration of Large Language Models (LLMs) into Security Operations Centers (SOCs) streamlines threat intelligence but introduces critical...
Yiwei Fang, Yichen Liu, Ze Jin +3 more
Logic flaws pose significant challenges in the design and implementation of modern, semantically rich systems and applications, impacting security,...
Bokang Zeng, Zheng Gao, Xiaoyu Li +2 more
Watermarking the final patch produced by a coding agent provides provenance evidence for the submitted artifact, but does not authenticate the...
Ivana Clairine Irsan, Ratnadira Widyasari, Huihui Huang +6 more
Static analysis remains a cornerstone of software security, yet the effectiveness of tools such as CodeQL is often limited by the substantial manual...
Zhantong Xue, Pingchuan Ma, Zhaoyu Wang +3 more
Zero-knowledge (ZK) proof systems for neural-network inference compile the model into a system of arithmetic constraints. Many of these constraints...
Ryan Lum, Yongfeng Zhang
AI systems become more useful when they can adapt to the people using them, but in multi-agent systems, useful context learned by one agent often...
Jing Guan, Yachao Yang, Zhaoliang Liu +3 more
Large language models remain fragile against malicious fine-tuning, motivating training-time defenses against harmful persona drift. Preventative...
Hanyi Zhou, Chenyang Li, Yuanzhe Pang +3 more
Trusted Execution Environments (TEEs) offer a promising mechanism for safeguarding the intellectual property of on-device Large Language Models...
Arnab Chattopadhayay, Debdipta Halder
Large language model agents produce fluent action sequences across a wide range of tasks, yet they fail in characteristic ways once the environment...
Rafael M. Mamede, Pedro C. Neto, Ana F. Sequeira
Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and...
Jinyang Li, Mingyu Guo, Hung X. Nguyen
Large language models (LLMs) have been ex- ploited to generate malware, but the effective- ness of guardrails for code generation secu- rity remains...
Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa +2 more
Agentic systems are rapidly moving to production, where they read untrusted inputs, call tools with real permissions, and act autonomously, expanding...
Ammar Kamoona, Sajad Koushkbaghi, Mahdi Jalili +2 more
The digitalisation of electrical distribution networks has increased the exposure of power-grid infrastructure to cyber attacks. Existing intrusion...
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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