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 21–40 of 1,257 papers
Clear filtersMd Nazmul Hoque, Shaswata Mitra, Subash Neupane +2 more
Vulnerability classification based on root cause weaknesses is essential for numerous cybersecurity activities, where the Common Weakness Enumeration...
Aman Priyanshu, Supriti Vijay, Kimia Majd +8 more
Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can...
Roberto Riaño, Gorka Abad, Stjepan Picek +1 more
Pretrained world models, learned simulators that encode an observation into a latent state and predict how it evolves under actions, are beginning to...
Xiaoyan Li, Yunli Wang
Large Language Model (LLM) agents have demonstrated impressive capabilities across a variety of domains, particularly when integrated with external...
Oliver Stevanovic, Jasmin Wachter
Logic attack graphs grounded in scanner output provide explicit and auditable attack path reasoning LLM-based agents lack. Integrating symbolic...
Zhaofeng Yu, Haokai Ma, Dongyang Zhan +3 more
A centralized LLM-based multi-agent system (MAS) extends its functionality by registering new worker agents, whose descriptions are read by the...
Aashiq Muhamed, Mona T. Diab, Virginia Smith +2 more
Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns...
Yusuf Khalid Shire, Sang-Chul Kim
Prompt-injection detectors are typically evaluated using aggregate F1 on in-distribution test data, which offers limited insight into behavior under...
Bingzheng Wang, Xiaoyan Gu, Wentao Wang +3 more
Large language model (LLM) agents interact with external environments through tool invocation, but tool outputs can also expose them to indirect...
Haozhe Lu, Jiaqi Li, Xinyuan Zhu +1 more
Retrieval-Augmented Generation (RAG) can ground large language model (LLM) outputs in external evidence, but it also exposes the system to knowledge...
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...
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...
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...
Thomas Rivasseau
Large language model safety and security research is preoccupied with, among other things, detecting and preventing jailbreak attacks: alignment...
Julien Michel, Abdul Qadir Khan, Majed Jaber +1 more
In network traffic, legitimate behaviours and attack techniques evolve jointly - the phenomenon known as 'concept drift' [1]. Every detector is...
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