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 1–20 of 252 papers
Clear filtersZhezhen Cao, Weiyuan Ding, Bowen Xu
Static application security testing (SAST) tools such as CodeQL serve as security gates in continuous integration and code review, and a gate is also...
Ali Tajfar, Behnam Tajfar
Interpretability tools increasingly support safety claims, such as that a sparse dictionary has recovered most of a model's features. Such claims...
Jonas Limniatis, Bennet Schaarmann, Georg Reuber +1 more
Taxonomies are structured knowledge representations that support semantic search, recommendation, product catalog organization, and...
Penghui Li
Office documents are now routinely uploaded to large language models (LLMs) for processing. However, an LLM does not always see what a human sees,...
Hui Xuan Tan, Stefanos Chaliasos, Alastair F. Donaldson
A zero-knowledge virtual machine (zkVM) lets an untrusted prover convince a verifier that a program, typically Rust compiled to RISC-V, ran...
Zhuyi Zhao, Shengjie Yuan, Shengyu Wang +4 more
Single-pass scoring models such as Jev give each code function a vulnerability probability, so that security teams can rank many functions for review...
Zhuyi Zhao, Shengjie Yuan, Shengyu Wang +4 more
Single-pass scoring models such as Jev give each code function a vulnerability probability, so that security teams can rank many functions for review...
Long Dang, Ali Alfatemi, Kaiqi Xiong +4 more
Distributed machine learning (DML), including conventional distributed training and federated learning (FL), enables collaborative model development...
Kshitish Ghate, Michael Saxon, Tadayoshi Kohno +1 more
Large language models (LLMs) are often conditioned into specific roles such as tutors, interviewers, or agents acting for an organisation, through...
Mostafijur Rahman Akhond, Mahmuda Khatun, Gias Uddin
As AI coding assistants generate more code, the need to review it for security flaws grows. Existing approaches leave a gap: static analyzers...
Sai Suresh Macharla Vasu, Sumedh Santosh Joshi, Philipp Christmann +6 more
The number of paper submissions to major AI conferences has increased dramatically in recent years, substantially increasing the peer-review workload...
Sheila Zingg, Florian Tramèr, Martin Vechev +1 more
There are different levels of transparency for LLMs: open weights, open data, and/or open pipeline. Recently, more interest has emerged in fully...
Frank Breitinger, Jan Gruber, Christopher Hargreaves +3 more
Large language models (LLMs) are entering digital forensics as examination assistants and autonomous agents. Digital forensics inverts the usual...
Gadi Evron
LLM-based vulnerability discovery harnesses can miss real, reachable vulnerabilities because of how the harness around the model is built. Steps that...
Nishant Abhishek, Ping Charoenwet, Kla Tantithamthavorn
Finding vulnerabilities early, before they can affect the production system, is important for reducing the risk of any software attacks. However,...
Mirza Masfiqur Rahman, MD Ajwad Akil, Imtiaz Karim +3 more
Code generated by large language models often passes every functional test while remaining insecure. These security hallucinations are common,...
Jiaming Zhang, Zherui Li, Siqi Guo +3 more
As vision-language models (VLMs) transition from frozen-encoder pipelines to native multimodal architectures, the visual channel increasingly serves...
Harsh Aaryan, Ce Zhou
Autonomous vehicles (AVs) couple safety-critical perception to thermally constrained compute. Interfaces built for night perception and package...
Jiahong Qian
Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures...
Jerrold Huang, Junwei Quan, Saiyue Lyu +2 more
Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look...
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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