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 56 papers
Clear filtersOshando Johnson, Ranjith Krishnamurthy, Rohith Kumar Shanmuganathan +2 more
Static application security testing (SAST) tools surface vulnerabilities early and their verdicts are rule-based and auditable. Yet developers...
Nicholas Roehner, Bryan Bartley, Jacob Beal +3 more
The rapid democratization of biotechnology, enhanced by large language models (LLMs) and agentic software, is eroding longstanding barriers that have...
Chaomeng Lu, Pooya Rostami Mazrae, Bert Lagaisse
Automated vulnerability detection is critical for securing modern software systems, yet existing learning-based approaches often treat functions as...
Martin Pinzger, Katharina Stengg, Christian Macho +2 more
Many recent approaches for vulnerability detection use large language models (LLMs) to classify functions as vulnerable or benign, leaving developers...
Gerardo Iuliano, Gabriele De Vito, Dario Di Nucci
Smart contracts are immutable programs deployed on blockchain platforms, where vulnerabilities can translate directly into financial loss. Among the...
Jonathan F. Jao, Andrew Kyle Lampinen
Automated red-team auditing of language models is an important way to evaluate safety-relevant behaviors. These audits implicitly search a tree of...
Amine Lbath, Massih-Reza Amini
A vulnerability costs more to fix the later it is discovered. As coding agents generate code faster than developers can review it, software needs a...
Xiarun Chen, Jiaxing Xu, Bozhi Wu +6 more
Missing project context can prevent build-dependent analyzers from constructing program relations required for vulnerability detection, while neural...
Haoran Xue, Nikta Akbarpour, Gias Uddin +1 more
Large language model (LLM) coding agents, such as Claude Code, are increasingly used for security auditing, with task-specific skills providing...
Heyang Tan, Chengxin Gao, Xin Wen +2 more
In real-world software development, the primary challenge in vulnerability detection is often not finding suspicious code, but identifying which...
Ruohan Li, Miaoqing Tian, Haipeng Qu +1 more
Large Language Models (LLMs) have been widely applied to software vulnerability detection. However, their performance is often limited by...
Sabrina Kaniewski, Tim Krämer, Julius Bächle +3 more
Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding...
Djamel Eddine Hakim Ghorab, Farid Mokhati, Mostafa Anouar Ghorab
The growing adoption of blockchain technologies, particularly the Ethereum platform, has amplified the critical role of smart contracts in...
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...
Enna Basic, Alberto Giaretta
Large Language Models (LLMs) are increasingly used for software vulnerability detection, but their performance depends on how source code is...
Tenghui Huang, Jiawen Kang, Dongning Liu +5 more
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories...
Ben Falchuk, Himanshu Garg, Euthimios Panagos +1 more
Just like software and hardware, business processes are susceptible to vulnerabilities that can lead to product quality issues, delays, and increased...
Sungju Yun, Sijune Hwang, Yeonjoon Lee +2 more
Detecting source code vulnerabilities is increasingly difficult as modern security flaws are rooted in complex causal dependencies between execution...
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Colton Payne +4 more
Large language models are increasingly used to reason about software vulnerabilities, but their outputs can silently violate domain knowledge,...
Mingyang Sun, Guozhu Meng
Large language models (LLMs) are increasingly applied to vulnerability detection due to their strong code comprehension capabilities, but most...
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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