AI Threat Alert indexes 3,023+ 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 181–200 of 272 papers
Clear filtersSasha Behrouzi, Lichao Wu, Mohamadreza Rostami +1 more
Safety alignment is essential for the responsible deployment of large language models (LLMs). Yet, existing approaches often rely on heavyweight...
Robert Ranisch, Sabine Salloch
The emergence of agentic AI marks a new phase in the digital transformation of healthcare. Distinct from conventional generative AI, agentic AI...
Ahmed Ryan, Ibrahim Khalil, Abdullah Al Jahid +4 more
The prevalence of malicious packages in open-source repositories, such as PyPI, poses a critical threat to the software supply chain. While Large...
Huijia Lin, Kameron Shahabi, Min Jae Song
Language models now routinely produce text that is difficult to distinguish from human writing, raising the need for robust tools to verify content...
Manuel Cherep, Pranav M R, Pattie Maes +1 more
The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs)....
David Puertolas Merenciano, Ekaterina Vasyagina, Raghav Dixit +4 more
LoRA adapters let users fine-tune large language models (LLMs) efficiently. However, LoRA adapters are shared through open repositories like Hugging...
Tianyu Chen, Dongrui Liu, Xia Hu +2 more
Clawdbot is a self-hosted, tool-using personal AI agent with a broad action space spanning local execution and web-mediated workflows, which raises...
George Alexandru Adam, Alexander Cui, Edwin Thomas +7 more
While historical considerations surrounding text authenticity revolved primarily around plagiarism, the advent of large language models (LLMs) has...
Jiyong Uhm, Minseok Kim, Michalis Polychronakis +1 more
Binary code analysis plays an essential role in cybersecurity, facilitating reverse engineering to reveal the inner workings of programs in the...
Zhaoxin Wang, Jiaming Liang, Fengbin Zhu +5 more
Large language models (LLMs) and multimodal LLMs are typically safety-aligned before release to prevent harmful content generation. However, recent...
Yujun Zhou, Yue Huang, Han Bao +8 more
While most AI alignment research focuses on preventing models from generating explicitly harmful content, a more subtle risk is emerging:...
Christian Rondanini, Barbara Carminati, Elena Ferrari +2 more
The proliferation of edge devices has created an urgent need for security solutions capable of detecting malware in real time while operating under...
Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria
Mining is rapidly evolving into an AI driven cyber physical ecosystem where safety and operational reliability depend on robust perception,...
Adel ElZemity, Joshua Sylvester, Budi Arief +1 more
SMS-based phishing (smishing) attacks have surged, yet training effective on-device detectors requires labelled threat data that quickly becomes...
Samal Mukhtar, Yinghua Yao, Zhu Sun +3 more
Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations...
Enrico Ahlers, Daniel Passon, Yannic Noller +1 more
Machine learning models are increasingly present in our everyday lives; as a result, they become targets of adversarial attackers seeking to...
Zijing Xu, Ziwei Ning, Tiancheng Hu +4 more
The rapid evolution of cyber threats has highlighted significant gaps in security knowledge integration. Cybersecurity Knowledge Graphs (CKGs)...
Weichen Yu, Ravi Mangal, Yinyi Luo +4 more
Large Language Models are rapidly becoming core components of modern software development workflows, yet ensuring code security remains challenging....
Jayesh Choudhari, Piyush Kumar Singh
Domain fine-tuning is a common path to deploy small instruction-tuned language models as customer-support assistants, yet its effects on...
Kun Wang, Zherui Li, Zhenhong Zhou +8 more
Omni-modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross-modal safety risks. However, a...
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 3,023+ 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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