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 121–140 of 562 papers
Clear filtersLei Zhao, Abhay Bhaskar, Edgar Dobriban
AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI)...
Xiao Yang, Ronghao Fu, Zhiwen Lin +10 more
Remote sensing vision-language models commonly rely on pretrained visual encoders to convert images into semantic features before language-model...
Qiqi Liu, Thorsten Holz, Shilin Ye +1 more
Multi-agent systems extend large language models (LLMs) by decomposing tasks among specialized agents, but their distributed decision process creates...
Simiao Liu, Fang Liu, Li Zhang +2 more
Large language model (LLM) agents are increasingly used for automated vulnerability repair (AVR), where repository-level reasoning enables them to...
Simiao Liu, Li Zhang, Fang Liu +3 more
Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can...
Zhen Xu, Zihao Wang, Yuhua Sun +1 more
Side-channel attacks exploit unintended information leakage from system behavior and continue to pose serious privacy risks in modern platforms....
Nils Loose, Joseph Bienhüls, Kristoffer Hempel +2 more
Automated detection of vulnerability-fixing commits (VFCs) is critical for timely security patch deployment, as advisory databases lag patch releases...
Toluwani Aremu, Nils Lukas, Jie Zhang
Watermarking is widely proposed for provenance, attribution, and safety monitoring in generative models, yet is typically evaluated only under...
Joana Pasquali, Ramiro N. Barros, Arthur S. Bianchessi +7 more
LoRA is widely adopted for continual fine-tuning of Large Language Models due to its parameter efficiency, modularity across tasks, and compatibility...
Hao Wang, Hanchen Li, Qiuyang Mang +3 more
Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward...
Rezarta Islamaj, Robert Leaman, Joey Chan +13 more
Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain...
Sam Herring, Jake Naviasky, Karan Malhotra
Language models are instruction-tuned to refuse harmful requests, but the mechanisms underlying this behavior remain poorly understood. Popular...
Sae Furukawa, Alina Oprea
Supervised Finetuning (SFT) has become one of the primary methods for adapting a large language model (LLM) with extensive pre-trained knowledge to...
Shuo Ni, Tong Wang, Jing Zhang +4 more
Vision-Language Models (VLMs) increasingly operate on ultra-high-resolution (UHR) Earth observation imagery, yet they remain vulnerable to a severe...
Fanpu Cao, Xin Zou, Xuming Hu +1 more
Multimodal large language models (MLLMs) have become a key interface for visual reasoning and grounded question answering, yet they remain vulnerable...
Stefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu
The irreversible nature of blockchain transactions makes the identification of smart contract vulnerabilities an essential requirement for secure...
Pedro Conde, Henrique Branquinho, Valerio Mazzone +3 more
AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will...
Saba Pourhanifeh, AbdulAziz AbdulGhaffar, Ashraf Matrawy
Large Language Models(LLMs) are increasingly explored for cybersecurity applications such as vulnerability detection. In the domain of threat...
Chiyu Zhang, Huiqin Yang, Bendong Jiang +8 more
The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content...
Dahlia Shehata, Ming Li
Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that...
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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