AI Threat Alert indexes 3,406+ 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 101–120 of 981 papers
Clear filtersYuanzhi Liu, Shousheng Zhao, Bo Zhou +2 more
Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to...
Brett Reynolds
Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model has followed an...
Tien-Huy Nguyen, Minh-Nhat Nguyen, Nguyen Nhat Huy +9 more
Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning....
Tong Xu, Xinzhe Cao, Zhihui Zhu +2 more
Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical...
Jiahui Wang, Zhenyuan Li, Zhengkai Wang +2 more
Sophisticated cyber attacks, particularly Advanced Persistent Threats (APTs), require effective post-intrusion forensic analysis. Provenance-based...
Siddhant Panpatil, Arth Singh, Mijin Koo +3 more
Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch...
Robert Schambach, Quoc Do Le, Sergei Arnautov +1 more
Large Language Models (LLMs) have rapidly proliferated, driving widespread adoption of AI applications. Most deployments rely on centralized...
Hongliang Liu, Yuhao Wu, Tung-Ling Li
AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from...
Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos
Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do...
Xudong Shen, Li Yuan, Ye Chen +3 more
Large Language Models (LLMs) exhibit strong semantic capabilities, yet their resilience to manipulative linguistic patterns such as logical fallacies...
Afia Mubashir, Boden Moraski, Stephanie Choi +1 more
Artificial intelligence (AI) systems are increasingly integrated into daily life, with millions now using AI chatbots built on Large Language Models...
Brent A. Griffin, Jason J. Corso
Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by...
Luciano Pianese, Vittorio Orbinato, Pietro Liguori +1 more
Generative AI has emerged as a significant cybersecurity threat, with several recent attack campaigns leveraging LLMs to generate code for malicious...
Nicole Geumheon Liu, Haeun Jang, Yonghyun Jun +1 more
Large Language Models (LLMs) often fail to maintain instruction hierarchies (IH) when processing multi-source inputs with varying role-level...
Caglar Uysal, Baturay Birinci, Süha Orhun Mutluergil +1 more
Large Language Models (LLMs) have rapidly evolved, transforming industries by automating complex tasks and generating human-like content. However, as...
Ryan Fetterman
LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data. We show...
Inderjeet Singh, Andrés Murillo, Motoyoshi Sekiya +2 more
Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image...
Praneeth Narisetty, Shiva Nagendra Babu Kore, Uday Kumar Reddy Kattamanchi +1 more
Recent work (2024 to 2026) has converged on a strategy for defending tool-using LLM agents against indirect prompt injection: rather than training...
Giulian Biolo, Michael Tezza, Yuanjun Gong +1 more
Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers....
Hyejun Jeong, Dzung Pham, Amir Houmansadr +1 more
To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs. Many...
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 3,406+ 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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