AI Threat Alert indexes 3,371+ 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 61–80 of 1,142 papers
Clear filtersCorban Villa, Alp Eren Ozdarendeli, Sijun Tan +1 more
Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web's oldest attack surfaces. Cross-Site Scripting proved...
Jennifer Za, Julija Bainiaksina, Nikita Ostrovsky +2 more
Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface...
Anupam Wagle, Ifrat Ikhtear Uddin, Chaowei Zhang +1 more
Large language models (LLMs) exhibit remarkable capabilities but remain highly vulnerable to adversarial prompts and jailbreak attacks. Existing...
Yujiao Chen
We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and...
Harry Owiredu-Ashley
Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not. We argue that...
Aya Spira, Stav Cohen, Elad Feldman +3 more
The growing adoption of agentic LLM applications has introduced a new threat previously named as promptware. While prior work has established that...
Chethan Krishnamurthy Ramanaik, Tobias Callies, Michael Hecht +1 more
Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness,...
Xin Li, Jiaju Han, Ma Yaqi +5 more
Infrared vision-language models are increasingly used for perception under low-light and adverse visual conditions, yet their robustness to localized...
Cong Su, Jiaju Han, Xuemeng Sun +5 more
Vision-language models (VLMs) are increasingly deployed on infrared (IR) remote sensing imagery in security-critical settings, yet their adversarial...
Marwan Lazrag, Badis Hammi, Lorena Gonzalez-Manzano +1 more
Poisoning attacks against public datasets lead to major concerns, such as (i) misclassification of perceived objects when the poisoned data is used...
Mingchen Li, Meikang Qiu, Zifan Peng +4 more
Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate...
Yechao Zhang, Shiqian Zhao, Jiawen Zhang +5 more
Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and...
Woohyuk Choi, Juhee Kim, Taehyun Kang +3 more
AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security...
Neeraj Karamchandani, Piyush Nagasubramaniam, Sencun Zhu +1 more
Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage...
Om Solanki, Lopamudra Praharaj, Deepti Gupta +1 more
This paper presents an adversarial security study of the Policy-Aware LLM Retrieval-Augmented Generation (PA-LLM-RAG) framework for Internet of...
Josh Hills, Ida Caspary, Asa Cooper Stickland
As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence...
Bohan Liu, Wenqian Ye, Guangzhi Xiong +3 more
Models trained via Contrastive Language-Image Pretraining (CLIP) serve as the foundational vision encoders for most modern Large Vision Language...
Jeroen Janssen
Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known...
Yuqiang Sun, Han Liu, Ying Li +4 more
Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns...
Yueming Huang, Wenhan Yao, Fen Xiao +2 more
Recently, speech classification methods have gained widespread adoption in intelligent gadgets. Current study indicates that backdoor attacks provide...
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 3,371+ 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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