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 221–240 of 266 papers
Clear filtersYanyan Luo, Xue Han, Ruiqiao Bai +10 more
Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term...
Jianguo Zhu
Retrieval-augmented generation (RAG) systems often serialize user queries, retrieved documents, metadata, system labels, and task instructions into...
Ziqian Zhong, Ivgeni Segal, Ivan Bercovich +3 more
Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit...
Emre Turan
As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop...
Mikhail Terekhov, Caglar Gulcehre, Vivek Hebbar +1 more
AI models deployed in critical domains, such as AI safety research, may subtly sabotage our efforts due to misalignment. Diffuse AI Control is a...
Kuncan Wang, Ziting Wang, Peizhuo Lv +4 more
Data agents integrate LLM-driven reasoning with relational data access, executable analytical tools, and multi-step workflow orchestration, making...
Bernhard Kneip, Nhien-An Le-Khac, Hong-Hanh Nguyen-Le
Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present...
Lin-Fa Lee, Yi-Yu Chang, Chia-Mu Yu +1 more
WebMCP is a newly emerging protocol that enables websites to expose tools directly to AI agents, bypassing traditional user interfaces and...
Victor De Marez, Luna De Bruyne, Walter Daelemans
Factual sycophancy occurs when a language model abandons a correct, verifiable answer under social pressure. Because a flip occurs only when pressure...
Parsa Memarzadehsaghezi, Zahra Hashemi, Pooria Madani +1 more
Evasion attacks present a significant challenge to the robustness of machine learning (ML)-based classifiers, particularly in critical applications...
Xi Yang, Chang Liu, Zhenglin Huang +4 more
As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This...
Liangsheng Liu, Si Chen, Jiamin Wu +5 more
Vision-Language Models (VLMs), such as CLIP, have shown strong zero-shot generalization but remain highly vulnerable to adversarial perturbations,...
Weilin Lin, Ziqi Lin, Zhenxing Zhou +4 more
Image safety classifiers serve as a critical component of contemporary content moderation systems on the internet. However, their resilience against...
Jiawen Zhang, Kejia Chen, Jiachen Ma +7 more
Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines...
Virginia Ceccatelli, Yejin Jeon, David Ifeoluwa Adelani
Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on...
Abzal Aidakhmetov, Donato Crisostomi, Tommaso Mencattini +3 more
Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is...
Nikolai Dorofeev, Alexey Odinokov, Rostislav Yavorskiy
In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We...
Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker
Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted,...
Minseok Choi, Seungbin Yang, Dongjin Kim +5 more
Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety...
Shuze Liu, Qianwen Guo, Yushun Dong
Large language models (LLMs) are increasingly deployed through hosted APIs, making model extraction a practical threat to model ownership and service...
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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AI Threat Alert