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 266 papers
Clear filtersElliot Jones, William Knottenbelt
Advances in Artificial Intelligence (AI) have led AI for Theorem Proving to become a promising means of formally verifying computer systems. Whilst...
Hobin Kim, Xiaoyuan Wu, Omer Akgul +2 more
Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and...
Guillermo Gil de Avalle, Laura Maruster, Shaina Raza +1 more
Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in...
Ning Ni, Yingjie Lao
Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks, but their large size introduces...
Kunlan Xiang, Haomiao Yang, Wenbo Jiang
Contrastive Language-Image Pre-training models are widely reused across downstream interfaces, including feature extraction, retrieval, reranking,...
Ziliang Wang, Kang An, Faqiang Qian +5 more
Although reinforcement learning (RL) has expanded the cognitive boundaries of large language models (LLMs), it often remains vulnerable to the...
Xinru Liu, Xianglong Zhang, Di Cai +3 more
Injecting malicious knowledge into retrieval-augmented generation (RAG) systems can manipulate retrieved evidence and mislead downstream generation,...
Aaditya Pai
Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and interleave...
Yujeong Kwon, Yiyue Zhang, Shakhzod Yuldoshkhujaev +3 more
Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains...
Manoj Parmar
Neuro-symbolic AI (NeSy) pairs neural perception with symbolic reasoning, making it attractive for high-stakes domains where explainability and...
Xiaolin Li, Ning Wang, Ninghui Li +1 more
Prior research suggests that differential privacy (DP) inherently enhances the robustness of federated learning (FL) against backdoor attacks. In...
Mufei Li, Shikun Liu, Dongqi Fu +5 more
Post-hoc context erasing over the KV cache is challenging because a local edit has a global consequence: once a span has been processed, its...
Fouad Bousetouane
AI agents must be evaluated as behavioral systems, not as isolated response generators. They reason across turns, call tools, preserve context,...
Yimeng Chen, Zhe Ren, Firas Laakom +3 more
Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that...
Chen Chen, Xiang Gao, Xianshun Wang +6 more
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive...
Ziniu Liu, Aiping Li
When a person's records appear in k independent data silos, each protected by (epsilon, delta)-differential privacy, standard composition yields a...
Qi Wang, Chengcheng Wan, Weijia He +4 more
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their safety remains a critical concern...
Rowdy Chotkan, Bulat Nasrulin, Johan Pouwelse +1 more
Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine...
Bojie Li
A personalized AI agent needs a user memory: a persistent model of who the user is, built across many conversations and consulted on each new one....
Xuanyu Yin, Yilin Jiang, Jun Zhou +3 more
As large language models (LLMs) are increasingly deployed in user-facing systems, black-box jailbreak defense has become an important practical...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert