AI Threat Alert indexes 4,319+ 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 1201–1220 of 1,740 papers
Clear filtersFelix Wang, Anudeep Das, Mei Nagappan +1 more
Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security...
Victor Giannakouris, Immanuel Trummer
Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the...
Yujiao Chen
We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and...
Phat T. Tran-Truong, Xuan-Bach Le, Minh Nhat Nguyen
Federated foundation-model adaptation increasingly relies on heterogeneous private artifacts (retrieval corpora, prompts and demonstrations, LoRA...
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...
Yang Shi, Jiaheng Fu, Yihe Huang +3 more
Large language models (LLMs) are increasingly deployed as autonomous agents that interact with external tools and services via the Model Context...
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...
Arun Malik
AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously...
Kiarash Ahi, Saeed Valizadeh
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI,...
Haowen Xu, Xue Tan, Lei Ma +6 more
While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems (MAS) face critical security challenges due to vulnerabilities...
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...
Zhangheng LI, Jianing Zhu, Junyuan Hong +4 more
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on cross-modal tasks by jointly training on large-scale textual and...
Md Raihan Uddin, Fatemeh Lotfi, Tolunay Seyfi +1 more
Open Radio Access Networks (O-RAN) increasingly delegate near-real-time control to deep reinforcement learning (DRL) xApps obtained from third-party...
Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1 more
Backdoor attacks severely threaten large-scale AI models. When model owners delegate training to external compute providers within a decentralized...
Nikita P. Kalinin, Rasmus Pagh
We present the dithered Gaussian mechanism, a novel alternative to the discrete Gaussian mechanism for differential privacy that discretizes the...
Alicia Parrish, Rajat Shinde, Sanket Badhe +57 more
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional...
Melika Honarmand, Samin Mahdipour Aghabagher, Martin Schrimpf
Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward...
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 4,319+ 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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