AI Threat Alert indexes 3,082+ 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 1721–1740 of 3,082 papers
Eranga Bandara, Ross Gore, Sachin Shetty +9 more
6G networks are expected to be AI-native, intent-driven, and economically programmable, requiring fundamentally new approaches to network slice...
Xingcheng Xu, Jingjing Qu, Qiaosheng Zhang +4 more
The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral...
Quy-Anh Dang, Chris Ngo
Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors....
Yuxiang Wang, Hongyu Liu, Dekun Chen +2 more
As Speech Language Models (SLMs) transition from personal devices to shared, multi-user environments such as smart homes, a new challenge emerges:...
Yangyang Guo, Ziwei Xu, Si Liu +2 more
This study reveals a previously unexplored vulnerability in the safety alignment of Large Language Models (LLMs). Existing aligned LLMs predominantly...
Sen Nie, Jie Zhang, Zhuo Wang +2 more
Vision-language models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, yet remain highly vulnerable to adversarial...
Chi Zhang, Wenxuan Ding, Jiale Liu +3 more
Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks. However, their...
Nirhoshan Sivaroopan, Kanchana Thilakarathna, Albert Zomaya +6 more
Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that...
Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3 more
Secure code review is critical at the pre-commit stage, where vulnerabilities must be caught early under tight latency and limited-context...
Satyapriya Krishna, Matteo Memelli, Tong Wang +5 more
Amazon published its Frontier Model Safety Framework (FMSF) as part of the Paris AI summit, following which we presented a report on Amazon's Premier...
Harsh Chaudhari, Ethan Rathbun, Hanna Foerster +5 more
Chain-of-Thought (CoT) reasoning has emerged as a powerful technique for enhancing large language models' capabilities by generating intermediate...
Henry Chen, Victor Aranda, Samarth Keshari +2 more
Prompt-based attack techniques are one of the primary challenges in securely deploying and protecting LLM-based AI systems. LLM inputs are an...
Zahra Hashemi, Zhiqiang Zhong, Jun Pang +1 more
The rapid evolution of large language models (LLMs) has fuelled enthusiasm about their role in advancing scientific discovery, with studies exploring...
Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Autonomous unmanned aerial vehicle (UAV) systems are increasingly deployed in safety-critical, networked environments where they must operate...
Dongrui Liu, Qihan Ren, Chen Qian +40 more
The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current...
Zihan Wu, Jie Xu, Yun Peng +2 more
Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to two key limitations: the heterogeneity of vulnerability...
Gabriel Lee Jun Rong, Christos Korgialas, Dion Jia Xu Ho +3 more
Existing automated attack suites operate as static ensembles with fixed sequences, lacking strategic adaptation and semantic awareness. This paper...
Geunsik Lim
As climate-related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective...
Mengyuan Jin, Zehui Liao, Yong Xia
Multimodal Large Language Models (MLLMs) have shown remarkable capability in assisting disease diagnosis in medical visual question answering (VQA)....
Krittin Pachtrachai, Petmongkon Pornpichitsuwan, Wachiravit Modecrua +1 more
Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented...
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 3,082+ 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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