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 1,050 papers
Clear filtersMohammed N. Swileh, Shengli Zhang, Kai Lei
Software-Defined Networking (SDN) provides flexible and programmable network management; however, its centralized control architecture remains highly...
Faruk Alpay, Taylan Alpay
LLM agents process trusted instructions, retrieved records, and tool observations through a common generative channel. This conflates data flow with...
Yue Liu, Yanjie Zhao, Yunbo Lyu +3 more
Agentic AI coding assistants can edit files, run commands, and access the internet on behalf of developers. However, their reliance on unvetted...
Lingyao Li, Junjie Xiong, Changjia Zhu +5 more
Large language models (LLMs) are increasingly used in academic peer review, yet their reliability, alignment with human judgment, and robustness to...
Bingyu Yan, Xiaoming Zhang, Jinyu Hou +4 more
While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating...
Aditya Sridhar
Concept Bottleneck Models (CBMs) have emerged as a cornerstone approach for interpretable machine learning, providing human-understandable...
Dongpeng Zhang, Ke Ma, Yangbangyan Jiang +4 more
Adversarial images pose a severe security threat to multimodal large language models through prompt injection. Existing defenses largely lack a...
Jingyi Kang, Junyu Lu, Bo Xu +4 more
Large language models (LLMs) require robust toxicity evaluation beyond explicit wording. This setting remains underexplored in Chinese, where...
Aman Saxena, Jan Schuchardt, Yan Scholten +1 more
Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations, including poisoning attacks via randomized training...
Ze Sheng, Zhicheng Chen, Qingxiao Xu +2 more
Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise...
Giorgio Piras, Raffaele Mura, Fabio Brau +4 more
Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal...
Pengyu Sun, Qishu Jin, Enhao Huang +4 more
Model Context Protocol (MCP) has emerged as a standard interface for connecting LLM agents to external tools. Because MCP servers expose privileged...
Abdullah Al Nomaan Nafi, Fnu Suya, Swarup Bhunia +1 more
Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial...
Leitao Yuan, Qinghua Mao, Daizong Liu +5 more
Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate...
Laura Jiang, Reza Ryan, Qian Li +1 more
Single-turn safety evaluation is a poor proxy for real fraud defense, where attackers escalate across multiple rounds. This paper evaluates fraud...
Jiachen Ma, Jiawen Zhang, Xiangtian Li +3 more
While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that...
Yifei Wang, Tianlin Li, Xiaohan Zhang +3 more
Inference optimization is a vital technique for deploying LLMs at scale. Compilation is the most widely adopted optimization technique for LLMs....
Yasmine Hayder
Knowledge Graphs (KGs) are a powerful representation of linked data, offering flexibility, semantic richness, and support for knowledge enrichment...
Zheng Lin, Zhenxing Niu, Haoxuan Ji +2 more
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning...
Doguhuan Yeke, Yanming Zhou, Leo Y. Lin +3 more
Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical...
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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