AI Threat Alert indexes 3,406+ 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 581–600 of 1,673 papers
Clear filtersDongcheng Zhang, Yiqing Jiang
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a...
Ziyao Tang, Pengkun Jiao, Bin Zhu +3 more
Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under...
Ting Zhang, Yikun Li, Chengran Yang +15 more
Software vulnerabilities remain one of the most persistent threats to modern digital infrastructure. While static application security testing (SAST)...
Shozo Saeki, Minoru Kawahara, Hirohisa Aman
A nearest-neighbor framework is a fundamental tool for various applications involving Large Language Models (LLMs) and Visual Language Models (VLMs)....
Yuan Fang, Yiming Luo, Aimin Zhou +1 more
Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains...
Yihao Zou, Tianming Zheng, Futai Zou +1 more
Fuzzing has become a widely adopted technique for vulnerability discovery, yet it remains ineffective for structured-input programs due to strict...
Hailin Liu, Eugene Ilyushin, Jie Ni +1 more
Large language model (LLM) agents are vulnerable to prompt-injection attacks that propagate through multi-step workflows, tool interactions, and...
Jianming Tong, Hanshen Xiao, Krishna Kumar Nair +5 more
Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive...
Dongwook Lee, Eunwoo Song, Che Hyun Lee +2 more
While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party...
Rina Mishra, Gaurav Varshney, Doddipatla Sesha Sahithi
The rapid adoption of open-source Large Language Models (LLMs) in offline and enterprise environments has introduced a largely unexamined security...
XiangRui Zhang, Qiang Li, Haining Wang
Binary analysis increasingly relies on large language models (LLMs) to perform semantic reasoning over complex program behaviors. However, existing...
Xuanli He, Bilgehan Sel, Faizan Ali +3 more
Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and...
Firas Ben Hmida, Philemon Hailemariam, Kashif Ali Khan +1 more
Deep neural networks (DNNs) remain largely opaque at inference time, limiting our ability to detect and diagnose malicious input manipulations such...
Pavel Chizhov, Egor Bogomolov, Ivan P. Yamshchikov
Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves...
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3 more
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing...
Xixun Lin, Yang Liu, Yancheng Chen +9 more
The performance of large language model (LLM) agents depends critically on the execution harness, the system layer that orchestrates tool use,...
Xiaohua Wang, Muzhao Tian, Yuqi Zeng +20 more
Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and...
Sujan Ghimire, Parsa Mirfasihi, Muhtasim Alam Chowdhury +6 more
The globalization of integrated circuit (IC) design and manufacturing has increased the exposure of hardware intellectual property (IP) to untrusted...
Prajas Wadekar, Venkata Sai Pranav Bachina, Kunal Bhosikar +2 more
3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this...
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 3,406+ 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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