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 121–140 of 1,450 papers
Clear filtersMohammad Reza Mirbagheri, Amir Mahdi Sadeghzadeh
Aligned large language models (LLMs) remain vulnerable to jailbreak prompts that circumvent their safety guardrails, making the identification of...
Seyedarmin Azizi, Erfan Baghaei Potraghloo, Massoud Pedram
A typed decision model reads a piece of text and returns a probability over caller-defined options, each with a short written definition, generating...
Heyang Tan, Chengxin Gao, Xin Wen +2 more
In real-world software development, the primary challenge in vulnerability detection is often not finding suspicious code, but identifying which...
Luman Zhao, Minghui Xu, Yue Zhang +1 more
Large language models (LLMs) perform remarkably well on complex tasks, yet remain highly vulnerable to prompt injection attacks, where malicious...
Adam Y. J. Jones, Yu Yuan, Sergio Maffeis
Speculative decoding is a popular technique for increasing the speed and reducing the costs of large language model (LLM) inference by verifying...
Dang K Le, Wenxuan Shi, Xinyu Xing
Large language models (LLMs) have shown strong potential for automated vulnerability patching, but current benchmarks can substantially distort...
Juanyang Xu, Zheng Wang, Xingyu Zhao +2 more
When the harmfulness of an LLM agent's output can be quantified, a natural jailbreaking objective is to maximize expected harmfulness over admissible...
Zitong Yao, Jiangrong Wu, Yixi Lin +3 more
Large language model (LLM) agents interact with external resources to complete complex user tasks, exposing them to indirect prompt injection (IPI),...
Xiaoyi Pang, Haoyue Feng, Quanxin Shou +3 more
Vision-Language-Action models (VLAs) are increasingly deployed in safety-critical physical environments, yet their adversarial robustness remains...
Ruohan Li, Miaoqing Tian, Haipeng Qu +1 more
Large Language Models (LLMs) have been widely applied to software vulnerability detection. However, their performance is often limited by...
Yupu Wang, Zhengyuan Jiang, Reachal Wang +1 more
Modern agentic coding frameworks increasingly rely on community-shared rule files (e.g., AGENTS.md or .cursorrules) to guide autonomous code...
Xunlei Qian, Yingqian Cui, Yuping Lin +2 more
Jailbreak defenses for large language models are usually calibrated on a fixed set of known attack methods. However, new attack methods keep...
Pengfei He, Deep Mitra, Vishesh Sharma +4 more
LLM agents retrieve untrusted content and act through tools, creating indirect prompt-injection risks that can cause unauthorized actions or...
Sarim Hashmi, Mukul Ranjan, Kshitij Mishra +3 more
Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent...
Habibur Rahaman, Swastik Bhattacharya, Sanjay Das +2 more
Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few...
Habibur Rahaman, Swastik Bhattacharya, Sanjay Das +2 more
Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few...
Niveen O. Jaffal, Ahmet Yuksel, David Mohaisen
Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a...
Mandana Ghadamian, David Mohaisen
Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design....
Heyam Bin Jahlan Areej Alhothali Abeer Alhothali
The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in...
Haneen Najjar, Luca Scionis, Haritz Puerto +1 more
AI agents increasingly rely on modular third-party "skills" that are dynamically selected by skill routers to execute complex tasks. While recent...
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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