AI Threat Alert indexes 3,771+ 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 41–60 of 1,257 papers
Clear filtersViet K. Nguyen, Mohammad I. Husain
Agentic AI frameworks let a language model plan, keep memory, and call tools that reach real files, mail, and services. Most of these agents also...
Md. Wasiul Haque, Sagar Dasgupta, Mizanur Rahman
Automated vehicles rely on millions of lines of safety-critical software, yet general-purpose analyzers do not understand which code can affect...
Tejasvi C. Addagada
Aligned language models fail under two independent pressures: the structural jailbreak class recently formalized as Involuntary In-Context Learning...
Dimitrios Stamatios Bouras, Yihan Dai, Sergey Mechtaev
Coding agents use system-level tools to read files, execute commands, and modify source code. Within the agent's sandbox, these tools often carry...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
Automated red-team attacks and blue-team defenses for large language models (LLMs) are advancing quickly. However, attackers and defenders are built...
Jiahao Shi, Edward Tsien, Yifeng Di +10 more
The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing...
Boyang Zhang, Qingxin Xiao, Lingwei Dang +1 more
Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions...
Feifei Liu, Jintao Cheng, Chi Man Vong +1 more
Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense...
Weizhe Wang, Yitong Zhang, Yao Zhang +4 more
Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration...
Nivedita Singh, Alsharif Abuadbba, Yansong Gao +2 more
Large language models (LLMs) are becoming integral to web applications and browser agents, transforming online interactions while introducing new...
Pengxun Li, Litian Zhang, Jianwei Hou +4 more
Modern AI agent harnesses expose lifecycle hooks that bind shell commands to runtime events such as session start, tool calls, and file edits. These...
Saikat Mondal, Mamta, Deeksha Varshney +2 more
Large language models (LLMs) are increasingly used in multilingual settings, yet their safety is still evaluated primarily in English. This limits...
Jakub Reš, Petr Kaška, Martin Perešíni +2 more
Large language models (LLMs) are vulnerable to jailbreak attacks that bypass safety alignment through carefully crafted prompts. Many existing...
Varun Gadey, Ziad Marey, Alexandra Dmitrienko
Retrieval-Augmented Code Generation (RACG) improves LLM-based software development by retrieving external code artifacts, documentation, and patches,...
Luca Migliaccio, Roberto Natella, Naghmeh Ivaki +2 more
Assessing vulnerability detection tools for smart contracts requires datasets with known ground truth, yet such datasets are scarce and difficult to...
Atsuki Sato, Martin Aumüller, Yusuke Matsui
The PGM-index (Ferragina and Vinciguerra, VLDB'20) is one of the most practical learned indexes, owing to its theoretical elegance and consistently...
Chengyin Hu, Dingyi Lu, Jiaju Han +5 more
Infrared vision-language models (IR-VLMs) have emerged as a promising paradigm for multimodal perception under low-visibility conditions, yet their...
Enna Basic, Alberto Giaretta
Large Language Models (LLMs) are increasingly used for software vulnerability detection, but their performance depends on how source code is...
Nikita Oblakov, Sabrina Sadiekh, Evgeniy Kokuykin
Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common...
Chou Jin Chua, Sarang Nambiar, Murali Srinivasan +1 more
We present AKRASIA, a stealthy, inference-time backdoor attack against reasoning-based Code LLMs. AKRASIA aims to achieve a backdoor target (e.g.,...
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 3,771+ 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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