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 4221–4240 of 4,319 papers
Marco Zimmerli, Andreas Plesner, Till Aczel +1 more
Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training...
Dennis Jacob, Emad Alghamdi, Zhanhao Hu +2 more
Large language models (LLMs) have become increasingly popular due to their ability to interact with unstructured content. As such, LLMs are now a key...
Guolei Huang, Qinzhi Peng, Gan Xu +3 more
As Vision-Language Models (VLMs) move into interactive, multi-turn use, safety concerns intensify for multimodal multi-turn dialogue, which is...
Simin Chen, Yixin He, Suman Jana +1 more
LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair (APR). APR agents automatically fetch...
Ruolin Chen, Yinqian Sun, Jihang Wang +3 more
Embodied agents powered by large language models (LLMs) inherit advanced planning capabilities; however, their direct interaction with the physical...
Yein Park, Jungwoo Park, Jaewoo Kang
Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes....
Xiang Zhang, Kun Wei, Xu Yang +3 more
As Large Language Models (LLMs) become increasingly prevalent, their security vulnerabilities have already drawn attention. Machine unlearning is...
Juyeop Kim, Songkuk Kim, Jong-Seok Lee
Despite their success in image generation, diffusion models can memorize training data, raising serious privacy and copyright concerns. Although...
Jing-Jing Li, Jianfeng He, Chao Shang +6 more
As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based...
Boyang Zhang, Istemi Ekin Akkus, Ruichuan Chen +4 more
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing and reasoning over diverse modalities, but their...
Jiacheng Shi, Hongfei Du, Y. Alicia Hong +1 more
Speech emotion recognition (SER) with audio-language models (ALMs) remains vulnerable to distribution shifts at test time, leading to performance...
Giuseppe Canale
This paper presents the Cybersecurity Psychology Framework (CPF), a novel methodology for quantifying human-centric vulnerabilities in security...
Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li +4 more
Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it...
Kunlun Zhu, Zijia Liu, Bingxuan Li +15 more
Large Language Model (LLM) agents, which integrate planning, memory, reflection, and tool-use modules, have shown promise in solving complex,...
Yanchen Jiang, Zhe Feng, Aranyak Mehta
Large language models (LLMs) are increasingly used in modern search and answer systems to synthesize multiple, sometimes conflicting, texts into a...
Aryan Yazdan Parast, Parsa Hosseini, Hesam Asadollahzadeh +4 more
Object hallucination in Multimodal Large Language Models (MLLMs) is a persistent failure mode that causes the model to perceive objects absent in the...
Ayda Aghaei Nia
Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs) are a foundational component of web security, yet traditional...
Dawei Li, Zhen Tan, Chengshuai Zhao +6 more
Large Language Model (LLM)-based judgments leverage powerful LLMs to efficiently evaluate candidate content and provide judgment scores. However, the...
Akio Hayakawa, Stefan Bott, Horacio Saggion
Despite their strong performance, large language models (LLMs) face challenges in real-world application of lexical simplification (LS), particularly...
Qianshan Wei, Tengchao Yang, Yaochen Wang +7 more
Large Language Model (LLM) agents use memory to learn from past interactions, enabling autonomous planning and decision-making in complex...
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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