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 401–420 of 521 papers
Clear filtersWei Zhao, Zhe Li, Yige Li +1 more
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in cross-modal understanding, but remain vulnerable to adversarial...
Jingzhuo Zhou
The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing...
Saeefa Rubaiyet Nowmi, Jesus Lopez, Md Mahmudul Alam Imon +2 more
Quantum Machine Learning (QML) integrates quantum computational principles into learning algorithms, offering improved representational capacity and...
W. Bradley Knox, Katie Bradford, Samanta Varela Castro +6 more
Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to...
Abolfazl Younesi, Leon Kiss, Zahra Najafabadi Samani +2 more
Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who...
Hongwei Liu, Junnan Liu, Shudong Liu +33 more
The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to...
Yuyang Xia, Ruixuan Liu, Li Xiong
Large language models (LLMs) perform in-context learning (ICL) by adapting to tasks from prompt demonstrations, which in practice often contain...
Longfei Chen, Ruibin Yan, Taiyu Wong +2 more
Smart contracts are prone to vulnerabilities and are analyzed by experts as well as automated systems, such as static analysis and AI-assisted...
Minjie Wang, Jinguang Han, Weizhi Meng
In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage...
Shanmin Wang, Dongdong Zhao
Knowledge Distillation (KD) is essential for compressing large models, yet relying on pre-trained "teacher" models downloaded from third-party...
Yanbo Dai, Zongjie Li, Zhenlan Ji +1 more
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, demonstrating human-level...
Zichao Wei, Jun Zeng, Ming Wen +8 more
Software vulnerabilities are increasing at an alarming rate. However, manual patching is both time-consuming and resource-intensive, while existing...
Feilong Wang, Fuqiang Liu
The integration of large language models (LLMs) into automated driving systems has opened new possibilities for reasoning and decision-making by...
Guangke Chen, Yuhui Wang, Shouling Ji +2 more
Modern text-to-speech (TTS) systems, particularly those built on Large Audio-Language Models (LALMs), generate high-fidelity speech that faithfully...
Fred Heiding, Simon Lermen
We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to...
Catherine Xia, Manar H. Alalfi
AI programming assistants have demonstrated a tendency to generate code containing basic security vulnerabilities. While developers are ultimately...
Zexu Wang, Jiachi Chen, Zewei Lin +7 more
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract...
Yunfei Yang, Xiaojun Chen, Yuexin Xuan +3 more
Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific...
Kazuki Iwahana, Yusuke Yamasaki, Akira Ito +2 more
Backdoor attacks pose a critical threat to machine learning models, causing them to behave normally on clean data but misclassify poisoned data into...
Junxiao Han, Zheng Yu, Lingfeng Bao +5 more
The widespread adoption of open-source software (OSS) has accelerated software innovation but also increased security risks due to the rapid...
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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