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 201–220 of 264 papers
Clear filtersRongzhe Wei, Peizhi Niu, Xinjie Shen +7 more
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches...
Henry Onyeka, Emmanuel Samson, Liang Hong +3 more
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated...
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5 more
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a...
Junbo Zhang, Ran Chen, Qianli Zhou +2 more
Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety...
Onat Gungor, Roshan Sood, Jiasheng Zhou +1 more
Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their...
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1 more
The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant...
Swastik Bhattacharya, Sanjay Das, Anand Menon +3 more
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these...
Samih Fadli
Large language model safety is usually assessed with static benchmarks, but key failures are dynamic: value drift under distribution shift, jailbreak...
Zhaoxin Zhang, Borui Chen, Yiming Hu +3 more
Recent research on large language model (LLM) jailbreaks has primarily focused on techniques that bypass safety mechanisms to elicit overtly harmful...
Zheyu Lin, Jirui Yang, Yukui Qiu +3 more
Evaluating the safety robustness of LLMs is critical for their deployment. However, mainstream Red Teaming methods rely on online generation and...
Quoc Viet Vo, Tashreque M. Haq, Paul Montague +3 more
Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better...
JoonHo Lee, HyeonMin Cho, Jaewoong Yun +3 more
We present SGuard-v1, a lightweight safety guardrail for Large Language Models (LLMs), which comprises two specialized models to detect harmful...
Thong Bach, Dung Nguyen, Thao Minh Le +1 more
Large language models exhibit systematic vulnerabilities to adversarial attacks despite extensive safety alignment. We provide a mechanistic analysis...
Ruoxi Cheng, Haoxuan Ma, Teng Ma +1 more
Large Vision-Language Models (LVLMs) exhibit powerful reasoning capabilities but suffer sophisticated jailbreak vulnerabilities. Fundamentally,...
Jialin Wu, Kecen Li, Zhicong Huang +3 more
Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code...
Daniyal Ganiuly, Nurzhau Bolatbek
The increasing virtualization of fifth generation (5G) networks expands the attack surface of the user plane, making spoofing a persistent threat to...
Binayak Kara, Ujjwal Sahua, Ciza Thomas +1 more
Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a...
Tyler Slater
Context: The integration of Large Language Models (LLMs) into core software systems is accelerating. However, existing software architecture patterns...
Haonan Shi, Guoli Wang, Tu Ouyang +1 more
Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow...
Oshando Johnson, Alexandra Fomina, Ranjith Krishnamurthy +3 more
The prevalence of security vulnerabilities has prompted companies to adopt static application security testing (SAST) tools for vulnerability...
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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AI Threat Alert