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 201–220 of 4,319 papers
Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker
Neural receivers for 5G New Radio (NR) match classical model-based receivers in link-level performance, but their robustness to adversarial...
Xiangyu Zhang, Yucheng Su, Lingling Fan +2 more
Modern Android apps extensively rely on native libraries, bridging Java/Kotlin and C/C++ through the Java Native Interface (JNI). This cross-language...
Yahao Ren, Jian Lou, Huadi Zheng +1 more
Instruction-data selectors choose which instruction-following records are used for fine-tuning, improving alignment quality, raising training...
Frank Breitinger, Jan Gruber, Christopher Hargreaves +3 more
Large language models (LLMs) are entering digital forensics as examination assistants and autonomous agents. Digital forensics inverts the usual...
Yiheng Li, Jingyue Li
With the rapid integration of Multi-modal Large Language Models (MLLMs) into real-world applications, Prompt Injection (PI) has emerged as a critical...
Qingyue Wang, Yudong Gao, Lixia Wang +4 more
Provable robustness is a cornerstone of trustworthy machine learning, aiming to guarantee model predictions under adversarial perturbations. Existing...
Gadi Evron
LLM-based vulnerability discovery harnesses can miss real, reachable vulnerabilities because of how the harness around the model is built. Steps that...
Zixuan Yu, Zhenheng Tang, Xiaowen Chu +1 more
As Large Language Models (LLMs) evolve into autonomous Deep Search and Deep Research Agents capable of executing long-horizon, multi-step tasks,...
Hanhui Xu, Jiacheng Ji, Yuhan Wang +3 more
Large language models increasingly address users in the register of care: they remember personal details, adopt a first-person emotional voice, and...
Hongyu Li, Xinlong Huang, Zhan Yu +13 more
Current LLM safety benchmarks predominantly rely on binary safe or unsafe labels, lacking explanatory depth and suffering from low consistency across...
Eric Liang, Alan Luo, Kaiwen Yuan
Recent advancements in Vision-Language Models (VLMs) enable end-to-end visual grounding via language prompts and generative decoding, demonstrating...
Luca Zanolini
Before an AI agent takes a consequential action, such as paying, messaging or changing code, several monitors check it. Their verdicts are combined...
Guoqiang Hua, Sakshi Udeshi, Matheus Eduardo Garbelini +2 more
In this paper, we present 5uGUE, a large language model (LLM)-driven hardware-in-the-loop agent, which enables the wireless fuzzing of 5G-enabled...
Muxuan He, Wang Bill Zhu, Yue Chen +5 more
Although large language models (LLMs) encode language in highly distributed spaces, they remain vulnerable to focal disruptions---a phenomenon...
Amelia Kawasaki, Stefan Lee
Indirect prompt injection (IPI) attacks, in which adversarial instructions are embedded in external data processed by a language model, pose a...
Tanqiu Jiang, Min Bai, Sadat Shahriar +2 more
Persistent memory is a largely undefended surface for injection and poisoning, and the same risk limits memory sharing among agents. We present...
Yuqi Jia, Hangsheng Zhang, Chenxi Liu +2 more
Large language model (LLM) agents are increasingly deployed in multi-agent systems (MAS) to accomplish complex tasks. Prior black-box red-teaming...
Pedro H. Barcha Correia, Matheus Azevedo de Sá, Paulo H. Gomes de Senna +4 more
The widespread adoption of generative artificial intelligence in recent years has brought a parallel rise in prompt injection and jailbreak attacks....
Carlo Ciliberto, Alessandro Rudi, Enrico Tassi
AI agents increasingly act on behalf of people or other agents, purchasing services, exchanging information and delegating work. What an agent must...
Vita Santa Barletta, Danilo Caivano, Gabriel Cellammare +3 more
The increasing complexity of Industrial Control Systems (ICS) and their integration with IT infrastructures has significantly expanded the cyber...
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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