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 81–100 of 562 papers
Clear filtersVincent Koc, Patrick Erichsen, Jacob Tomlinson +3 more
Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from...
Abdelrahman Abouelenein, Marwan Torki
It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising...
Michael J. Bommarito
The attack surface of a modern operating system is a haystack: thousands of signed binaries and millions of functions, almost none relevant to any...
Victor Akinode, Senyu Li, Wassim Hamidouche +3 more
Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones,...
Haichuan Hu, Guoqing Xie, Quanjun Zhang +5 more
Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack...
Tanmoy Chakraborty, Ayan Sengupta, Suparna Bhattacharya +7 more
Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their...
Mark Vero, Fabian Kaczmarczyck, Ivan Petrov +6 more
Honeypots are decoy systems mimicking real system components designed to defend against cyber attacks. Recently, LLMs increasingly serve as...
Shizhe Zhou, Bohan Jia, Kai Wu +4 more
While multimodal large language models (MLLMs) have achieved rapid progress in vision-language understanding, they remain prone to multimodal...
Taojie Zhu, Wentao Zhao, Rui Sun +7 more
Evaluating whether large language model (LLM) agents can profit in capital markets is increasingly framed as end-to-end trading: place an agent in a...
Víctor Mayoral-Vilches
We present CAI Dataset, a fourteen-month corpus of cybersecurity LLM trajectories collected through the open-source CAI agent framework, built in...
Yujie Ma, Jialin Rong, Chenxi Yang +4 more
Large Language Models(LLMs) have been actively integrated into modern software systems as critical components. LLM-in-the-loop vulnerabilities, where...
Yongwoo Kim, Sojung An, Yunjin Park +8 more
Multimodal Large Language Models (MLLMs) exacerbate safety risks by introducing vulnerabilities across multiple modalities, such as language and...
Xuesi Hu, Peng Wang, Jinpeng Miao +7 more
Recently, large language models (LLMs) have achieved superior performance in static financial reasoning and simple dynamic trading tasks. However,...
Akshaj Murhekar, Abhijit Mishra
Recent advances in large language models have accelerated open-vocabulary EEG-to-imagined-text decoding, where non-invasive neural activity recorded...
Cihan Xiao, Yiwen Shao, Chenxing Li +5 more
Audio and omni-modal large language models exhibit impressive cross-modal reasoning capabilities. However, applying standard reinforcement learning...
Syed Huma Shah
Modern retrieval-augmented generation(RAG) deployments increasingly rely on caching to reduce token cost and time-to-first-token(TTFT). Prefix-level...
Akindoyin Akinrele, Shreyank N Gowda
Prompt injection poses a critical threat to the safe deployment of large language models, yet existing detection approaches are typically evaluated...
Wajdi Zaghouani, Kholoud K. Aldous, Isra Fejzullaj
Safety evaluation of Large Language Models (LLMs) has largely focused on high-resource languages, leaving low-resource languages critically...
Anas H. Alzahrani
Background: Large language models are typically evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens...
Haodong Zhao, Tianyi Xu, Tianhang Zhao +2 more
Fine-tuning Large Language Models with untrusted data exposes models to backdoor attacks, where poisoned samples cause targeted misbehavior. Existing...
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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