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 2061–2080 of 4,319 papers
Zhaofen Wu, Hanrong Zhang, Fulin Lin +9 more
To sustain coherent long-term interactions, Large Language Model (LLM) agents must navigate the tension between acquiring new information and...
Yulin Chen, Tri Cao, Haoran Li +7 more
Web agents powered by vision-language models (VLMs) enable autonomous interaction with web environments by perceiving and acting on both visual and...
Qingchao Shen, Zibo Xiao, Lili Huang +3 more
Large Language Models (LLMs) are increasingly deployed across diverse domains, yet their vulnerability to jailbreak attacks, where adversarial inputs...
Pei-Yu Tseng, Lan Zhang, ZihDwo Yeh +3 more
Cyber Threat Intelligence (CTI) reports contain Indicators of Compromise (IOCs) that are critical for security operations. To operationalize these...
Shangkun Che, Silin Du, Ge Gao
The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes....
Hongru Song, Yu-An Liu, Ruqing Zhang +4 more
Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack...
Dominik Blain
We present COBALT-TLA, a neuro-symbolic verification loop that pairs an LLM with TLC, the TLA+ model checker, in an automated REPL. The LLM generates...
Anes Abdennebi, Nadjia Kara, Laaziz Lahlou
The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance,...
Willy Carlos Tchuitcheu, Tan Lu, Ann Dooms
Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We...
Georgianna, Lin, Rencong Jiang +2 more
Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management,...
Gamze Kirman Tokgoz, Onat Gungor, Tajana Rosing +1 more
Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of...
Adam Stein, Davis Brown, Hamed Hassani +2 more
To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare,...
Wei Zhao, Zhe Li, Peixin Zhang +1 more
Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet...
Ricardo Bessa, Rui Claro, João Trindade +1 more
Large Language Models (LLMs) are redefining offensive cybersecurity by allowing the generation of harmful machine code with minimal human...
Javad M Alizadeh, Genhui Zheng, Chiu C Tan +7 more
People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG...
Junxiao Yang, Haoran Liu, Jinzhe Tu +9 more
Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried...
Ningyan Zhu, Huacan Wang, Jie Zhou +8 more
The rise of OpenClaw in early 2026 marks the moment when millions of users began deploying personal AI agents into their daily lives, delegating...
Hanbo Huang, Xuan Gong, Yiran Zhang +2 more
Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to...
Jinhua Wang, Biswa Sengupta
Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We...
Ricardo Bessa, Rui Claro, João Trindade +1 more
The application of Machine Learning techniques in code generation is now a common practice for most developers. Tools such as ChatGPT from OpenAI...
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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