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 1901–1920 of 1,984 papers
Clear filtersShidong Cao, Hongzhan Lin, Yuxuan Gu +2 more
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias...
Di Wu, Yanyan Zhao, Xin Lu +2 more
Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve...
Rasmus Erlemann, Charles Colyer Morris, Sanjyot Sathe
The emergence of large-scale quantum computing threatens widely deployed public-key cryptographic systems, creating an urgent need for...
Zhakshylyk Nurlanov, Frank R. Schmidt, Florian Bernard
As Large Language Models (LLMs) are increasingly deployed in safety-critical domains, rigorously evaluating their robustness against adversarial...
Xiangdong Hu, Yangyang Jiang, Qin Hu +1 more
Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous...
Sofie Goethals, Foster Provost, João Sedoc
As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their...
Xiangzhe Yuan, Zhenhao Zhang, Haoming Tang +1 more
As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that...
Nick Pepper, Adam Keane, Amy Hodgkin +11 more
This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital...
Bocheng Chen, Xi Chen, Han Zi +5 more
Identifying specific moral errors in an input and generating appropriate corrections require moral sensitivity in large language models (LLMs), which...
Xi Wang, Songlei Jian, Shasha Li +5 more
Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a...
Chengcheng Feng, Haojie Yin, Yucheng Jin +1 more
Comic-based visual question answering (CVQA) poses distinct challenges to multimodal large language models (MLLMs) due to its reliance on symbolic...
Yuetian Chen, Yuntao Du, Kaiyuan Zhang +4 more
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data....
Gaurav Sarraf, Vibhor Pal
Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious...
Dinghong Song, Zhiwei Xu, Hai Wan +3 more
Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe...
Gongao Zhang, Haijiang Zeng, Lu Jiang
Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic...
Scott Thornton
Large language models remain vulnerable to jailbreak attacks, and single-layer defenses often trade security for usability. We present TRYLOCK, the...
Ahmed Ahmed, A. Feder Cooper, Sanmi Koyejo +1 more
Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's...
Devang Kulshreshtha, Hang Su, Chinmay Hegde +1 more
Most jailbreak methods achieve high attack success rates (ASR) but require attacker LLMs to craft adversarial queries and/or demand high query...
Md Ajoad Hasan, Dipayan Saha, Khan Thamid Hasan +5 more
The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental steps...
Neusha Javidnia, Ruisi Zhang, Ashish Kundu +1 more
We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLM owners by...
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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