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 681–700 of 748 papers
Clear filtersRagib Amin Nihal, Rui Wen, Kazuhiro Nakadai +1 more
Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints...
Muxi Diao, Yutao Mou, Keqing He +6 more
The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on...
Stanisław Pawlak, Jan Dubiński, Daniel Marczak +1 more
Model merging (MM) recently emerged as an effective method for combining large deep learning models. However, it poses significant security risks....
Kazuki Egashira, Robin Staab, Thibaud Gloaguen +2 more
Model pruning, i.e., removing a subset of model weights, has become a prominent approach to reducing the memory footprint of large language models...
Weisen Jiang, Sinno Jialin Pan
This paper introduces MetaDefense, a novel framework for defending against finetuning-based jailbreak attacks in large language models (LLMs). We...
Renhua Ding, Xiao Yang, Zhengwei Fang +3 more
Large vision-language models (LVLMs) enable autonomous mobile agents to operate smartphone user interfaces, yet vulnerabilities in their perception...
Christos Ziakas, Nicholas Loo, Nishita Jain +1 more
Automated red-teaming has emerged as a scalable approach for auditing Large Language Models (LLMs) prior to deployment, yet existing approaches lack...
Artur Horal, Daniel Pina, Henrique Paz +7 more
This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework,...
Sri Durga Sai Sowmya Kadali, Evangelos E. Papalexakis
Jailbreaking large language models (LLMs) has emerged as a pressing concern with the increasing prevalence and accessibility of conversational LLMs....
Giorgio Giannone, Guangxuan Xu, Nikhil Shivakumar Nayak +4 more
Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a...
Nouar Aldahoul, Yasir Zaki
The rapid spread of misinformation on digital platforms threatens public discourse, emotional stability, and decision-making. While prior work has...
Raffaele Mura, Giorgio Piras, Kamilė Lukošiūtė +3 more
Jailbreaks are adversarial attacks designed to bypass the built-in safety mechanisms of large language models. Automated jailbreaks typically...
Meng Tong, Yuntao Du, Kejiang Chen +2 more
Membership inference attacks (MIAs) are widely used to assess the privacy risks associated with machine learning models. However, when these attacks...
Xiaogeng Liu, Chaowei Xiao
Recent advancements in jailbreaking large language models (LLMs), such as AutoDAN-Turbo, have demonstrated the power of automated strategy discovery....
Weiliang Zhao, Jinjun Peng, Daniel Ben-Levi +2 more
The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving...
Kuofeng Gao, Yiming Li, Chao Du +4 more
Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are...
Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov +4 more
Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction...
Santhosh KumarRavindran
The rapid adoption of large language models (LLMs) in enterprise systems exposes vulnerabilities to prompt injection attacks, strategic deception,...
Buyun Liang, Liangzu Peng, Jinqi Luo +3 more
Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising...
Yu Cui, Sicheng Pan, Yifei Liu +2 more
Large language models (LLMs) have been widely deployed in Conversational AIs (CAIs), while exposing privacy and security threats. Recent research...
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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