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 121–140 of 1,229 papers
Clear filtersBoyue Caroline Hu, Kaivalya Ahir, Ronghao Ni +1 more
Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient;...
Aniruddh Pramod, James Oldfield, Adel Bibi
LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful...
Seungmin Oh, Seunghun Kang, Jongbin Ryu
Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time...
Xunguang Wang, Qingyue Wang, Yuguang Zhou +3 more
Embodied agents powered by large language models and vision-language models are increasingly deployed in physical environments, but jailbreak attacks...
Yao Lu, Zhaiyuan Ji, Yaxin Gao +6 more
With the rapid development of artificial intelligence, the emergence of various Large Language Models (LLMs) has created a rich model ecosystem....
Shovan Roy, Lopamudra Praharaj, Maanak Gupta +1 more
Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces...
Jianing Wen, Tianshi Li
As Large Language Models (LLMs) gain tool use capabilities such as web search, they can retrieve and cross-reference public information, creating...
Chaeeun Han, Soodeh Atefi, Yevgeniy Vorobeychik +1 more
As machine learning increasingly relies on public, untrusted data sources, data poisoning attacks, which inject malicious examples into training data...
Alessandro Pegoraro, Daryan Merx, Phillip Rieger +1 more
With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems,...
Amit Singh Bhatti, Vishal Vaddina
Safety routers send each request to one of several models and are judged against the best single model. A major routing benchmark picks that...
Jianhong Li, Jiahao Chen, Yuwen Pu +6 more
Beyond adapting Large Language Models (LLMs) to specialized applications, fine-tuning has recently been shown to recover private information that is...
Ryota Mibayashi, Hiroaki Ohshima
Large language models (LLMs) have achieved strong performance across various natural language processing tasks. However, their robustness to...
Laziz U. Abdullaev, Minh-Hieu Pham, Bach Do +2 more
Activation steering provides a simple, training-free mechanism for controlling attributes of generative models such as sentiment, style, and...
Shuze Liu, Kaixiang Zhao, Runyang Xu +4 more
Large language models (LLMs) deployed through text-only APIs face model extraction risks, as adversaries can collect their responses to train...
Jianwei Li, Jung-Eun Kim
With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become...
Jianwei Li, Min-Seon Kim, Jung-Eun Kim
Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers....
Ayan Javeed Shaikh, Arunesh Sinha, Nathaniel D. Bastian +1 more
Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement...
Yi Song, Dongchen Xie, Xiaoyuan Xie +4 more
Modern software heavily relies on code reuse, so upstream vulnerability fixes do not automatically propagate to downstream codebases. Downstream...
Minghan Wang, Boyuan Wang, Jinhang Zuo +2 more
Agent harnesses often improve language models with human-designed workflows, but as models grow more capable, unreliable guidance can increasingly...
Yuelin Han
Agent interaction protocols such as ACP and A2A have moved LLM-based agents toward multi-agent collaboration, introducing new security threats. A...
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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