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 141–160 of 176 papers
Clear filtersJaehwan Park, Kyungchan Lim, Seonhye Park +1 more
The advent of Artificial Intelligence (AI), particularly large language models (LLMs), has revolutionized software development by enabling developers...
Jiangrui Zheng, Yingming Zhou, Ali Abdullah Ahmad +2 more
Bug bounty platforms (e.g., HackerOne, BugCrowd) leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of...
Strahinja Janjusevic, Anna Baron Garcia, Sohrob Kazerounian
Generative AI is reshaping offensive cybersecurity by enabling autonomous red team agents that can plan, execute, and adapt during penetration tests....
Zimo Ji, Xunguang Wang, Zongjie Li +6 more
Large Language Model (LLM)-based agents with function-calling capabilities are increasingly deployed, but remain vulnerable to Indirect Prompt...
Gioliano de Oliveira Braga, Pedro Henrique dos Santos Rocha, Rafael Pimenta de Mattos Paixão +3 more
Wi-Fi Channel State Information (CSI) has been repeatedly proposed as a biometric modality, often with reports of high accuracy and operational...
Shaowei Guan, Hin Chi Kwok, Ngai Fong Law +3 more
Retrieval-augmented generation (RAG) has rapidly emerged as a transformative approach for integrating large language models into clinical and...
James Jin Kang, Dang Bui, Thanh Pham +1 more
The growing use of large language models in sensitive domains has exposed a critical weakness: the inability to ensure that private information can...
Gabrielle M Gauthier, Eesha Ali, Amna Asim +2 more
Human content moderators (CMs) routinely review distressing digital content at scale. Beyond exposure, the work context (e.g., workload, team...
Gian Maria Campedelli
While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized...
Yuntao Wang, Siamak P. Najad-Davarani, Elizabeth Bossart +3 more
Background: Modern large language models (LLMs) offer powerful reasoning that converts narratives into structured, taxonomy-aligned data, revealing...
Qin Zhou, Zhexin Zhang, Zhi Li +1 more
With the rapid advancement of AI models, their deployment across diverse tasks has become increasingly widespread. A notable emerging application is...
Kathrin Grosse, Nico Ebert
Recent improvement gains in large language models (LLMs) have lead to everyday usage of AI-based Conversational Agents (CAs). At the same time, LLMs...
Dominik Schwarz
As Large Language Models (LLMs) are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust...
Robert A. Bridges, Thomas R. Mitchell, Mauricio Muñoz +1 more
The advent of Large Language Models (LLMs) promised to resolve the long-standing paradox in honeypot design, achieving high-fidelity deception with...
Xinwei Wu, Heng Liu, Jiang Zhou +5 more
Large Language Models (LLMs) have advanced machine translation but remain vulnerable to hallucinations. Unfortunately, existing MT benchmarks are not...
Bin Wang, Zexin Liu, Hao Yu +6 more
The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and...
Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu +5 more
Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical...
Wu Yichao, Wang Yirui, Ding Panpan +3 more
With the wide application of deep reinforcement learning (DRL) techniques in complex fields such as autonomous driving, intelligent manufacturing,...
Wm. Matthew Kennedy, Cigdem Patlak, Jayraj Dave +10 more
AI systems have the potential to produce both benefits and harms, but without rigorous and ongoing adversarial evaluation, AI actors will struggle to...
Oleksandr Adamov, Anders Carlsson
This paper explores the challenges of cyberattack attribution, specifically APTs, applying the case study approach for the WhisperGate cyber...
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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