AI Threat Alert indexes 3,371+ 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 21–40 of 1,651 papers
Clear filtersRui Yang, Michael Fu, Kla Tantithamthavorn +2 more
Autonomous coding agents are increasingly embedded in enterprise software workflows with delegated authority over connected systems. Central to this...
Azizi Ariffin, Afif Haris, Faiz Zaki +2 more
Network intrusion detection systems (IDS) trained on fixed traffic snapshots decay silently after deployment as threat distributions shift....
Xuebin Li, Hanqing Zhao, Siyuan Liang +4 more
LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security...
Haoting Qian, Qingjie Zhang, Zhicong Huang +2 more
Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not....
Kazuya Horibe, Kenji Itao, Wataru Toyokawa
Can cooperation among large language model (LLM) agents be evolutionarily stable against free-rider invasion? We study an indirect reciprocity...
Zhaoqi Wang, Daqing He, Zijian Zhang +13 more
While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new...
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi +6 more
Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated...
Peichun Hua, Haoxuan Xu, Mengyuan Li
Closed source agent skills may encode proprietary instructions, scripts, constants, and data. Providers may offer their capabilities as services...
Manali Dangarikar, Cory Merkel
Deep neural networks (DNNs) deployed on resource-constrained neuromorphic hardware face three concurrent challenges: the need for model compression...
Narendra Kumar Dewangan, Mounira Msahli
Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator...
Zhijing Hu, Changjun Fan, Yufan Deng +1 more
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance...
Nizhang Li, Zonghao Ying, Xiangfan Wu +7 more
External skills extend the capabilities of large language model agents, but also introduce an execution-time attack surface: a skill that appears...
Yiming Chen, Kemou Li, Haiwei Wu +1 more
Backdoor attacks in multimodal contrastive learning (MCL) have garnered growing attention in recent years, as many downstream tasks critically depend...
Francis Heylighen
AI systems based on Large Language Models (LLMs) have prompted fears that they may harbor hidden goals, seek to dominate or eliminate humanity, or...
Jian Zhao, Shenao Wang, Qingyang Wu +3 more
The widespread adoption of open source software (OSS) has introduced significant security risks, with malicious code poisoning attacks increasingly...
Haocheng Fu, Yuqi Qian, Luyao Wang +1 more
Large language model (LLM) watermarking provides an important mechanism for tracing the provenance of generated text. Existing statistical watermarks...
Walid Saidi
Persistent agent memory must adapt as later outcomes change earlier evidence, yet mutable retrieval weights create an attribution problem: reviewers...
Giorgio Severi, Shujaat Mirza, Blake Bullwinkel +1 more
Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning...
Yiran Gao, Tao Li, Kim Hammar
Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making...
Vincenzo Longo, Alberto Verna, Nikhil Jha +1 more
The Microsoft 365 (M365) ecosystem hosts thousands of third-party applications that integrate with enterprise tenants via fine-grained OAuth...
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 3,371+ 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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AI Threat Alert