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 1–20 of 247 papers
Clear filtersAlexander Panfilov, David Schmotz, Ilia Shumailov +5 more
Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and...
Puyu Zeng, Simeng Qin, Jingzhi Li +3 more
Agent skills are emerging as an important attack surface in LLM-based agent systems. Through an empirical study of existing skill scanners, we find...
Hongli Shen, Shaopeng Fu, Qinbo Zhang +2 more
Large reasoning models (LRMs) achieve remarkable success on complex tasks but remain vulnerable to harmful prompts that induce unsafe outputs. Recent...
Hanlin Jiang, Puyi Wang, Jiandong Jin +10 more
Real-world cyberattacks often require sustained progress across multiple hosts and network segments, making multi-hop cyber ranges essential...
Tejasvi C. Addagada
The key-value (KV) cache is the primary throughput optimization in modern large language model (LLM) inference, enabling prefix reuse across...
Mouhamed Amine Bouchiha, Gregory Blanc
Crowdsourced Federated Learning (CrowdFL) extends traditional federated learning by enabling open and heterogeneous participation through a...
Leo Sambrook, Sampo Sovio
AI agents performing cryptographic operations (signing Git commits, authenticating API calls, issuing certificates) currently store private keys in...
Jialuo Chen, Lingqi Jiang, Xinhao Deng +7 more
Self-evolving skill (SES) systems distill agent trajectories into persistent skills, allowing untrusted experience to become trusted instruction. We...
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....
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...
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...
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...
Giovanni Pizzenti, Alberto Verna, Nikhil Jha +3 more
In recent years, cybersecurity threats have increasingly exploited human behaviour rather than purely technical vulnerabilities, exposing the limits...
Jia-Chen Zhang, Ze-Yu Zhang, Kai-Wei Zhang
Computer-use agents (CUAs), which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to...
Qianlong Yang, Bowen Ye, Xianda Guo +4 more
Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual...
Alireza Lotfi, Subangkar Karmaker Shanto, Imtiaz Karim +1 more
Autonomous agents are increasingly used to execute consequential tasks in environments governed by operational constraints, organizational policies,...
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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