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 1481–1500 of 1,651 papers
Clear filtersJie Zhang, Meng Ding, Yang Liu +2 more
We present a novel approach for attacking black-box large language models (LLMs) by exploiting their ability to express confidence in natural...
Asmita Mohanty, Gezheng Kang, Lei Gao +1 more
Large Language Models (LLMs) have demonstrated strong performance across diverse tasks, but fine-tuning them typically relies on cloud-based,...
Shivam Ratnakar, Sanjay Raghavendra
Integration of Large Language Models with search/retrieval engines has become ubiquitous, yet these systems harbor a critical vulnerability that...
Xiaofan Li, Xing Gao
The Model Context Protocol (MCP) is an emerging open standard that enables AI-powered applications to interact with external tools through structured...
David Peer, Sebastian Stabinger
Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent...
Shuai Li, Kejiang Chen, Jun Jiang +5 more
Large Language Models (LLMs) have demonstrated remarkable capabilities, but their training requires extensive data and computational resources,...
Sarah Egler, John Schulman, Nicholas Carlini
Large Language Model (LLM) providers expose fine-tuning APIs that let end users fine-tune their frontier LLMs. Unfortunately, it has been shown that...
Yang Feng, Xudong Pan
Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language...
Eduard Andrei Cristea, Petter Molnes, Jingyue Li
Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools...
Yuexiao Liu, Lijun Li, Xingjun Wang +1 more
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have gained significant attention due to their objective and verifiable...
Hanbin Hong, Shuya Feng, Nima Naderloui +6 more
Large Language Models (LLMs) have rapidly become integral to real-world applications, powering services across diverse sectors. However, their...
Ahmed Aly, Essam Mansour, Amr Youssef
Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these...
Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1 more
As Large Language Models (LLMs) gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern....
Andrew Zhao, Reshmi Ghosh, Vitor Carvalho +4 more
Large language model (LLM) systems increasingly power everyday AI applications such as chatbots, computer-use assistants, and autonomous robots,...
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3 more
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires...
Fanchao Meng, Jiaping Gui, Yunbo Li +1 more
Modern Network Intrusion Detection Systems generate vast volumes of low-level alerts, yet these outputs remain semantically fragmented, requiring...
Edoardo Allegrini, Ananth Shreekumar, Z. Berkay Celik
Agentic AI systems, which leverage multiple autonomous agents and Large Language Models (LLMs), are increasingly used to address complex, multi-step...
Jianzhu Yao, Hongxu Su, Taobo Liao +4 more
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little...
Qiushi Wu, Yue Xiao, Dhilung Kirat +3 more
Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project...
Yibo Peng, James Song, Lei Li +6 more
Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively...
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