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 1961–1980 of 4,319 papers
Jialiang Wang, Yuchen Liu, Hang Xu +7 more
The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At...
Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny +3 more
Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs...
Naheed Rayhan, Sohely Jahan
Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This...
Zihan Wang, Rui Zhang, Yu Liu +4 more
LLM agents increasingly rely on skills to encapsulate reusable capabilities via progressively disclosed instructions. High-quality skills inject...
Jiali Wei, Ming Fan, Guoheng Sun +3 more
The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent...
Run Hao, Zhuoran Tan
Model Context Protocol (MCP) is increasingly adopted for tool-integrated LLM agents, but its multi-layer design and third-party server ecosystem...
Arthur Douillard, Keith Rush, Yani Donchev +14 more
Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling...
Yuchen Shi, Xin Guo, Huajie Chen +3 more
Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to...
Vishal Rajput
We prove that empirical risk minimisation (ERM) imposes a necessary geometric constraint on learned representations: any encoder that minimises...
Yongcan Yu, Lingxiao He, Jian Liang +5 more
Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization...
Zhaohui Geoffrey Wang
Automated code vulnerability detection is critical for software security, yet existing approaches face a fundamental trade-off between detection...
Guilin Deng, Silong Chen, Yuchuan Luo +6 more
Federated Large Language Models (FedLLMs) enable multiple parties to collaboratively fine-tune LLMs without sharing raw data, addressing challenges...
Jesse Zymet, Andy Luo, Swapnil Shinde +2 more
Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with...
Irti Haq, Belén Saldías
As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical....
Ari Azarafrooz
AI-agent guardrails are memoryless: each message is judged in isolation, so an adversary who spreads a single attack across dozens of sessions slips...
Mohammad Farhad, Shuvalaxmi Dass
Software security relies on effective vulnerability detection and patching, yet determining whether a patch fully eliminates risk remains an...
Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis +2 more
The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the...
Mikko Lempinen, Joni Kemppainen, Niklas Raesalmi
As artificial intelligence (AI) systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of...
Hanzhi Liu, Chaofan Shou, Xiaonan Liu +4 more
LLM agents have begun to find real security vulnerabilities that human auditors and automated fuzzers missed for decades, in source-available targets...
Krishiv Agarwal, Ramneet Kaur, Colin Samplawski +6 more
Effective safety auditing of large language models (LLMs) demands tools that go beyond black-box probing and systematically uncover vulnerabilities...
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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