AI Threat Alert indexes 3,092+ 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 741–760 of 3,092 papers
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...
Hoang Nguyen, Lu Wang, Marta Gaia Bras
Freight brokerages negotiate thousands of carrier rates daily under dynamic pricing conditions where models frequently revise targets...
Abhijit Talluri
Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected...
Yuhang Wu, Qinyuan Liu, Qiuyang Zhao +1 more
Currently, Large Language Models (LLMs) feature a diversified architectural landscape, including traditional Transformer, GateDeltaNet, and Mamba....
Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2 more
Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address...
Pranav Pallerla, Wilson Naik Bhukya, Bharath Vemula +1 more
Retrieval-augmented generation (RAG) systems are increasingly deployed in sensitive domains such as healthcare and law, where they rely on private,...
Yingyong Hou, Xinyuan Lao, Huimei Wang +10 more
Background: Agent skills are increasingly deployed as modular, reusable capability units in AI agent systems. Medical research agent skills require...
Chao Pan, Yu Wu, Xin Yao
Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion...
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 3,092+ 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