AI Threat Alert indexes 3,023+ 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 341–360 of 946 papers
Clear filtersAri 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...
Mikko Lempinen, Joni Kemppainen, Niklas Raesalmi
As artificial intelligence (AI) systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of...
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....
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...
He Yang Yuan, Xin Wang, Kundi Yao +3 more
Logging code plays an important role in software systems by recording key events and behaviors, which are essential for debugging and monitoring....
Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan +1 more
The rapid advancement of Audio Large Language Models (ALMs), driven by Neural Audio Codecs (NACs), has led to the emergence of highly realistic...
Robert Stanley, Avi Verma, Lillian Tsai +2 more
AI agents promise to serve as general-purpose personal assistants for their users, which requires them to have access to private user data (e.g.,...
Alankrit Chona, Igor Kozlov, Ambuj Kumar
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of...
Alankrit Chona, Igor Kozlov, Ambuj Kumar
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of...
Divyesh Gabbireddy, Suman Saha
Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious...
Sarang Nambiar, Dhruv Pradhan, Ezekiel Soremekun
Pre-trained machine learning models (PTMs) are commonly provided via Model Hubs (e.g., Hugging Face) in standard formats like Pickles to facilitate...
Ali Al-Kaswan, Maksim Plotnikov, Maxim Hájek +3 more
Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive...
Kun Wang, Cheng Qian, Miao Yu +6 more
Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is...
Hugo Lyons Keenan, Christopher Leckie, Sarah Erfani
We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was...
Ahson Saiyed, Sabrina Sadiekh, Chirag Agarwal
Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse...
Ruixuan Liu, David Evans, Li Xiong
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to...
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 3,023+ 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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