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 41–60 of 109 papers
Clear filtersSarbartha Banerjee, Prateek Sahu, Anjo Vahldiek-Oberwagner +2 more
Rapid progress in generative AI has given rise to Compound AI systems - pipelines comprised of multiple large language models (LLM), software tools...
Xiangwen Wang, Ananth Balashankar, Varun Chandrasekaran
Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with...
Yu He, Haozhe Zhu, Yiming Li +4 more
LLM agents are highly vulnerable to Indirect Prompt Injection (IPI), where adversaries embed malicious directives in untrusted tool outputs to hijack...
Touseef Hasan, Blessing Airehenbuwa, Nitin Pundir +2 more
Large language models (LLMs) have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security...
Max Landauer, Wolfgang Hotwagner, Thorina Boenke +2 more
Log data are essential for intrusion detection and forensic investigations. However, manual log analysis is tedious due to high data volumes,...
Xiaoyi Pang, Xuanyi Hao, Pengyu Liu +3 more
Recent intelligent systems integrate powerful Large Language Models (LLMs) through APIs, but their trustworthiness may be critically undermined by...
Xinfeng Li, Shenyu Dai, Kelong Zheng +4 more
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However,...
Che Wang, Jiaming Zhang, Ziqi Zhang +6 more
The integration of external data services (e.g., Model Context Protocol, MCP) has made large language model-based agents increasingly powerful for...
Ian Steenstra, Paola Pedrelli, Weiyan Shi +2 more
Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the...
Xingyu Shen, Tommy Duong, Xiaodong An +6 more
Age estimation systems are increasingly deployed as gatekeepers for age-restricted online content, yet their robustness to cosmetic modifications has...
Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan +3 more
The increasing deployment of Federated Learning (FL) in Intrusion Detection Systems (IDS) introduces new challenges related to data privacy,...
Doron Shavit
Jailbreak prompts are a practical and evolving threat to large language models (LLMs), particularly in agentic systems that execute tools over...
Yuepeng Hu, Yuqi Jia, Mengyuan Li +2 more
In a malicious tool attack, an attacker uploads a malicious tool to a distribution platform; once a user installs the tool and the LLM agent selects...
Hayfa Dhabhi, Kashyap Thimmaraju
Large Language Models (LLMs) deploy safety mechanisms to prevent harmful outputs, yet these defenses remain vulnerable to adversarial prompts. While...
Xiaoxu Peng, Dong Zhou, Jianwen Zhang +3 more
Vision Language Models (VLMs) have advanced perception in autonomous driving (AD), but they remain vulnerable to adversarial threats. These risks...
Tianyi Wang, Huawei Fan, Yuanchao Shu +2 more
Large Language Models face an emerging and critical threat known as latency attacks. Because LLM inference is inherently expensive, even modest...
Zehua Cheng, Jianwei Yang, Wei Dai +1 more
Large Language Models (LLMs) remain vulnerable to adaptive jailbreaks that easily bypass empirical defenses like GCG. We propose a framework for...
Haoran Ou, Kangjie Chen, Gelei Deng +4 more
Fact-checking systems with search-enabled large language models (LLMs) have shown strong potential for verifying claims by dynamically retrieving...
Chanwoo Park, Chanwoo Kim
Evasion attacks pose significant threats to AI systems, exploiting vulnerabilities in machine learning models to bypass detection mechanisms. The...
Nirhoshan Sivaroopan, Kanchana Thilakarathna, Albert Zomaya +6 more
Sponge attacks increasingly threaten LLM systems by inducing excessive computation and DoS. Existing defenses either rely on statistical filters that...
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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