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 181–200 of 1,450 papers
Clear filtersMark Russinovich
Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that...
Lijie Zheng, Ji He, Ying Wang +2 more
Provenance-based intrusion detection systems (PIDSs) identify suspicious activity in audit streams, but their outputs remain difficult to turn into...
Rikhiya Ghosh, Himanshu Kumar, Sriram Venkatapathy +3 more
In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety...
Aadith Sukumar, Isha Singh, Devershika Mohane +4 more
Distributed Denial of Service attacks are a growing threat to network infrastructure, and new techniques, including the use of generative AI, make...
Jie Zhang, Andrei Baroian, Jan N. van Rijn +2 more
Large language models are vulnerable to prompt injection attacks, where third-party adversarial content can hijack the model's behavior. In this...
Sidharth Pulipaka, Ansh Sharma, Stanislau Hlebik +4 more
Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and...
Yan Zhan, Yunze Song, Mengkai Hou +3 more
Prompt injection against LLM agents becomes much stronger when the injected instruction is wrapped in the model's own chat template. A forged...
Xi Wang, Songlei Jian, Yiming Zhang +5 more
As large language models increasingly operate as tool-using agents, post-jailbreak safety feedback is often assumed to serve as a reliable safeguard;...
Xiao Yang, Yangchen Ou, Yuhan Gao +3 more
Large language model (LLM)-based agents increasingly rely on external tools and content, exposing them to indirect prompt injection (IPI). This...
Liang He, Sheng Wu, Haomiao Hao +3 more
Large language model (LLM) agents are increasingly evaluated on cybersecurity tasks such as vulnerability reproduction, exploitation, and patching....
Yiyong Liu, Jun Sakuma, Michael Backes +1 more
Training efficiency has become the central driver of recent progress in foundation models. To overcome the massive computational and data...
Sen Nie, Jie Zhang, Zhongqi Wang +2 more
Adversarial attacks have long posed a fundamental threat to machine learning systems. As multimodal large language models (MLLMs) rapidly evolve and...
Chenlong Yin, Xiaolong Jin, Wei Zou +2 more
Prompt injection is a leading security risk for LLMs and LLM-based applications such as agents. State-of-the-art red-teaming methods for prompt...
Lukáš Brůna, Robert Bridges, Adam Ek
Large Language Models (LLMs) consume and produce a single sequence of text; hence, if text can be added to the beginning of the LLM's response, i.e.,...
Karina Elzer, Niklas Netterstrøm Johansen, Emmanouil Vasilomanolakis
Publicly exposed large language model (LLM) infrastructure creates a growing attack surface, yet real-world targeting remains poorly understood. We...
Yu-Ling Liao, Tzu-Chin Chiu, Zong-You Chen +2 more
Large audio-language models (LALMs) expand language models to process and interpret audio, but also expose them to heterogeneous audio jailbreaks. We...
Siyuan Pang, Yepeng Yao, Zhengwei Jiang +2 more
The Node.js ecosystem heavily relies on NPM packages, and software supply chain attacks targeting malicious NPM packages are rampant. Malicious code...
Tiantong Wu, Wei Yang Bryan Lim
Most studies of prompt injection focus on generative agents, leaving their effects on models with schema-defined outputs unclear. We examine these...
Jinqian Zhang, Haojun Xia, Shujiang Wu +4 more
Multi-step tool-calling LLM agents rely on host runtimes to preserve state across turns. When a runtime carries an external tool return into later...
Djamel Eddine Hakim Ghorab, Farid Mokhati, Mostafa Anouar Ghorab
The growing adoption of blockchain technologies, particularly the Ethereum platform, has amplified the critical role of smart contracts in...
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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