AI Threat Alert indexes 3,397+ 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 841–860 of 1,668 papers
Clear filtersYanna Jiang, Delong Li, Haiyu Deng +4 more
Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably....
Longxiang Wang, Xiang Zheng, Xuhao Zhang +3 more
Multi-tenant LLM serving frameworks widely adopt shared Key-Value caches to enhance efficiency. However, this creates side-channel vulnerabilities...
Zac Garby, Andrew D. Gordon, David Sands
A conversation with a large language model (LLM) is a sequence of prompts and responses, with each response generated from the preceding...
Natalie Shapira, Chris Wendler, Avery Yen +35 more
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent...
Xunzhuo Liu, Huamin Chen, Samzong Lu +27 more
As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- selecting...
Yedi Zhang, Haoyu Wang, Xianglin Yang +2 more
LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task...
Kaiwen Wang, Xiaolin Chang, Yuehan Dong +1 more
Secure comparison is a fundamental primitive in multi-party computation, supporting privacy-preserving applications such as machine learning and data...
Lei Ba, Qinbin Li, Songze Li
LLM-based code interpreter agents are increasingly deployed in critical workflows, yet their robustness against risks introduced by their code...
Jingwei Shi, Xinxiang Yin, Jing Huang +2 more
The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existing...
Florin Adrian Chitan
The proliferation of autonomous AI agents capable of executing real-world actions - filesystem operations, API calls, database modifications,...
Kiarash Ahi, Vaibhav Agrawal, Saeed Valizadeh
Large Language Models (LLMs) & Generative AI are transforming cybersecurity, enabling both advanced defenses and new attacks. Organizations now use...
Emmanuel Bamidele
Long-running LLM agents require persistent memory to preserve state across interactions, yet most deployed systems manage memory with age-based...
Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday
Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during...
Chun Yan Ryan Kan, Tommy Tran, Vedant Yadav +4 more
Defending LLMs against adversarial jailbreak attacks remains an open challenge. Existing defenses rely on binary classifiers that fail when...
Diego Soi, Silvia Lucia Sanna, Lorenzo Pisu +2 more
In recent years, stealthy Android malware has increasingly adopted sophisticated techniques to bypass automatic detection mechanisms and harden...
Zachary Coalson, Bo Fang, Sanghyun Hong
Multi-turn interaction length is a dominant factor in the operational costs of conversational LLMs. In this work, we present a new failure mode in...
Gelei Deng, Yi Liu, Yuekang Li +5 more
LLM-based agents show promise for automating penetration testing, yet reported performance varies widely across systems and benchmarks. We analyze 28...
Boyang Ma, Hechuan Guo, Peizhuo Lv +5 more
Embodied AI systems (e.g., autonomous vehicles, service robots, and LLM-driven interactive agents) are rapidly transitioning from controlled...
Zachary Coalson, Beth Sohler, Aiden Gabriel +1 more
We identify a structural weakness in current large language model (LLM) alignment: modern refusal mechanisms are fail-open. While existing approaches...
Arnold Cartagena, Ariane Teixeira
Large language models deployed as agents increasingly interact with external systems through tool calls--actions with real-world consequences 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 3,397+ 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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