AI Threat Alert indexes 3,795+ 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 2061–2080 of 3,795 papers
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...
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...
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...
Jongwon Jeong, Jungtaek Kim, Kangwook Lee
Language Model (LM) agents have demonstrated remarkable capabilities in solving tasks that require multiple interactions with the environment....
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...
Nadav Kadvil, Malak Fares, Ayellet Tal
Large Vision-Language Models (LVLMs) can be vulnerable to adversarial images that subtly bias their outputs toward plausible yet incorrect responses....
Xiaochong Jiang, Shiqi Yang, Wenting Yang +2 more
Agentic systems built on large language models (LLMs) extend beyond text generation to autonomously retrieve information and invoke tools. This...
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...
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...
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...
Kunal Mukherjee
Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating system,...
Amirhossein Farzam, Majid Behabahani, Mani Malek +2 more
Large language models (LLMs) remain vulnerable to jailbreak prompts that are fluent and semantically coherent, and therefore difficult to detect with...
Charles Ye, Jasmine Cui, Dylan Hadfield-Menell
Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models...
Charles Ye, Jasmine Cui, Dylan Hadfield-Menell
Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models...
Florin Adrian Chitan
The proliferation of autonomous AI agents capable of executing real-world actions - filesystem operations, API calls, database modifications,...
Sieun Kim, Yeeun Jo, Sungmin Na +5 more
Red-teaming, where adversarial prompts are crafted to expose harmful behaviors and assess risks, offers a dynamic approach to surfacing underlying...
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...
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 3,795+ 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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