AI Threat Alert indexes 3,371+ 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 461–480 of 494 papers
Clear filtersVladimer Khasia
The pursuit of world model based artificial intelligence has predominantly relied on projecting high-dimensional observations into parameterized...
Nazanin Mohammadi Sepahvand, Eleni Triantafillou, Hugo Larochelle +3 more
Large language models (LLMs) trained on webscale data can produce toxic outputs, raising concerns for safe deployment. Prior defenses, based on...
Mohammed Cherifi
Public EV charging infrastructure suffers from significant failure rates -- with field studies reporting up to 27.5% of DC fast chargers...
Guangnian Wan, Qi Li, Gongfan Fang +2 more
Multimodal Diffusion Language Models (MDLMs) have recently emerged as a competitive alternative to their autoregressive counterparts. Yet their...
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...
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...
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...
Martin Bertran, Riccardo Fogliato, Zhiwei Steven Wu
Empirical conclusions depend not only on data but on analytic decisions made throughout the research process. Many-analyst studies have quantified...
Mirae Kim, Seonghun Jeong, Youngjun Kwak
Jailbreaking poses a significant risk to the deployment of Large Language Models (LLMs) and Vision Language Models (VLMs). VLMs are particularly...
Anna Babarczy, Andras Lukacs, Peter Vedres +1 more
The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infer...
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...
Takyoung Kim, Jinseok Nam, Chandrayee Basu +5 more
Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue...
Priyaranjan Pattnayak, Sanchari Chowdhuri
Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities...
Simon Lermen, Daniel Paleka, Joshua Swanson +3 more
We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News...
Stephan Rabanser, Sayash Kapoor, Peter Kirgis +3 more
AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many...
Michael Cunningham
We present a practical system for privacy-aware large language model (LLM) inference that splits a transformer between a trusted local GPU and an...
Nivya Talokar, Ayush K Tarun, Murari Mandal +2 more
LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to...
Johannes Bertram, Jonas Geiping
We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access security,...
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 3,371+ 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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert