AI Threat Alert indexes 3,771+ 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 1–20 of 123 papers
Clear filtersMauro Conti, Lorenzo Perinello, Umberto Salviati
The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at...
Heewon Baek, Alsharif Abuadbba, Kristen Moore +2 more
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other...
Mengxiao Wang, Nitesh Saxena
Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely...
Guosen Wu, Huizhen Huang, Guoxiong Long +2 more
Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss...
Divyanshu Kumar, Rohith HN, Nitin Aravind Birur +2 more
AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare...
Victoria Lovelace, Cameron Berryman, Yuhan You +3 more
Large language model (LLM) agents are increasingly applied to penetration testing, but we still know little about what they can do or how they fail....
Sarah Meriem Ourari
Software supply chain security has become increasingly critical due to the widespread reliance on third-party dependencies and the growing attack...
Qikai Wang, Yongzhao Zhang, Zhiwei Chen +3 more
Skill selection is a key stage in LLM-agent workflows, determining which installed skill should handle a user request. Existing attacks on this stage...
Saastha Vasan, Hadjer Benkraouda, Jizhou Chen +8 more
Cyber Threat Intelligence (CTI) is essential for defending mission-critical infrastructure, yet the process of transforming raw attack evidence into...
Ziwei Zhao, Yu Gu, Haojun Liang +2 more
AI coding agents are evolving from solitary tools into collaborative teammates that discover and invoke one another's specialized skills. But the...
Rui Yang, Junjie Xu, Zhengyu Liu +4 more
Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating...
Feitong Qiao, Liren Peng, Shiming Ren +7 more
Frontier language models that refuse harmful single-turn prompts often comply when the same intent is reached gradually over many turns, making...
Xingbang He, Yuanwei Chen, Yi Qian +6 more
Instruction hierarchy is a model-side defense that assigns instructions different levels of privilege according to their sources. These levels...
Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari
Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning,...
Johann Knechtel, Ozgur Sinanoglu, Paul V. Gratz +1 more
The semiconductor industry is undergoing a dual revolution: the shift toward heterogeneous 2.5D chiplet systems and the integration of Large Language...
Walid Saidi
Persistent agent memory must adapt as later outcomes change earlier evidence, yet mutable retrieval weights create an attribution problem: reviewers...
Yuekun Wang, Mingfei Cheng, Xiaofei Xie
LLM-based code auditors are increasingly integrated into pull-request (PR) workflows, yet their reliability against adversarial changes distributed...
Jifeng Gao, Kang Xia, Yi Zhang +5 more
Persistent external memory enhances agent continuity but introduces persistent security vulnerabilities: adversarial content can be injected via...
Alexandra E. Michael, Franziska Roesner
As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination,...
Huihao Jing, Wenbin Hu, Shaojin Chen +10 more
The capability of LLM agents to function as the ``brain'' of a system fundamentally expands the scope of analysis beyond a standalone model....
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 3,771+ 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