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 1–20 of 62 papers
Clear filtersJohann 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....
Lea Roxanne Muth, Marian Margraf
In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk...
Kiarash Ahi, Saeed Valizadeh
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI,...
Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar +5 more
Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical,...
Yuanai Xie, Jiaxin Chen, Zhaozhi Liu
Large language model (LLM)-enabled edge networks (LLMENs) offer mobile users high-quality and low-latency AI-generated content services in the 6G...
Buğra Alperen Uluırmak, Rifat Kurban
LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can improve while...
Nada Lahjouji, Ashwin Gerard Colaco
Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a...
Jintao Huang, Fengqing Jiang, Radha Poovendran +1 more
We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability...
Thi Huyen Nguyen, Zahra Ahmadi
The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large...
Annika Marie Schoene, Cansu Canca, Gautham Vijay Kumar +1 more
General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate...
Junquan Deng, Zhiyu Fan, Ruijie Meng
Recent advances in large language models (LLMs) have enabled vibe coding, an emerging software development paradigm in which users create...
Junquan Deng, Zhiyu Fan, Ruijie Meng
Recent advances in large language models (LLMs) have enabled vibe coding, an emerging software development paradigm in which users create...
Nafiseh Kahani, Masoud Barati, Diana Addae
AI agents now handle personal data through tool use, function calls, and multi turn dialogue, which can create obligations under the General Data...
Yujeong Kwon, Yiyue Zhang, Shakhzod Yuldoshkhujaev +3 more
Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains...
Liran Tal, Johannes Kloos, Arsenii Rudich +2 more
We ran 300 repeated vulnerability-finding scans to measure how repeatable agentic large language model (LLM) security review is on the same...
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.
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