AI Threat Alert indexes 3,381+ 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 181–200 of 1,659 papers
Clear filtersChristopher Ellis, Shreyas Chaudhari, Mei-Yu Wang +3 more
In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given...
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,...
Brett Reynolds
Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model has followed an...
Shei Pern Chua, Fangzhao Wu
Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks...
MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis
Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their...
Jiahui Wang, Zhenyuan Li, Zhengkai Wang +2 more
Sophisticated cyber attacks, particularly Advanced Persistent Threats (APTs), require effective post-intrusion forensic analysis. Provenance-based...
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1 more
Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw...
Moreno D'Incà, Massimiliano Mancini, Nicu Sebe
To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation...
Zhengxing Li, David J. Miller, Guangmingmei Yang +1 more
While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such...
Hongliang Liu, Yuhao Wu, Tung-Ling Li
AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from...
Xudong Shen, Li Yuan, Ye Chen +3 more
Large Language Models (LLMs) exhibit strong semantic capabilities, yet their resilience to manipulative linguistic patterns such as logical fallacies...
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...
Arash Raftari, Mehrdad Mahdavi, Nathan Blackthorn +1 more
Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious...
Luciano Pianese, Vittorio Orbinato, Pietro Liguori +1 more
Generative AI has emerged as a significant cybersecurity threat, with several recent attack campaigns leveraging LLMs to generate code for malicious...
Peizhi Niu, Wenjie Qu, Shangding Gu +14 more
Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on...
Kunyang Li, Kyle Domico, Jonathan Gregory +1 more
Multi-agent systems (MAS) are increasingly used to automate complex, distributed workflows. However, their inter-agent communication channels...
Jun Wen Leong
We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through...
Dvir Alsheich, Adar Peleg, Ben Hagag +3 more
The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to...
Chen Frydman, Aviram Zilberman, Rubin Krief +6 more
Mitigating an observed adversary in an enterprise network typically takes weeks of expert work: an analyst derives a mitigation tailored to that...
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...
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 3,381+ 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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