AI Threat Alert indexes 3,397+ 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 121–140 of 669 papers
Clear filtersXavier Martínez-Luaña, Alba Gude-Santos, Manuel Fernández-Veiga +1 more
Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy...
Yueming Huang, Wenhan Yao, Fen Xiao +2 more
Recently, speech classification methods have gained widespread adoption in intelligent gadgets. Current study indicates that backdoor attacks provide...
Mona Rajhans, Vishal Khawarey
Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that...
Christopher 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...
Junlong Liu, Haobo Wang, Weiqi Luo +1 more
Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs). While prior work has primarily studied attacks and...
Chanwoo Choi, Euntae Kim, Kyuho Lee +6 more
Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to...
Zhihao Dou, Qinjian Zhao, Zhiqiang Gao +1 more
Vision--Language Models (VLMs) are increasingly deployed in safety-critical applications, yet remain vulnerable to backdoor attacks. Existing methods...
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...
Chenyu Zhou, Qiliang Jiang, Shuning Wu +1 more
Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a...
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...
Mitchell Hermon, Rahul Gupta, Weitong Ruan +2 more
We identify a security-fidelity tradeoff in defending LLMs against indirect prompt injection: defenses resist injected instructions largely by...
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...
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1 more
Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless...
Jun Wen Leong
We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through...
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...
Michael Kühr, Mevlüt Yildirim, Maximilian Luedecke +2 more
Traffic sign classification is a crucial task for autonomous vehicles, and numerous attacks against it have been identified. A majority of physical...
Yiting Hu, Lingjie Duan
Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being adopted across key fields such as large language...
Yingjie Wang, Yi Dong, Edmund Lau +3 more
Rare events govern the safety profile of modern AI systems, yet their probabilities are extremely difficult to estimate: direct Monte Carlo requires...
Alex Kwon
LLM agents carry conclusions across steps and sessions in compressed memory, and memory products (e.g., mem0, LangMem) rewrite conversation into...
Preet Baxi, Jiannan Xu, Jane Yi Jiang +1 more
Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate...
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 3,397+ 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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AI Threat Alert