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 101–120 of 660 papers
Clear filtersKristina Nikolić, Egor Zverev, Javier Rando +3 more
Defenses that provide security guarantees against prompt injection attacks rely on strict isolation between trusted instructions and untrusted data....
Konstantin Berlin
Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays...
Yechao Zhang, Shiqian Zhao, Jiawen Zhang +5 more
Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and...
Woohyuk Choi, Juhee Kim, Taehyun Kang +3 more
AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security...
Neeraj Karamchandani, Piyush Nagasubramaniam, Sencun Zhu +1 more
Persistent memory has enabled large language model (LLM) agents to store factual knowledge, prior decisions, reasoning histories, tool usage...
Mouhamed Amine Bouchiha, Gregory Blanc
Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated...
Mouhamed Amine Bouchiha, Gregory Blanc
Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated...
Josh Hills, Ida Caspary, Asa Cooper Stickland
As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence...
Bohan Liu, Wenqian Ye, Guangzhi Xiong +3 more
Models trained via Contrastive Language-Image Pretraining (CLIP) serve as the foundational vision encoders for most modern Large Vision Language...
Xavier 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...
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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