AI Threat Alert indexes 4,319+ 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 141–160 of 685 papers
Clear filtersHabibur Rahaman, Swastik Bhattacharya, Sanjay Das +2 more
Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few...
Habibur Rahaman, Swastik Bhattacharya, Sanjay Das +2 more
Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few...
Heyam Bin Jahlan Areej Alhothali Abeer Alhothali
The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in...
Thiago Santos de Moura, Fynn Matuschek, Flavio Toffalini +1 more
Large Language Models (LLMs) are widely used to generate code. Although their functional plausibility keeps improving, the generated code often...
Haneen Najjar, Luca Scionis, Haritz Puerto +1 more
AI agents increasingly rely on modular third-party "skills" that are dynamically selected by skill routers to execute complex tasks. While recent...
Yibo Zhang, Tianrong Guan, Liang Lin +3 more
Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric:...
Shailen Smith, Rasmus Torp, Adam Breuer
In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks (MIAs) significantly underestimate training-data privacy...
Lizhi Zhang, Xin He, Dianxuan Fu +5 more
Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this...
Qiusi Zhan, Nian Lyu, Stephanie Ding +3 more
Developers can build LLM agents by adapting third-party models through benign post-training. We study a supply-chain threat in which an attacker...
Krishna Kabra, Constantin Venhoff, Christian Schroeder de Witt
Interpretability methods for neural networks are predominantly reactive: they analyse activations produced during specific forward passes, requiring...
Marsalis Gibson, Claire Tomlin, Shankar Sastry
Security evaluation of learning-based systems requires more than just testing the system against a fixed collection of attacks. It requires adaptive...
Abhinav Sudhakar Dubey, Scott Sirri, Vaggos Chatziafratis +1 more
While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important...
Jiaming Qian, Pengyang Zhou, Jiahe Xu +1 more
Adversarial attacks on Large Language Models (LLMs) aim to induce harmful content. However, existing methods suffer from high computational costs or...
Tobias Heldt, Matt Turk, Christoph Landolt +1 more
The release decision for frontier AI systems increasingly relies on cyber capability benchmarks, yet public vulnerability benchmarks can expose...
Mohamed Dhouib, Clement Elliker, Alexi Canesse +5 more
Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external content. Existing...
Alexandru Nazare, Agnese Profico, Nicolò Vania +6 more
The robustness of Personally Identifiable Information (PII) protection in Large Language Models (LLMs) is a critical concern, yet the risks...
Théo Lasnier, Romain Froger, Maxence Lasbordes +1 more
Large Language Model (LLM) assistants routinely decide whether they can trust users and third parties whose competence, intentions, and integrity...
Enrico Ahlers, Daniel Passon, Tobias Kiecker +2 more
Sparse autoencoders (SAEs) are increasingly used not only to interpret language models but also to intervene on their internal representations. We...
Christoph Bühler, Matteo Biagiola, Luca Di Grazia +1 more
AI agents built on large language models (LLMs) run shell commands, read and write files, and reach the network, typically with their user's...
Tural Hagverdiyev
A tool-using model can follow a malicious instruction even when its credentials are valid. We study whether task-scoped authorization contains the...
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 4,319+ 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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