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 321–340 of 4,319 papers
Mohammad Reza Mirbagheri, Amir Mahdi Sadeghzadeh
Aligned large language models (LLMs) remain vulnerable to jailbreak prompts that circumvent their safety guardrails, making the identification of...
Arya Fayyazi, Mehdi Kamal, Massoud Pedram
Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact...
Ali Baheri, Peng Wei
Software agents built on large language models act by issuing tool calls, some of which cannot be undone. To control theory the model is an opaque...
Anning Hu, Yangyu Li, Yuqi Huang +2 more
Large language models (LLMs) are increasingly used as judges to assign scalar scores that drive downstream decision-making, including tasks such as...
Jincheng Ying, Li Wenlin, Wencheng Yang +1 more
White-box neuron pruning has shown that refusal in large language models (LLMs) can depend on a small, removable set of feed-forward (FFN) channels....
Xiuyuan Qi, Yi Wang, Wenjie Wang
Preference alignment enables language models to better reflect human values, but the preference data contains sensitive information, especially when...
Zhixiong Zhuang, Jin Jia, Zhiwei Shang +1 more
Automated approval systems let coding agents execute high-risk actions with minimal human oversight by delegating the authorization decision to a...
Erin Crawley, Hidenori Tanaka
AI agents can now conduct real-world cyberattacks, scale up capabilities with the number of agents, and collectively pursue misaligned goals to...
Shihab Ahmed, Md Sajidul Islam Sajid, Teryl Taylor +2 more
Malware defenses often remove or isolate suspicious programs as quickly as possible. While effective for containment, this approach can also waste an...
Saisab Sadhu, Shreeyans Arora, Pratinav Seth
Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision...
Seyedarmin Azizi, Erfan Baghaei Potraghloo, Massoud Pedram
A typed decision model reads a piece of text and returns a probability over caller-defined options, each with a short written definition, generating...
Jingnan Zheng, Dongcheng Zhang, Yi Zhang +10 more
Recursive self-improvement, the participation of AI systems in improving their own capabilities, is beginning to move from theoretical prospect to...
Edward Chen, Yuntao Du
Unauthorized model distillation, in which a model is trained on the outputs of a proprietary large language model (LLM), is a growing threat to model...
Georgios Milis, Tom Sander, Tomáš Souček +2 more
Watermarking large language models is popular for tracing chatbot and agentic outputs, yet detectors remain unreleased since exposing them could let...
Steve Nouyep, Sébastien Salva, Maxime Puys
Retrieval-Augmented Generation (RAG) systems extend large language models (LLMs) with external knowledge through a multi-stage pipeline. While this...
Varun Gumma, Navonil Majumdar, Soujanya Poria
The rapid proliferation of Large Language Models (LLMs) necessitates reliable watermarking techniques to identify AI-generated text and ensure...
Ines Ortega-Fernandez, Mateusz Kowalczyk, Keri Warr
A compromised LLM inference server can leak model weights by encoding payload bits in otherwise plausible token choices. A replay of the same prompt...
Heyang Tan, Chengxin Gao, Xin Wen +2 more
In real-world software development, the primary challenge in vulnerability detection is often not finding suspicious code, but identifying which...
Luman Zhao, Minghui Xu, Yue Zhang +1 more
Large language models (LLMs) perform remarkably well on complex tasks, yet remain highly vulnerable to prompt injection attacks, where malicious...
Varun Kohli, Lee Bing Cheng, Nur Hazim Ghazali +3 more
Effective source recovery is critical to security applications such as malware analysis, vulnerability assessment, and legacy maintenance. Large...
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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