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 1701–1720 of 4,319 papers
Doohee You
The expansion of Multimodal Large Language Models (MLLMs) and their integration into autonomous agentic workflows has introduced a non-stationary...
Xuying Ning, Katherine Tieu, Dongqi Fu +39 more
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to...
Mohamed elShehaby, Ashraf Matrawy
Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning (ML) models to induce incorrect predictions. This paper investigates...
Juozas Dautartas, Olga Kurasova, Juozapas Rokas Čypas +1 more
Machine learning-based malware detectors are widely deployed in antivirus and endpoint detection systems, yet their reliance on static features makes...
Ali Iranmanesh, Peng Liu
Open-vocabulary embodied AI agents increasingly rely on vision-language models such as CLIP for object perception and task grounding. However, the...
Yubin Qu, Ying Zhang, Yanjun Zhang +4 more
Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than...
Kaixiang Wang, Jiong Lou, Zhaojiacheng Zhou +1 more
Memory-augmented large language model (LLM) agents use iterative reflection and self-evolution to solve complex tasks, but these mechanisms introduce...
Jonathan Diller, David Barnes, Rebekah Bogdanoff +14 more
As autonomous systems grow more advanced, objective metrics to evaluate their ethical and legal compliance are critical for informing end users of...
Rohith Uppala
Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when...
Dylan Marx, Marcel Dunaiski
Large Language Models (LLMs) remain vulnerable to jailbreak attempts that circumvent safety guardrails. We investigate whether multi-turn...
Yanyun Wang, Yu Huang, Zi Liang +2 more
The integration of audio modality into Large Audio Language Models (LALMs) significantly expands their attack surface. Existing jailbreak paradigms...
Xin Peng, Bo Lin, Jing Wang +5 more
Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby...
Hongjang Yang, Hyunsik Na, Daeseon Choi
LLM-based chatbot agents increasingly process user requests by combining natural-language reasoning with external tools such as web browsing. These...
Jiahe Guo, Xiangran Guo, Jiaxuan Chen +6 more
Multimodal large language models (MLLMs) often fail to transfer safety capabilities learned in the text modality to semantically equivalent non-text...
Lei Zhao, Abhay Bhaskar, Edgar Dobriban
AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI)...
Ziwei Wang, Jing Chen, Ruichao Liang +6 more
Despite rigorous safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. Existing black-box methods often rely on...
Md Navid Bin Islam, Sajal Saha, Senior Member
Machine-learning-based Intrusion Detection Systems (IDS) have achieved impressive accuracy in classifying network attacks, yet they consistently fall...
Xiao Yang, Ronghao Fu, Zhiwen Lin +10 more
Remote sensing vision-language models commonly rely on pretrained visual encoders to convert images into semantic features before language-model...
Zhi Quan Zhou, Dave Towey, Tsong Yueh Chen
Large language models (LLMs) are increasingly used to generate requirements specifications, design documents, code, and test cases. In contrast, much...
Ahmad Al-Tawaha, Shangding Gu, Peizhi Niu +2 more
Safety evaluations of memory-equipped LLM agents typically measure within-task safety: whether an agent completes a single scenario safely, often...
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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