AI Threat Alert indexes 3,023+ 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 161–180 of 759 papers
Clear filtersTobias Braun, Jonas Henry Grebe, Hossein Shakibania +2 more
Unified autoregressive models (UAMs) are transformer models that generate text as well as image tokens within a single autoregressive pass. Shared...
Becky Mashaido, Tapadhir Das
Prompt injection attacks pose significant risks to language model safety, yet existing defenses are typically evaluated using classification...
John T. Halloran, Noopur S. Bhatt
Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful...
Yixiang Yao, Yuhang Yao, Xinyi Fan +5 more
The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these...
Doohee You
The expansion of Multimodal Large Language Models (MLLMs) and their integration into autonomous agentic workflows has introduced a non-stationary...
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...
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...
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...
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...
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...
Datta Manikanta Sri Hari Danduri, Aravind Kumar Machiry
AI Accelerator (AIA) are specialized hardware e.g., Tensor Processing Unit (TPU), that enable optimal and efficient execution of AI applications and...
Sahar Abdelnabi, Eugene Bagdasarian
Prompt injection is the most critical vulnerability in deployed AI agents. Despite recent progress, we show that the prevailing defense paradigm...
Xin Wang, Yixu Wang, Jiaming Zhang +6 more
Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to...
Udari Madhushani Sehwag, Zhengyang Shan, Heming Liu +3 more
Clarification-seeking behavior is widely regarded as a desirable property of LLM agents, enabling them to resolve ambiguity before acting on...
Rui Wen, Mark Russinovich, Andrew Paverd +2 more
Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in...
Tri Cao, Yulin Chen, Hieu Cao +8 more
Web agents can autonomously complete online tasks by interacting with websites, but their exposure to open web environments makes them vulnerable to...
Zhigao Huang, Zhengqing Hu, Dong Chen +5 more
Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional...
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 3,023+ 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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