New survey published on agentic AI security threats
Autonomous AI systems create unique security challenges
Taxonomy of threats specific to agentic AI systems outlined
Security risks distinct from traditional AI safety and software security
📖 Full Retelling
Researchers published a comprehensive survey on agentic AI security threats (arXiv:2510.23883v2) in October 2025, detailing new security risks posed by autonomous AI systems with planning capabilities. The paper outlines a taxonomy of threats specific to agentic AI systems that can independently execute tasks across web, software, and physical environments, addressing security challenges distinct from both traditional AI safety and conventional software security vulnerabilities. The survey examines agentic AI systems, which are powered by large language models (LLMs) and endowed with advanced capabilities including planning, tool use, memory, and autonomy. These emerging platforms represent a significant advancement in automation technology but simultaneously introduce complex security considerations that require new defensive approaches. The researchers emphasize that the autonomous nature of these systems amplifies security risks in ways that previous AI safety frameworks and traditional software security measures were not designed to address. According to the paper, agentic AI systems' ability to operate independently across multiple environments creates unprecedented security challenges. The survey categorizes these threats systematically, providing researchers and developers with a framework for understanding and addressing potential vulnerabilities. As these systems become more prevalent in critical applications, from automated decision-making to physical task execution, the identified security concerns become increasingly urgent for both the AI research community and industry stakeholders.
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses AI alignment (which aims to ensure AI systems behave as intended), monitoring AI systems for risks, and enhancing their rob...
arXiv:2510.23883v2 Announce Type: replace
Abstract: Agentic AI systems powered by large language models (LLMs) and endowed with planning, tool use, memory, and autonomy, are emerging as powerful, flexible platforms for automation. Their ability to autonomously execute tasks across web, software, and physical environments creates new and amplified security risks, distinct from both traditional AI safety and conventional software security. This survey outlines a taxonomy of threats specific to ag
