Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges

Anshuman Chhabra, Shrestha Datta, Shahriar Kabir Nahin, Prasant Mohapatra

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 agentic AI, reviews recent benchmarks and evaluation me

Categories

Cite This Resource

@article{llmsec202600133,
  title = {Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges},
  author = {Anshuman Chhabra and Shrestha Datta and Shahriar Kabir Nahin and Prasant Mohapatra},
  year = {2025},
  doi = {10.1109/ACCESS.2026.3675554},
  url = {https://arxiv.org/abs/2510.23883},
}