paperAugust 2026Unreviewed

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari

Abstract

Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world privileges---calling APIs, modifying files, and querying databases---a compromised reasoning step can trigger unauthorized data access, irreversible state changes, or cascading failures, yet the security research community has not kept pace. To

Cite

@misc{hossain2026understanding,
  title = {{On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models}},
  author = {Md Jafrin Hossain and Mohammad Arif Hossain and Nirwan Ansari},
  year = {2026},
  month = aug,
  eprint = {2608.10530},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.10530}
}