# Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics

arXiv: https://arxiv.org/abs/2509.12233 · HTML: https://mlsanity.com/paper/2509.12233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 8 of 8)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models](https://mlsanity.com/paper/2404.13236/) (2024)
- [Integration of Agentic AI with 6G Networks for Mission-Critical Applications: Use-case and Challenges](https://mlsanity.com/paper/2502.13476/) (2025)
- [EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation](https://mlsanity.com/paper/2201.12358/) (2022)
- [Towards Energy Efficiency in RAN Network Slicing](https://mlsanity.com/paper/2307.07016/) (2023)

## Built on by (most cited first, 1 of 1)

- [AgentSOC: A Multi-Layer Agentic AI Framework for Security Operations Automation](https://mlsanity.com/paper/2604.20134/) (2026)

## Abstract (arXiv)

The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust analytics, and interpretable decision support. Specifically, we design an AAI architecture comprising dedicated agents for cyber-threat detection and response at charging stations, real-time State of Charge (SoC) estimation, and State of Health (SoH) anomaly detection, all coordinated through a shared, explainable reasoning layer; develop interpretable threat-mitigation mechanisms that proactively identify and neutralize attacks on both physical charging points and learning components; propose resilient SoC and SoH models that leverage continuous and adversarial-aware learning to produce accurate, uncertainty-aware forecasts with human-readable explanations; and implement a three-agent pipeline, where each agent uses LLM-driven reasoning and dynamic tool invocation to interpret intent, contextualize tasks, and execute formal optimizations for user-centric assistance. Finally, we validate our framework through comprehensive experiments across diverse IoEV scenarios, demonstrating significant improvements in security and prediction accuracy. All datasets, models, and code will be released publicly.