# Chances and Challenges of the Model Context Protocol in Digital Forensics and Incident Response

arXiv: https://arxiv.org/abs/2506.00274 · HTML: https://mlsanity.com/paper/2506.00274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 30 May 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 4 of 4)

- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown](https://mlsanity.com/paper/2307.10195/) (2023)
- [ChatGPT, Llama, can you write my report? An experiment on assisted digital forensics reports written using (Local) Large Language Models](https://mlsanity.com/paper/2312.14607/) (2023)
- [Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency](https://mlsanity.com/paper/2402.19366/) (2024)

## Abstract (arXiv)

Large language models hold considerable promise for supporting forensic investigations, but their widespread adoption is hindered by a lack of transparency, explainability, and reproducibility. This paper explores how the emerging Model Context Protocol can address these challenges and support the meaningful use of LLMs in digital forensics. Through a theoretical analysis, we examine how MCP can be integrated across various forensic scenarios - ranging from artifact analysis to the generation of interpretable reports. We also outline both technical and conceptual considerations for deploying an MCP server in forensic environments. Our analysis reveals a wide range of use cases in which MCP not only strengthens existing forensic workflows but also facilitates the application of LLMs to areas of forensics where their use was previously limited. Furthermore, we introduce the concept of the inference constraint level - a way of characterizing how specific MCP design choices can deliberately constrain model behavior, thereby enhancing both auditability and traceability. Our insights demonstrate that MCP has significant potential as a foundational component for developing LLM-assisted forensic workflows that are not only more transparent, reproducible, and legally defensible, but also represent a step toward increased automation in digital forensic analysis. However, we also highlight potential challenges that the adoption of MCP may pose for digital forensics in the future.