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OffRAMPS: An FPGA-based Intermediary for Analysis and Modification of Additive Manufacturing Control Systems

Jason Blocklove, Md Raz, Prithwish Basu Roy, Hammond Pearce, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri

TL;DR

OffRAMPS introduces an FPGA-based intermediary that sits between the printer controller and I/O to enable real-time analysis, modification, and monitoring of 3D printer control signals. By leveraging a RAMPS-based open-source stack, it supports hardware-in-the-middle Trojan insertion and detection, demonstrated across a suite of attacks inspired by the literature, including the Flaw3D family. The paper demonstrates the platform’s capability to emulate and detect attacks, with a pragmatic monitoring approach that compares live pulse profiles to a golden model, achieving broad Trojan detection. This hardware-centric approach provides a practical, extensible path toward evaluating security blind spots and validating defenses in AM, with open-source availability to accelerate adoption. The results have direct implications for improving safety and reliability in AM applications, especially in aerospace, medical, and other critical domains.

Abstract

Cybersecurity threats in Additive Manufacturing (AM) are an increasing concern as AM adoption continues to grow. AM is now being used for parts in the aerospace, transportation, and medical domains. Threat vectors which allow for part compromise are particularly concerning, as any failure in these domains would have life-threatening consequences. A major challenge to investigation of AM part-compromises comes from the difficulty in evaluating and benchmarking both identified threat vectors as well as methods for detecting adversarial actions. In this work, we introduce a generalized platform for systematic analysis of attacks against and defenses for 3D printers. Our "OFFRAMPS" platform is based on the open-source 3D printer control board "RAMPS." OFFRAMPS allows analysis, recording, and modification of all control signals and I/O for a 3D printer. We show the efficacy of OFFRAMPS by presenting a series of case studies based on several Trojans, including ones identified in the literature, and show that OFFRAMPS can both emulate and detect these attacks, i.e., it can both change and detect arbitrary changes to the g-code print commands.

OffRAMPS: An FPGA-based Intermediary for Analysis and Modification of Additive Manufacturing Control Systems

TL;DR

OffRAMPS introduces an FPGA-based intermediary that sits between the printer controller and I/O to enable real-time analysis, modification, and monitoring of 3D printer control signals. By leveraging a RAMPS-based open-source stack, it supports hardware-in-the-middle Trojan insertion and detection, demonstrated across a suite of attacks inspired by the literature, including the Flaw3D family. The paper demonstrates the platform’s capability to emulate and detect attacks, with a pragmatic monitoring approach that compares live pulse profiles to a golden model, achieving broad Trojan detection. This hardware-centric approach provides a practical, extensible path toward evaluating security blind spots and validating defenses in AM, with open-source availability to accelerate adoption. The results have direct implications for improving safety and reliability in AM applications, especially in aerospace, medical, and other critical domains.

Abstract

Cybersecurity threats in Additive Manufacturing (AM) are an increasing concern as AM adoption continues to grow. AM is now being used for parts in the aerospace, transportation, and medical domains. Threat vectors which allow for part compromise are particularly concerning, as any failure in these domains would have life-threatening consequences. A major challenge to investigation of AM part-compromises comes from the difficulty in evaluating and benchmarking both identified threat vectors as well as methods for detecting adversarial actions. In this work, we introduce a generalized platform for systematic analysis of attacks against and defenses for 3D printers. Our "OFFRAMPS" platform is based on the open-source 3D printer control board "RAMPS." OFFRAMPS allows analysis, recording, and modification of all control signals and I/O for a 3D printer. We show the efficacy of OFFRAMPS by presenting a series of case studies based on several Trojans, including ones identified in the literature, and show that OFFRAMPS can both emulate and detect these attacks, i.e., it can both change and detect arbitrary changes to the g-code print commands.
Paper Structure (26 sections, 4 figures, 2 tables)

This paper contains 26 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Prior Work
  3. 3D Printing Security Threats
  4. 3D Printing Threat Detection Techniques
  5. OffRAMPS Board Overview
  6. Open Source 3D Printing
  7. OffRAMPS: Design Motivation
  8. Primary OffRAMPS Printed Circuit Board Design
  9. Digilent Cmod-A7
  10. Arduino Mega
  11. RAMPS 1.4
  12. Logic level shifting
  13. Board Power
  14. Test Environment
  15. OffRAMPS for Trojan Insertion
  16. ...and 11 more sections

Figures (4)

  • Figure 1: Simplified Additive Manufacturing (3D printing) process. Malicious interference can occur at any step.
  • Figure 2: The stack of Arduino Mega and RAMPS board separated and put in place on the OffRAMPS board.
  • Figure 3: Different signal path options on the OffRAMPS.
  • Figure 4: Detection of an emulated Flaw3D Trojan which relocates material every 20 movements.