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A5/1 is in the Air: Passive Detection of 2G (GSM) Ciphering Algorithms

Matthias Koch, Christian Nettersheim, Thorsten Horstmann, Michael Rademacher

TL;DR

This work analyzes the ongoing use of the A5/1 cipher in 2G GSM networks by passively detecting Cipher Mode Command messages with low-cost hardware. A measurement system based on RTL-SDRs and Raspberry Pis collected 565,115 CMC samples across 10 locations in Germany over 88 days, enabling cross-provider comparisons. The results show significant variation: one operator favors A5/3, another relies heavily on A5/1, while a third mixes A5/3 and A5/4, highlighting security concerns tied to legacy encryption. The study demonstrates the value of long-term, passive monitoring for understanding real-world cipher usage and informs discussions on upgrading legacy systems to mitigate downgrade risks in mixed-network environments.

Abstract

This paper investigates the ongoing use of the A5/1 ciphering algorithm within 2G GSM networks. Despite its known vulnerabilities and the gradual phasing out of GSM technology by some operators, GSM security remains relevant due to potential downgrade attacks from 4G/5G networks and its use in IoT applications. We present a comprehensive overview of a historical weakness associated with the A5 family of cryptographic algorithms. Building on this, our main contribution is the design of a measurement approach using low-cost, off-the-shelf hardware to passively monitor Cipher Mode Command messages transmitted by base transceiver stations (BTS). We collected over 500,000 samples at 10 different locations, focusing on the three largest mobile network operators in Germany. Our findings reveal significant variations in algorithm usage among these providers. One operator favors A5/3, while another surprisingly retains a high reliance on the compromised A5/1. The third provider shows a marked preference for A5/3 and A5/4, indicating a shift towards more secure ciphering algorithms in GSM networks.

A5/1 is in the Air: Passive Detection of 2G (GSM) Ciphering Algorithms

TL;DR

This work analyzes the ongoing use of the A5/1 cipher in 2G GSM networks by passively detecting Cipher Mode Command messages with low-cost hardware. A measurement system based on RTL-SDRs and Raspberry Pis collected 565,115 CMC samples across 10 locations in Germany over 88 days, enabling cross-provider comparisons. The results show significant variation: one operator favors A5/3, another relies heavily on A5/1, while a third mixes A5/3 and A5/4, highlighting security concerns tied to legacy encryption. The study demonstrates the value of long-term, passive monitoring for understanding real-world cipher usage and informs discussions on upgrading legacy systems to mitigate downgrade risks in mixed-network environments.

Abstract

This paper investigates the ongoing use of the A5/1 ciphering algorithm within 2G GSM networks. Despite its known vulnerabilities and the gradual phasing out of GSM technology by some operators, GSM security remains relevant due to potential downgrade attacks from 4G/5G networks and its use in IoT applications. We present a comprehensive overview of a historical weakness associated with the A5 family of cryptographic algorithms. Building on this, our main contribution is the design of a measurement approach using low-cost, off-the-shelf hardware to passively monitor Cipher Mode Command messages transmitted by base transceiver stations (BTS). We collected over 500,000 samples at 10 different locations, focusing on the three largest mobile network operators in Germany. Our findings reveal significant variations in algorithm usage among these providers. One operator favors A5/3, while another surprisingly retains a high reliance on the compromised A5/1. The third provider shows a marked preference for A5/3 and A5/4, indicating a shift towards more secure ciphering algorithms in GSM networks.

Paper Structure

This paper contains 12 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. A5 Usage Surveys
  4. SnoopSnitch
  5. Background on GSM
  6. Methodology
  7. Sensor
  8. Deployment
  9. Measuring campaign
  10. Evaluation & Results
  11. Known limitations
  12. Conclusion & Open Questions

Figures (4)

  • Figure 1: Components of our sensor
  • Figure 2: Distribution of algorithm usage for the different providers across the various locations. Mean values are marked with red line.
  • Figure 3: Heatmap showing the usage rates of the algorithms per provider and per location. The color intensity represents the rate of algorithm usage. Settlement type of the location given by u = urban, s = suburban, r = rural.
  • Figure 4: Normalized hourly usage rates of different encryption algorithms for each provider. The rates are calculated as the proportion of the total measurements for each location and hour. The graph shows the average rate across all locations for each hour of the day.