By Auto Threat Researcher Jerold Camacho
The electronic control unit (ECU) that is connected to the vehicle’s CAN bus and displays speed, RPM, warnings, and status indicators is more than just a display. Because it directly reflects vehicle state to the driver, any manipulation or corruption of its CAN inputs can result in misleading or unsafe information being displayed.
In-vehicle networks’ security assumptions and functional dependencies can be better understood by studying the instrument cluster alone.
In This Content
How Safe CAN-Driven ECU
Analysis Is Facilitated by Bench Testing Bench testing allows ECUs to be powered, observed, and stimulated without integration into a live vehicle. In this study, a Suzuki Wagon R MH55S instrument cluster was sourced and tested in a controlled environment, eliminating risks associated with on-vehicle experimentation while preserving real-world behavior.
The observations that are going to be made here aren’t unique to Suzuki cars; rather, they are based on common trust assumptions that are found in a lot of CAN-based automotive architectures. Under controlled conditions, this method makes it possible to repeat the analysis of CAN traffic, message dependencies, and ECU responses. To safely operate the instrument cluster off-vehicle, a stable 12V power source with current limiting was used. A USB-based CAN interface provided CAN communication, allowing message injection and monitoring without the need for additional vehicle ECUs.
Power and signaling must be carefully controlled to prevent hardware damage and ensure that observed behavior only results from CAN input. Before CAN traffic was introduced, power, ground, and CAN-H/CAN-L pins were identified using a multimeter.
Each connection was validated prior to powering the cluster to ensure correct polarity and signal integrity.
Once powered, the cluster reliably entered an operational state and began responding to CAN messages, confirming correct pin identification.
Why Trust-Based Networks Can Benefit from CAN Fuzzing
The CAN protocol does not include authentication, integrity checking, or sender verification. As a result, ECUs typically trust any correctly formatted message placed on the bus.
CAN fuzzing is an efficient method for identifying message-driven functionality and potential security flaws because it takes advantage of this trust model by injecting randomized or malformed frames into the ECU in order to observe unexpected behavior.
How CAN Frame Injection Reveals and Isolates Instrument Cluster Behaviors
Random CAN frames were injected into the powered instrument cluster with the assistance of the automotive security tool caringcaribou. Warning sounds, indicator activation, shifts in RPM, and display updates were all observable outcomes of this.
These reactions demonstrate that the cluster processes unauthenticated messages without contextual validation of source or vehicle state.
Captured CAN traffic from the fuzzing session was analyzed to identify messages responsible for specific behaviors. By correlating injected frames with observed RPM changes, a CAN frame controlling engine speed indication was isolated.
The message-to-function mapping was confirmed by replaying the identified frame to consistently replicate RPM changes on the cluster.
How Testing Aids in Mitigation and Why Trust-Based
CAN Communication Is Risky The ability to induce meaningful ECU behavior through arbitrary CAN message injection exposes a structural weakness in CAN-based vehicle networks. Because many ECUs still rely on legacy trust assumptions—accepting correctly formatted messages without verifying origin or intent—unauthorized network access can allow injected messages to disrupt ECU coordination or present false information to the driver, even in vehicles equipped with modern gateways and filtering mechanisms.
Bench-level ECU testing provides a safe and effective way to move this risk from theory to evidence. Testing demonstrates the ease with which trust boundaries can be breached and the potential for legitimate-looking traffic to result in unintended outcomes by observing ECU behavior independently and under controlled CAN message injection. These insights are difficult to obtain through on-vehicle testing alone and are critical for understanding real-world security limitations.
Effective mitigation strategies are most successful when grounded in these observations. Replacing implicit trust with a zero trust communication model enables ECUs to validatemessage authenticity and context rather than accepting CAN traffic by default. Gateway-based anomaly detection can feed signals into structured risk assessment processes like TARA that can identify deviations in command sequences, timing, or frequency at the network level. Together, these measures allow OEMs to distinguish credible threats from benign anomalies and to apply mitigations across both current vehicle lifecycles and future platform designs.