Accelerating Security Research in Connected Vehicles

Introduction

Modern vehicles operate as complex, interconnected networks of Electronic Control Units (ECUs) that manage everything from infotainment systems to critical safety features like braking and steering. These ECUs communicate primarily over the Controller Area Network (CAN), a protocol developed in the 1980s without built-in security. As connectivity increases, so do the avenues for potential attacks, necessitating robust and scalable testing methodologies.

Fuzz testing, a technique traditionally used to find vulnerabilities by feeding unexpected or random data into a system, is now being adapted for use in cyber-physical systems, including automotive ECUs. This blog post explores an innovative approach to automated fuzzing of automotive ECUs, drawing from recent research that integrates fuzzing tools with sensor-based oracle functions to detect physical system changes.

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The Security Challenge of Automotive ECUs

ECUs in modern vehicles form a distributed system that is difficult to test exhaustively due to its complexity and proprietary nature. Each ECU typically performs a specific function, such as controlling the transmission or monitoring environmental sensors. The lack of authentication in the CAN protocol allows any ECU to send messages on the bus, making the network vulnerable to spoofing and injection attacks.

Notable past exploits have demonstrated the ability to control critical vehicle functions remotely, highlighting the need for proactive security assessments. Traditional fuzzing methods fall short in embedded environments like vehicles due to limited observability and the difficulty of defining meaningful oracle functions to evaluate system behavior.

In response to these risks, the automotive industry has adopted security frameworks and standards such as AUTOSAR Secure Onboard Communication and ISO/SAE 21434. These frameworks aim to embed security into vehicle systems from the design phase.

• AUTOSAR (AUTomotive Open System ARchitecture) provides specifications for software architecture in automotive ECUs. The Secure Onboard Communication (SecOC) module within AUTOSAR supports message authentication by enabling the exchange of cryptographic checksums and freshness values to mitigate replay and spoofing attacks.
• ISO/SAE 21434 is a standard dedicated to cybersecurity risk management for road vehicles. It defines a structured approach to identify, assess, and mitigate cybersecurity risks throughout the vehicle lifecycle, including development, production, and decommissioning.

These standards highlight the industry's shift toward "security by design," yet real-world systems often lag in implementation or present configuration errors. Fuzzing, particularly when automated and guided by sensor-based oracles, offers a pragmatic solution to uncover such issues even in theoretically secure systems.

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Integrating Fuzzing with Sensor Harnesses

The breakthrough described in the research involves the use of a sensor harness to monitor physical changes in ECUs as a result of fuzzing inputs. This approach allows for the automation of oracle functions, which are used to determine whether a test case has triggered an interesting or unexpected system behavior.

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Components of the Fuzzing Framework

• CaringCaribou Framework: A modular, open-source platform used for automotive penetration testing. The fuzzing modules are integrated into this framework for easy deployment.
• Sensor Harness: A setup of light and color sensors attached to ECUs that detect changes in indicators (LEDs, displays, etc.) to infer system responses.
• Oracle Functions: Logic that interprets sensor data to determine if a particular fuzz input has triggered a system state change or potential vulnerability.
• Fuzzing Strategies:
  • Random Fuzzing: Sends randomly generated CAN messages.
  • Brute-Force Fuzzing: Systematically enumerates specific parts of message payloads or IDs.
  • Mutation Fuzzing: Applies random bit flips to known good messages to explore variations.
  • Identify Fuzzing: Analyzes logs to determine the minimal message set that causes a specific response.
  • Omission Fuzzing: Tests the effect of omitting certain messages to identify dependencies.

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Real-World Evaluation: Case Studies

VulCAN Framework

In an experimental setup using the VulCAN framework (an AUTOSAR-compliant message authentication protocol), the fuzzer discovered two critical vulnerabilities:

1. Extended ID Injection: Messages with extended CAN IDs, not anticipated by the application, caused unauthorized display outputs.
2. Nonce Desynchronization: Flooding with certain patterns disrupted nonce synchronization, leading to denial-of-service conditions.

These findings underscore fuzzing's potential to uncover subtle yet impactful bugs even in systems designed with security in mind.

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Instrument Cluster Reverse Engineering

Instrument clusters from commercial vehicles were tested with the fuzzing tool. By using the sensor harness to detect LED activations and gauge needle movements, the researchers could automate the discovery of message formats and control mechanisms:

• Brute-force fuzzing over 11-bit IDs revealed the activation messages for various indicators.
• Mutation fuzzing identified specific bits in the payloads controlling each function.
• The process was reduced from days of manual testing to hours of automated analysis.

These insights are crucial when evaluating compliance with ISO/SAE 21434, which emphasizes continuous cybersecurity assessment and validation.

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Challenges and Limitations

While automated fuzzing of automotive ECUs holds significant promise, there are notable challenges and limitations to consider. These include both technical hurdles and broader contextual constraints that can affect the utility and reliability of fuzzing in automotive environments:

1. Timing Sensitivity: Many automotive ECUs rely on time-critical interactions. Small delays or irregular message timing introduced during fuzzing may trigger failsafe behaviors or cause ECUs to ignore input, which complicates the interpretation of results and the ability to maintain test stability.
2. False Positives and Environmental Noise: Physical sensors used in harnesses can generate false positives due to ambient light changes, electromagnetic interference, or vibration. This necessitates careful calibration and noise filtering, especially in uncontrolled environments.
3. Lack of Documentation and Vendor Lock-In: Reverse-engineering ECU behavior is complicated by proprietary protocols and undocumented behavior. This creates blind spots in oracle function design and reduces the ability to trace observed effects to specific message logic.
4. Complex State Machines: ECUs often implement stateful logic with multiple modes (e.g., ignition states, diagnostics, limp mode). Without knowledge of the current state or state transitions, fuzzing may yield inconsistent results or fail to reproduce previously observed behaviors.
5. Safety Considerations: Testing live vehicles or ECUs connected to actuators that can move or engage physical components carries inherent safety risks. Even bench setups must be rigorously controlled to prevent damage to hardware or unintended activation.
6. Scalability and Logging Overhead: High-frequency fuzzing can quickly generate massive volumes of log data. Efficient storage, filtering, and analysis systems are required to make the results usable and actionable.
7. Integration Gaps with Traditional QA Pipelines: Many fuzzing tools remain isolated from standard software QA processes, making it difficult to align fuzzing outcomes with test case coverage, regression tracking, or formal requirement verification.

Despite these limitations, many of them can be mitigated with thoughtful testbed design, robust hardware integration, and smart fuzzing strategies. Platforms like Penzzer are working to address several of these pain points by providing tighter integration with CI/CD environments, improved oracle modeling, and scalable result correlation mechanisms.

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Implications for Cyber-Physical Security

This methodology significantly advances the state of automotive penetration testing by:

• Automating discovery of undocumented functionality
• Detecting implementation bugs and protocol misconfigurations
• Enabling scalable, repeatable security assessments

Furthermore, this approach is not limited to automotive applications. Any cyber-physical system where physical outputs can be monitored (e.g., industrial controllers, medical devices) stands to benefit from similar automated fuzzing strategies.

The ability to correlate fuzzing inputs with physical system responses not only meets but enhances the security validation requirements outlined in ISO/SAE 21434. This standard calls for rigorous testing strategies throughout the development lifecycle, and automated fuzzing fits well into continuous integration pipelines.

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Conclusion and Future Directions

Automated fuzzing of automotive ECUs, enhanced by physical oracle detection, represents a powerful tool in the arsenal of security researchers. As vehicles grow more connected, and attack surfaces more complex, scalable and effective testing tools are essential.

This work illustrates the effectiveness of combining traditional fuzzing methodologies with innovative hardware-based observation mechanisms. Future developments could include:

• Expanding sensor types for broader actuator coverage
• Integrating machine learning to optimize input generation
• Enhancing support for other automotive communication standards like CAN-FD or Ethernet
• Embedding fuzzing routines into compliance workflows for ISO/SAE 21434 and AUTOSAR SecOC validation

By bridging the gap between software fuzzing and hardware interaction, this approach lays the foundation for next-generation security testing in connected vehicles.

For those interested in trying out these tools, the source code and build instructions are available at the CaringCaribou GitHub repository.

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Penzzer Note: The described approach aligns closely with Penzzer's mission to make advanced fuzzing accessible and automated for modern, security-conscious development environments. By integrating similar sensor-based oracles and modular fuzzing configurations, Penzzer offers a streamlined pathway to secure complex embedded systems.

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Penzzer Integration and Differentiators:

Unlike traditional fuzzers that operate in isolated environments or require extensive manual setup, Penzzer integrates natively with CI/CD pipelines and supports hardware-in-the-loop (HIL) testing scenarios. It provides a comprehensive dashboard for real-time monitoring, test orchestration, and trace analysis. This elevates it from a tool for experimentation to a platform suitable for regulated, production-grade environments.

Key differentiators of Penzzer include:

• Auto-discovery of protocol formats using machine learning assisted inference
• Modular plugin architecture for defining domain-specific oracles
• Native support for AUTOSAR SecOC and ISO/SAE 21434 reporting
• Built-in integrations with popular hardware interfaces (e.g., CAN adapters, I2C sensors)
• Automated baseline behavior learning to identify anomalies without pre-defined specifications

Compared to open-source tools like CaringCaribou or commercial fuzzers such as Peach or AFLNet, Penzzer offers deeper insight into embedded behavior by coupling software instrumentation with physical signal monitoring. While tools like AFLNet excel in protocol fuzzing at the packet level, and CaringCaribou focuses on exploratory testing, Penzzer unifies these strengths in a single platform that is both researcher-friendly and enterprise-ready.

With ongoing support for expanding communication standards (including CAN-FD, LIN, and automotive Ethernet), Penzzer is poised to become a cornerstone in the security validation of future mobility systems.