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The paper introduces Patchlings, a hotpatching framework tailored for automotive microcontrollers that addresses the limitations of existing solutions in terms of regulatory compliance, safety validation, and support for flash-based XIP architectures. Patchlings achieves safety by employing a novel approach to patching that preserves the integrity of safety-critical code regions and reduces the need for extensive re-validation. Evaluation on an automotive-grade platform demonstrates low overhead (3.3 $\mu$s patch application) and small firmware size increase (6.34%), while successfully patching real CVEs.
Patchlings slashes vulnerability mitigation time in automotive ECUs, offering a hotpatching solution that finally meets stringent safety standards without sacrificing performance.
The increasing presence of software in modern automobiles has created a growing need to deliver software updates throughout a vehicle's entire lifespan. Traditional update methods are slow and require months of re-validation to comply with stringent safety standards like ISO 26262. Although hotpatching offers a path to faster updates, existing solutions for real-time embedded systems are unsuitable for the automotive domain: they overlook regulatory compliance, demand extensive safety validation, and lack support for the flash-based Execute-in-Place (XIP) architecture commonly used in automotive electronic control units (ECUs). We introduce Patchlings, the first hotpatching framework designed for compliance, safety, and persistence in automotive systems. It fills the gap in applying hotpatching to automotive systems and fundamentally reduces the mean-time-to-mitigate (MTTM) for vulnerabilities and bugs. We implement and evaluate a complete prototype of Patchlings on an automotive-grade hardware platform, NXP S32K148EVB, with both FreeRTOS and Zephyr. Our results demonstrate low and deterministic overhead (e.g., 3.3 $\mu$s when a patch is applied), small firmware size increase (e.g., as low as 6.34%), and successful patching of different types of real CVEs, proving its real-world applicability and effectiveness.