CVE-2023-26257 in dlt-daemon
Summary
by MITRE • 02/27/2023
An issue was discovered in the Connected Vehicle Systems Alliance (COVESA; formerly GENIVI) dlt-daemon through 2.18.8. Dynamic memory is not released after it is allocated in dlt-control-common.c.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 11/08/2025
The vulnerability identified as CVE-2023-26257 resides within the dlt-daemon component of the Connected Vehicle Systems Alliance (COVESA) platform, formerly known as GENIVI. This critical memory management flaw affects versions through 2.18.8 and represents a classic case of memory leak vulnerability that can severely impact automotive cybersecurity and system reliability. The dlt-daemon serves as a crucial logging and diagnostics tool within automotive systems, facilitating communication between various vehicle components and external diagnostic tools. Given its central role in vehicle diagnostics and data logging, any vulnerability in this component can have far-reaching implications for both vehicle functionality and security posture.
The technical flaw manifests in the dlt-control-common.c source file where dynamic memory allocation occurs without subsequent deallocation, creating a persistent memory leak condition. This memory management error falls under the category of CWE-401: Improper Release of Memory and represents a fundamental failure in resource management practices. The vulnerability occurs during normal operational procedures when the daemon allocates memory for control operations but fails to properly free this memory after use, leading to progressive memory consumption over time. This type of memory leak is particularly concerning in automotive environments where system resources are finite and reliable operation is paramount for safety-critical functions.
The operational impact of this vulnerability extends beyond simple performance degradation to potentially compromising vehicle system stability and security. As memory leaks accumulate over time, the daemon's memory usage grows indefinitely, which can lead to system slowdowns, unexpected restarts, or even complete system failure in resource-constrained automotive environments. The vulnerability creates a persistent state where the daemon consumes increasing amounts of memory, potentially starving other critical automotive services of necessary resources. This memory exhaustion scenario could be exploited by attackers to cause denial-of-service conditions or to create opportunities for additional attacks through system instability. The vulnerability aligns with ATT&CK technique T1499.001: Network Denial of Service, as it can contribute to service unavailability through resource exhaustion.
Mitigation strategies for CVE-2023-26257 require immediate attention from automotive manufacturers and system integrators. The primary remediation involves updating to a patched version of the dlt-daemon component that properly implements memory deallocation following allocation. Organizations should conduct comprehensive vulnerability assessments to identify all systems running affected versions and prioritize patch deployment. Additionally, implementing memory monitoring and alerting mechanisms can help detect memory leak progression before it reaches critical levels. The vulnerability underscores the importance of proper memory management practices in automotive cybersecurity frameworks and highlights the need for rigorous code review processes that specifically address resource management in safety-critical systems. Security teams should also consider implementing runtime memory protection mechanisms and establishing baseline memory usage monitoring to detect anomalous behavior that may indicate memory leak conditions.