CVE-2020-36244 in Diagnostic Log and Trace
Summary
by MITRE • 02/10/2021
The daemon in GENIVI Diagnostic Log and Trace (DLT) before 2.18.6 has a heap-based buffer overflow in dlt_buffer_write_block in shared/dlt_common.c.
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Analysis
by VulDB Data Team • 02/27/2021
The vulnerability identified as CVE-2020-36244 affects the GENIVI Diagnostic Log and Trace (DLT) daemon, a widely used diagnostic logging and tracing system in automotive applications. This system serves as a critical component for collecting and managing diagnostic data from vehicle systems, making it a prime target for attackers seeking to compromise automotive cybersecurity. The DLT daemon operates as a centralized logging service that aggregates diagnostic information from various vehicle components and applications, providing essential debugging and monitoring capabilities for automotive software development and maintenance processes. The vulnerability exists in the buffer management functionality of the daemon, specifically within the shared/dlt_common.c file where the dlt_buffer_write_block function handles data buffer operations.
The technical flaw manifests as a heap-based buffer overflow occurring in the dlt_buffer_write_block function, which is responsible for writing data blocks to memory buffers within the daemon's memory management system. This buffer overflow vulnerability arises when the daemon processes incoming diagnostic data that exceeds the allocated buffer size, allowing an attacker to write beyond the intended memory boundaries. The heap-based nature of the vulnerability means that the overflow occurs in the heap memory region, potentially enabling arbitrary code execution or system instability. The vulnerability stems from insufficient input validation and bounds checking within the buffer writing mechanism, where the daemon fails to properly verify the size of incoming data before attempting to write it to allocated memory blocks. This particular flaw represents a classic buffer overflow vulnerability that can be exploited through carefully crafted malicious input data that triggers the overflow condition during normal operation of the diagnostic logging service.
The operational impact of this vulnerability extends significantly within automotive environments where DLT systems are deployed, as it could enable remote code execution on vehicle systems that rely on this diagnostic infrastructure. Attackers could potentially exploit this vulnerability to gain unauthorized access to vehicle diagnostic systems, potentially leading to system compromise, data exfiltration, or disruption of critical vehicle functions. The vulnerability is particularly concerning in automotive contexts where diagnostic services are accessible over network interfaces, as it could be exploited by remote attackers without physical access to the vehicle. The potential for system instability and denial of service represents a direct threat to vehicle operational integrity, especially in scenarios where diagnostic data is critical for vehicle functionality and safety. The vulnerability affects all versions of the DLT daemon prior to 2.18.6, indicating that a significant number of automotive systems could be exposed to this risk, particularly in legacy vehicle deployments where software updates may not be regularly applied. This vulnerability aligns with CWE-121, which describes heap-based buffer overflow conditions, and demonstrates how improper memory management in embedded systems can create critical security risks.
The exploitation of this vulnerability can be analyzed through the lens of the attack chain framework, where the heap overflow could be leveraged as an initial access vector for more sophisticated attacks. Security researchers have identified that this type of vulnerability typically requires specific conditions to be met for successful exploitation, including the ability to send crafted input to the vulnerable daemon process. The ATT&CK framework categorizes this type of vulnerability under software exploitation techniques, where adversaries leverage memory corruption vulnerabilities to execute malicious code. Organizations implementing automotive cybersecurity measures should consider this vulnerability as part of their risk assessment for vehicle diagnostic systems, particularly when evaluating the security posture of connected vehicle platforms. The vulnerability highlights the importance of proper input validation and memory management in embedded systems, especially those operating in safety-critical environments where security failures can have severe consequences. Mitigation strategies should include immediate patching to version 2.18.6 or later, along with network segmentation to limit access to diagnostic services and implementation of intrusion detection systems to monitor for potential exploitation attempts. Additionally, organizations should conduct thorough vulnerability assessments of their automotive diagnostic infrastructure to identify any other potentially vulnerable components within their vehicle cybersecurity ecosystem.