CVE-2026-59301 in Spring Cloud Function
Summary
by MITRE • 08/27/2026
Potential for logging sensitive data in Spring Cloud Function Azure. Spring Cloud Function 5.0.0 - 5.0.3 Spring Cloud Function 4.3.0 - 4.3.4 Spring Cloud Function 4.2.0 - 4.2.7
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Analysis
by VulDB Data Team • 08/28/2026
The identified vulnerability in Spring Cloud Function versions ranging from 4.2.0 to 4.2.7, and from 4.3.0 to 5.0.3 when deployed on the Azure platform, represents a significant information disclosure risk rooted in improper logging practices. This flaw allows sensitive data to be inadvertently written to application logs during normal operation or error handling processes. In enterprise environments where applications are integrated with centralized log aggregation systems such as ELK Stack, Splunk, or Azure Monitor, this behavior can lead to the exposure of credentials, personally identifiable information, financial details, or other confidential payloads that were processed by the function. The issue is particularly concerning because logging frameworks often operate at various verbosity levels, and if sensitive fields are not explicitly excluded from log outputs before serialization, they become permanently recorded in persistent storage accessible to system administrators and potentially external attackers who gain access to these logs through misconfigured permissions or compromised monitoring accounts.
From a technical perspective, this vulnerability aligns with CWE-532, which classifies the insertion of sensitive information into log files as a software weakness. The root cause typically involves developers using standard logging methods that automatically serialize entire objects or request contexts without filtering out high-risk attributes such as authorization headers, session tokens, password fields, or database connection strings. In the context of Spring Cloud Function on Azure, this may occur during the binding phase where input data is mapped to function parameters, or when exception handling mechanisms capture stack traces and associated variable states for diagnostic purposes. Because cloud-native functions are often stateless and ephemeral, developers might assume that transient memory does not persist; however, the logging subsystem decouples execution from storage by writing outputs to external services, thereby creating a persistent record of what was intended to be temporary processing data. This architectural nuance amplifies the impact compared to traditional monolithic applications where logs might remain local and less frequently audited or exposed via network interfaces.
The operational impact of this vulnerability extends beyond simple privacy violations. It facilitates lateral movement within an organization’s infrastructure by providing attackers with valid authentication tokens or API keys that can be used to access downstream services, databases, or other microservices integrated into the same Azure environment. Furthermore, compliance frameworks such as GDPR, HIPAA, and PCI-DSS mandate strict controls over how sensitive data is handled and stored. The inadvertent logging of protected health information, credit card numbers, or user identities constitutes a direct violation of these regulatory requirements, potentially resulting in substantial legal penalties, mandatory breach notifications, and reputational damage. Attackers can exploit this through passive reconnaissance if they have any level of access to the log management platform, including compromised service accounts with read-only permissions on monitoring dashboards or exported log files stored in accessible blob storage containers associated with the Azure Function App.
To mitigate this risk, organizations must implement strict data sanitization protocols within their logging configurations. This involves configuring the underlying logging framework, such as Logback or SLF4J used by Spring Boot applications, to exclude specific fields from being serialized into log events. Developers should avoid passing entire objects containing sensitive attributes directly to logger methods and instead construct explicit message strings that omit high-risk data points. Additionally, implementing a custom converter for JSON serialization can ensure that annotated fields are masked or omitted during the logging process. On the Azure side, administrators should enforce least-privilege access controls on log analytics workspaces and storage accounts containing function logs, ensuring that only authorized personnel with a verified business need can view raw log entries. Regular audits of application code to identify potential data leakage points in error handling blocks are also essential. Upgrading to patched versions of Spring Cloud Function where this issue has been addressed is the most effective remediation strategy, as these updates typically include built-in safeguards or documentation highlighting safe logging patterns specific to cloud deployments.