CVE-2026-59302 in Spring Cloud Stream
Summary
by MITRE • 08/27/2026
Potential for logging sensitive data in Spring Cloud Stream. Spring Cloud Stream 5.0.0 - 5.0.2 Spring Cloud Stream 4.3.0 - 4.3.3 Spring Cloud Stream 4.2.0 - 4.2.6
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Analysis
by VulDB Data Team • 08/28/2026
Spring Cloud Stream versions ranging from 4.2.0 to 4.2.6, 4.3.0 to 4.3.3, and 5.0.0 to 5.0.2 contain a vulnerability that allows for the unintended logging of sensitive data within application logs. This issue stems from how the framework handles message metadata or payload content during internal processing operations. When messages are processed through various bindings and processors, certain implementations may inadvertently include confidential information in log statements intended for debugging or operational monitoring purposes. The flaw is not necessarily a result of malicious intent but rather an oversight in sanitizing output streams before they are written to standard logging frameworks such as Logback or Log4j2 which are commonly integrated into Spring Boot applications.
The technical root cause involves the serialization and string representation of message objects during trace-level logging or when specific diagnostic flags are enabled. In some configurations, the toString method or similar debugging utilities may expose internal state variables that contain user credentials, personally identifiable information, or other proprietary data transmitted via Kafka, RabbitMQ, or other supported messaging protocols. Because Spring Cloud Stream acts as an abstraction layer over these message brokers, it processes incoming and outgoing messages through a series of interceptors and handlers. If any component in this chain logs the full context of a message without filtering out sensitive fields, that information becomes permanently recorded in log files unless explicitly purged or rotated away.
The operational impact of this vulnerability is significant for organizations relying on centralized logging solutions like ELK Stack, Splunk, or cloud-based monitoring services such as AWS CloudWatch or Azure Monitor. Since logs are often aggregated and stored for long-term retention to aid in troubleshooting and compliance auditing, the exposure of sensitive data can lead to direct security breaches. Attackers who gain access to log storage systems could extract credentials, session tokens, or private business logic details embedded within message payloads. This aligns with CWE-532 which classifies information exposure through log files as a distinct category of software defects. The risk is exacerbated in environments where logs are accessible by multiple teams including DevOps, support staff, and third-party monitoring providers who may not have the same security clearance levels as development engineers.
Furthermore, this vulnerability facilitates potential data leakage that can be exploited for further attacks such as credential stuffing or social engineering if sensitive identifiers are exposed. It also poses a compliance risk under regulations like GDPR, HIPAA, or PCI-DSS which mandate strict controls over how personal and financial data is handled and stored. The lack of automatic sanitization means that even well-intentioned developers enabling verbose logging for debugging purposes can inadvertently create security liabilities. This behavior reflects a common pattern in modern microservices architectures where observability features are prioritized without sufficient consideration for data privacy boundaries, often correlating with ATT&CK technique T1078 which involves valid accounts and credentials being used to maintain persistence or access sensitive resources indirectly through information gathering.
Mitigation strategies primarily involve upgrading to patched versions of Spring Cloud Stream that address the logging behavior by implementing stricter filtering mechanisms before data is written to log outputs. Organizations should also review their current application configurations to ensure that debug-level logging is disabled in production environments unless absolutely necessary and strictly controlled. Implementing structured logging with explicit field masking or redaction policies can help prevent sensitive fields from being serialized into logs regardless of the underlying framework version. Additionally, security teams should audit existing log storage systems for any previously recorded instances of exposed data and rotate affected credentials immediately if compromise is suspected. Regular penetration testing focused on information disclosure vectors will further ensure that logging practices remain secure as applications evolve.