Description

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

Executive Summary

Technical Details

Mitigations

References

Additional Metadata

{
  "score": "3.1",
  "pubDate": "2026-08-27T20:17:56.407Z",
  "pubdate": "2026-08-27T20:17:56.407Z",
  "executiveSummary": "A security vulnerability exists within Spring Cloud Function Azure whereby sensitive data may be inadvertently captured and written to application logs. This vulnerability concerns the improper handling of request or response data during the invocation process, potentially exposing sensitive information such as credentials, PII, or internal system details to logging frameworks.\nThe vulnerability affects multiple branches of Spring Cloud Function, specifically versions 5.0.0 through 5.0.3, 4.3.0 through 4.3.4, and 4.2.0 through 4.2.7. The impact is significant as log files are often ingested by centralized logging systems or SIEM platforms, broadening the scope of data exposure beyond the local environment.\nAttackers capable of triggering function executions with specifically crafted payloads may be able to force the application to log sensitive request parameters or headers. Exploitation does not necessarily require high-level privileges, as the exposure occurs during standard function processing. Organizations relying on default logging configurations are at the highest risk, as sensitive data is persisted in plaintext within log files, which can then be accessed by unauthorized personnel or extracted via secondary security misconfigurations.",
  "technicalDetails": "The root cause of this vulnerability lies in the logging implementation within the Azure adapter for Spring Cloud Function. During the processing of incoming requests or outgoing responses, the framework inappropriately logs full object representations or message headers that contain sensitive information, failing to redact or sanitize data before passing it to the logging abstraction layer.\nIn the context of the Azure function integration, the framework intercepts incoming triggers via the Azure Functions runtime. During the binding and transformation process, the underlying code path invokes logging statements that include the raw content of the function execution context. When specific input payloads are provided, the logger captures these payloads in their entirety rather than extracting only metadata or sanitized fields.\nThe exploitation flow proceeds as follows: 1) An attacker identifies an exposed endpoint or trigger mechanism for the Spring Cloud Function. 2) The attacker crafts a request containing sensitive data, such as authentication tokens, session identifiers, or personal data, often placed within headers or unconventional fields that the framework attempts to parse. 3) The function is triggered, and the Spring Cloud Function Azure adapter processes the input. 4) The adapter fails to apply filtering logic to the incoming message object and passes the raw, sensitive input to the configured logger. 5) The sensitive data is subsequently written to the stdout or file-based logging destination managed by the Azure Functions environment.\nThis behavior is prevalent in versions 5.0.0-5.0.3, 4.3.0-4.3.4, and 4.2.0-4.2.7. The exposure is exacerbated when logging levels are set to DEBUG or TRACE, which significantly increases the volume of captured data. Since these logs are often exported to external storage (e.g., Azure Monitor, Log Analytics, or third-party log aggregators), the sensitive data becomes part of a long-term data store with its own potential access control vulnerabilities. Because this is an inherent handling issue within the framework logic, the vulnerability exists regardless of the business logic implemented within the function itself. Authentication requirements for exploitation depend on the function's access restrictions; however, even public functions are susceptible to automated log-filling attacks designed to harvest information from logs."
}