CVE-2016-0929 in Cloud Foundry
Summary
by MITRE
The metrics-collection component in RabbitMQ for Pivotal Cloud Foundry (PCF) 1.6.x before 1.6.4 logs command lines of failed commands, which might allow context-dependent attackers to obtain sensitive information by reading the log data, as demonstrated by a syslog message that contains credentials from a command line.
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Analysis
by VulDB Data Team • 04/18/2019
The vulnerability identified as CVE-2016-0929 resides within the metrics-collection component of RabbitMQ for Pivotal Cloud Foundry version 1.6.x prior to 1.6.4. This flaw represents a significant security concern as it enables unauthorized information disclosure through log data manipulation. The issue manifests when the system logs command lines of failed commands, creating an attack surface where sensitive data can be inadvertently exposed to malicious actors who gain access to log files. The vulnerability specifically affects environments running Pivotal Cloud Foundry versions that have not been patched beyond 1.6.3, making it a critical concern for organizations utilizing these older versions of the platform.
The technical implementation of this vulnerability stems from insufficient input sanitization within the metrics collection subsystem. When commands fail during execution, the system automatically captures and logs the complete command line including all arguments and parameters passed to the command. This logging mechanism does not adequately filter or redact sensitive information such as authentication credentials, API keys, or other confidential data that might be present in command line arguments. The flaw directly aligns with CWE-209, which describes the issue of information exposure through log data, and represents a classic example of improper output filtering in security-critical systems. The vulnerability operates under the principle that logging should never contain sensitive information without proper sanitization, yet the implementation fails to enforce this security control.
From an operational perspective, this vulnerability creates substantial risk for organizations using Pivotal Cloud Foundry environments where RabbitMQ metrics collection is enabled. Attackers who can access system logs or syslog messages can extract sensitive credentials from failed command executions, potentially gaining access to additional systems or services that rely on those credentials. The impact extends beyond simple credential theft as these extracted credentials could enable lateral movement within networks, privilege escalation, or access to other critical systems that share authentication mechanisms. The vulnerability particularly affects environments where automated monitoring and metrics collection are enabled, as these systems typically run with elevated privileges and may execute commands containing sensitive information. This issue directly maps to ATT&CK technique T1070.004, which involves the use of log data for credential access and information gathering.
Organizations should immediately implement mitigations to address this vulnerability by upgrading to RabbitMQ for Pivotal Cloud Foundry version 1.6.4 or later, which contains the necessary patches to prevent command line logging of sensitive information. Additionally, system administrators should review existing log files and remove any sensitive data that may have been previously exposed through this vulnerability. Implementing proper log sanitization policies that filter out command line arguments containing sensitive information represents a critical security control that should be enforced across all system components. Organizations should also consider implementing log access controls and monitoring to detect unauthorized access to log files, as well as establishing procedures for regular log audits to identify potential exposure of sensitive data. The remediation process should include comprehensive testing to ensure that the patch does not introduce compatibility issues with existing monitoring or automation systems, while also verifying that proper logging controls are in place to prevent similar vulnerabilities from emerging in other components of the platform.