CVE-2026-44934 in Rancher AI Agent
Summary
by MITRE • 07/06/2026
A information disclosure when DEBUG loglevel is set in SUSE Rancher AI Agent 1.0 before 1.0.2 could leak API keys or LLM response text with potential sensitive data into logfiles, allowing local attackers to misuse respective gained data or credentials.
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Analysis
by VulDB Data Team • 07/06/2026
The vulnerability described represents a critical information disclosure weakness in the SUSE Rancher AI Agent 1.0 software ecosystem where improper logging configuration creates pathways for sensitive data exposure. This flaw specifically manifests when the system operates with DEBUG log level settings, which are typically intended for development and troubleshooting purposes but become dangerously exposed in production environments. The vulnerability stems from inadequate input sanitization and output filtering mechanisms within the logging subsystem that fail to properly redact or mask sensitive information before writing it to persistent storage locations. According to CWE-200, this classification encompasses information exposure through improper logging practices where system information is inadvertently disclosed to unauthorized parties.
The technical implementation of this vulnerability involves the agent's logging framework failing to distinguish between different types of data being processed and stored. API keys and large language model responses contain highly sensitive information that should never be persisted in plain text within log files, yet the debug logging mechanism indiscriminately captures and stores these elements without proper sanitization. This creates a persistent risk where local attackers with access to system logs can extract complete credential sets and potentially confidential information from LLM interactions that may include proprietary data, personal information, or strategic business insights. The vulnerability is particularly concerning because it leverages legitimate system functionality rather than exploiting external attack vectors, making it more difficult to detect and prevent.
Operational impact of this vulnerability extends beyond immediate credential compromise to encompass broader security implications within enterprise environments where SUSE Rancher AI Agent deployments are common. Local attackers who gain access to log files can effectively bypass authentication mechanisms by leveraging extracted API keys for unauthorized system access, potentially escalating privileges or accessing restricted resources. The exposure of LLM responses may contain sensitive business intelligence, customer data, or proprietary information that could be monetized or used for competitive advantage. From an ATT&CK framework perspective, this vulnerability maps to T1567.002 (Exfiltration Over Web Service) and T1078 (Valid Accounts) as attackers can use the compromised credentials to establish persistent access and exfiltrate additional sensitive data from the environment.
Mitigation strategies for this vulnerability must address both immediate remediation and long-term architectural improvements within the logging infrastructure. The primary recommendation involves implementing strict log sanitization policies that automatically redact or mask sensitive data types including API keys, authentication tokens, and potentially confidential LLM responses before any logging operations occur. System administrators should disable DEBUG logging in production environments and implement proper log rotation with access controls to prevent unauthorized viewing of historical logs. Additionally, organizations should deploy centralized logging solutions with built-in data loss prevention capabilities that can identify and alert on sensitive information patterns within log streams. The vulnerability demonstrates the critical importance of following security best practices such as those outlined in NIST SP 800-92 for log management and the principle of least privilege in system configuration to prevent unauthorized access to sensitive operational data. Regular security audits should verify that logging configurations adhere to established security policies and that sensitive information is properly handled throughout the system lifecycle.