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Cyber ResilienceSecurity teams have grown accustomed to the rapid weaponization of newly disclosed vulnerabilities. However, recent research from GreyNoise reveals that threat actors telegraph their intentions weeks before CVEs are even published. This discovery fundamentally changes how organizations should approach vulnerability management and threat intelligence.
The Value of Internal Logging Systems
Organizations implement centralized logging solutions like Elasticsearch/Kibana, Splunk, Datadog, and Sumo Logic to consolidate data from across their infrastructure. These platforms collect everything from application errors to authentication events, creating comprehensive repositories of organizational intelligence.
DTS Solution Red Team has discovered that these systems frequently contain credentials, API keys, connection strings, and other sensitive information that developers accidentally log during troubleshooting sessions. Our penetration testing methodology has identifies this attack vector as particularly effective because accessing logging platforms appears as legitimate user activity, rarely triggering security alerts.
Through HawkEye assessments, we’ve observed how these centralized logging implementations create attractive targets for several reasons: credential exposure through troubleshooting logs, operational security advantages that mimic legitimate user behavior, extensive infrastructure visibility, historical data spanning months or years, and the use of legitimate organizational tools that bypass traditional security controls.
Common Platform Vulnerabilities and Exploitation
Elasticsearch/Kibana Environments
Kibana provides visualization capabilities for Elasticsearch data, often serving as the central logging repository. Organizations frequently misconfigure these instances, leaving them accessible without authentication. HawkEye’s assessment methodology specifically tests for these misconfigurations by scanning for unauthenticated instances, testing default credentials, and identifying overly permissive access controls on dashboard interfaces.
Effective search patterns using Kibana Query Language include message:*password* OR message:*credential* OR message:*secret* for basic credential hunting, message:*api_key* OR message:*apikey* OR message:*api-key* for API key discovery, and message:*token* AND NOT (message:*validate* OR message:*invalid*) to filter out validation logs.
AWS-specific searches like message:*AKIA* OR tags:aws_access_key target cloud credentials that commonly appear in deployment logs.
Datadog and Unified Monitoring Platforms
Datadog combines metrics, traces, and logs into a unified platform. Organizations often grant broad access to development teams without implementing proper data sanitization.HawkEye’s assessments examine API key exposure in application traces, environment variable logging containing secrets, error messages with embedded connection strings, and deployment pipeline logs revealing infrastructure credentials.
Effective search patterns in Datadog include “password” OR “credential” OR “secret” for general credential discovery, “api_key” OR “apikey” OR “api-key” for API authentication tokens, “access_key” OR “accesskey” OR “access-key” for cloud service credentials, and “connection string” OR “connectionstring” for database access information. The platform’s Application Performance Monitoring features frequently capture sensitive data during API calls, database connections, and third-party service integrations, making these search patterns particularly effective.
Splunk Enterprise Implementations
Splunk installations commonly aggregate security logs, making them particularly valuable targets. The platform’s powerful search capabilities can be leveraged to identify authentication failures containing attempted credentials, network security device logs with configuration details, application deployment logs with embedded secrets, and system administration activities revealing privileged account information.
Technical Exploitation Methodologies
Discovery and Initial Access
HawkEye methodology begins with identifying accessible logging platforms through comprehensive network enumeration. Common discovery techniques include port scanning for default logging service ports, subdomain enumeration targeting logging-related hostnames, authentication bypass testing using default credentials, and certificate analysis revealing internal logging infrastructure.
Many organizations fail to properly secure these platforms, assuming their internal placement provides sufficient protection. This assumption proves false when attackers gain initial network access through other vectors such as phishing, VPN vulnerabilities, or wireless network compromises.
Credential Extraction and Analysis
Once access is obtained, systematic searching begins using carefully crafted queries that target specific credential patterns. DTS Solution approach focuses on CI/CD pipeline logs containing deployment credentials, error logs from failed authentication attempts, application startup logs with configuration parameters, and container orchestration logs revealing secrets.
Advanced attackers understand that logging platforms often contain historical data spanning months or years. This temporal depth means that even rotated credentials may still be discoverable in older log entries, providing multiple attack vectors and increasing the likelihood of finding valid authentication material.
Lateral Movement and Privilege Escalation
Discovered credentials enable lateral movement across the infrastructure. Recent assessments demonstrate how single credential discoveries can cascade into domain compromise through GitHub tokens providing access to private repositories, service account credentials enabling privilege escalation, database connection strings revealing critical data stores, and cloud provider keys allowing infrastructure manipulation.
Internal logging and monitoring systems have become critical infrastructure components for modern organizations. These platforms provide visibility into system operations, troubleshooting capabilities, and security insights. However, they also present significant attack vectors when improperly configured or when sensitive data inadvertently finds its way into log streams.
The Value of Internal Logging Systems
Organizations implement centralized logging solutions like Elasticsearch/Kibana, Splunk, Datadog, and Sumo Logic to consolidate data from across their infrastructure. These platforms collect everything from application errors to authentication events, creating comprehensive repositories of organizational intelligence.
DTS Solution Red Team has discovered that these systems frequently contain credentials, API keys, connection strings, and other sensitive information that developers accidentally log during troubleshooting sessions. Our penetration testing methodology has identifies this attack vector as particularly effective because accessing logging platforms appears as legitimate user activity, rarely triggering security alerts.
Through HawkEye assessments, we’ve observed how these centralized logging implementations create attractive targets for several reasons: credential exposure through troubleshooting logs, operational security advantages that mimic legitimate user behavior, extensive infrastructure visibility, historical data spanning months or years, and the use of legitimate organizational tools that bypass traditional security controls.
Common Platform Vulnerabilities and Exploitation
Elasticsearch/Kibana Environments
Kibana provides visualization capabilities for Elasticsearch data, often serving as the central logging repository. Organizations frequently misconfigure these instances, leaving them accessible without authentication. HawkEye’s assessment methodology specifically tests for these misconfigurations by scanning for unauthenticated instances, testing default credentials, and identifying overly permissive access controls on dashboard interfaces.
Effective search patterns using Kibana Query Language include message:*password* OR message:*credential* OR message:*secret* for basic credential hunting, message:*api_key* OR message:*apikey* OR message:*api-key* for API key discovery, and message:*token* AND NOT (message:*validate* OR message:*invalid*) to filter out validation logs. AWS-specific searches like message:*AKIA* OR tags:aws_access_key target cloud credentials that commonly appear in deployment logs.
Datadog and Unified Monitoring Platforms
Datadog combines metrics, traces, and logs into a unified platform. Organizations often grant broad access to development teams without implementing proper data sanitization. HawkEye’s assessments examine API key exposure in application traces, environment variable logging containing secrets, error messages with embedded connection strings, and deployment pipeline logs revealing infrastructure credentials.
Effective search patterns in Datadog include “password” OR “credential” OR “secret” for general credential discovery, “api_key” OR “apikey” OR “api-key” for API authentication tokens, “access_key” OR “accesskey” OR “access-key” for cloud service credentials, and “connection string” OR “connectionstring” for database access information. The platform’s Application Performance Monitoring features frequently capture sensitive data during API calls, database connections, and third-party service integrations, making these search patterns particularly effective.
Splunk Enterprise Implementations
Splunk installations commonly aggregate security logs, making them particularly valuable targets. The platform’s powerful search capabilities can be leveraged to identify authentication failures containing attempted credentials, network security device logs with configuration details, application deployment logs with embedded secrets, and system administration activities revealing privileged account information.
Technical Exploitation Methodologies
Discovery and Initial Access
HawkEye methodology begins with identifying accessible logging platforms through comprehensive network enumeration. Common discovery techniques include port scanning for default logging service ports, subdomain enumeration targeting logging-related hostnames, authentication bypass testing using default credentials, and certificate analysis revealing internal logging infrastructure.
Many organizations fail to properly secure these platforms, assuming their internal placement provides sufficient protection. This assumption proves false when attackers gain initial network access through other vectors such as phishing, VPN vulnerabilities, or wireless network compromises.
Credential Extraction and Analysis
Once access is obtained, systematic searching begins using carefully crafted queries that target specific credential patterns. DTS Solution approach focuses on CI/CD pipeline logs containing deployment credentials, error logs from failed authentication attempts, application startup logs with configuration parameters, and container orchestration logs revealing secrets.
Advanced attackers understand that logging platforms often contain historical data spanning months or years. This temporal depth means that even rotated credentials may still be discoverable in older log entries, providing multiple attack vectors and increasing the likelihood of finding valid authentication material.
Lateral Movement and Privilege Escalation
Discovered credentials enable lateral movement across the infrastructure. Recent assessments demonstrate how single credential discoveries can cascade into domain compromise through GitHub tokens providing access to private repositories, service account credentials enabling privilege escalation, database connection strings revealing critical data stores, and cloud provider keys allowing infrastructure manipulation.
Real-World Attack Scenarios
Scenario 1: Financial Services Firm
Penetration testers discovered an unauthenticated Kibana instance containing CI/CD pipeline logs. Searching revealed a GitHub Personal Access Token in deployment error logs. The token provided access to private repositories containing plaintext DevOps platform credentials. These credentials led to LDAP configuration access, service account capture through authentication coercion, and ultimately full domain compromise via privilege escalation chains.
Scenario 2: E-commerce Platform
Datadog APM traces exposed AWS API keys logged during troubleshooting sessions. The overprivileged credentials enabled reconnaissance of cloud infrastructure, security group modifications, and access to customer payment data stored in RDS instances. The compromise originated from a single exposed credential in application monitoring traces.
HawkEye Configuration Assessment Framework
HawkEye addresses logging infrastructure misconfigurations through automated testing that validates access controls across logging interfaces, identifies overly permissive search capabilities, and tests for unauthenticated access points. The platform performs automated scanning for credential patterns in log data while analyzing historical information for long-term exposure risks.
Configuration security reviews examine dashboard configurations for hardcoded secrets, validate data retention policies, and assess log forwarding security implementations. This comprehensive approach ensures that organizations understand their complete exposure profile across all logging infrastructure components.
Defense Strategies and Implementation
Access Control and Authentication
Organizations must implement strict role-based access controls on logging platforms combined with multi-factor authentication for administrative interfaces. Regular access reviews should be conducted to ensure that permissions remain appropriate as personnel roles change over time.
Network segmentation provides additional protection by isolating logging systems from general corporate networks. This approach limits attacker movement even when initial compromise occurs through other vectors.
Data Sanitization and Monitoring
Automated scrubbing tools prevent credential logging by implementing regex-based filtering for known credential patterns. Application logging frameworks should include built-in sanitization capabilities that activate by default rather than requiring manual configuration.
Monitoring suspicious search patterns in logging platforms helps detect potential compromise attempts. Organizations should configure alerts for bulk data exports, unusual query patterns, and access from unexpected locations or times.
Operational Security Measures
Log data classification based on sensitivity levels enables appropriate retention policies for different information types. Regular purging of logs containing sensitive information reduces the window of exposure while maintaining necessary operational visibility.
Integration with Security Operations Center workflows ensures that logging platforms connect with SIEM systems for correlation with security events. Automated incident response capabilities should trigger when credential exposure alerts activate.
Conclusion
Internal logging and monitoring services represent both critical infrastructure and significant attack vectors. DTS Solution Red Team consistently demonstrates how these systems can provide rapid paths to comprehensive infrastructure compromise. Organizations must balance the visibility benefits of centralized logging with the security risks of credential exposure.
HawkEye assessments reveal that the majority of organizations have significant security gaps in their logging infrastructure. These gaps range from basic access control failures to complex data exposure scenarios involving multiple interconnected systems.
Effective security requires treating logging infrastructure with the same rigor as other critical systems. This includes regular security assessments, proper access controls, data sanitization, and monitoring for suspicious activities. Organizations that fail to secure their logging infrastructure essentially provide attackers with roadmaps to their most critical assets.
The path forward involves implementing comprehensive logging security programs that address both technical and operational aspects. This includes automated tools for credential detection, proper configuration management, and regular assessment of logging infrastructure security posture. Only through such comprehensive approaches can organizations realize the benefits of centralized logging while minimizing associated security risks.
Real-World Attack Scenarios
Scenario 1: Financial Services Firm
Penetration testers discovered an unauthenticated Kibana instance containing CI/CD pipeline logs. Searching revealed a GitHub Personal Access Token in deployment error logs. The token provided access to private repositories containing plaintext DevOps platform credentials. These credentials led to LDAP configuration access, service account capture through authentication coercion, and ultimately full domain compromise via privilege escalation chains.
Scenario 2: E-commerce Platform
Datadog APM traces exposed AWS API keys logged during troubleshooting sessions. The overprivileged credentials enabled reconnaissance of cloud infrastructure, security group modifications, and access to customer payment data stored in RDS instances. The compromise originated from a single exposed credential in application monitoring traces.
HawkEye Configuration Assessment Framework
HawkEye addresses logging infrastructure misconfigurations through automated testing that validates access controls across logging interfaces, identifies overly permissive search capabilities, and tests for unauthenticated access points. The platform performs automated scanning for credential patterns in log data while analyzing historical information for long-term exposure risks.
Configuration security reviews examine dashboard configurations for hardcoded secrets, validate data retention policies, and assess log forwarding security implementations. This comprehensive approach ensures that organizations understand their complete exposure profile across all logging infrastructure components.
Defense Strategies and Implementation
Access Control and Authentication
Organizations must implement strict role-based access controls on logging platforms combined with multi-factor authentication for administrative interfaces. Regular access reviews should be conducted to ensure that permissions remain appropriate as personnel roles change over time.
Network segmentation provides additional protection by isolating logging systems from general corporate networks. This approach limits attacker movement even when initial compromise occurs through other vectors.
Data Sanitization and Monitoring
Automated scrubbing tools prevent credential logging by implementing regex-based filtering for known credential patterns. Application logging frameworks should include built-in sanitization capabilities that activate by default rather than requiring manual configuration.
Monitoring suspicious search patterns in logging platforms helps detect potential compromise attempts. Organizations should configure alerts for bulk data exports, unusual query patterns, and access from unexpected locations or times.
Operational Security Measures
Log data classification based on sensitivity levels enables appropriate retention policies for different information types. Regular purging of logs containing sensitive information reduces the window of exposure while maintaining necessary operational visibility.
Integration with Security Operations Center workflows ensures that logging platforms connect with SIEM systems for correlation with security events. Automated incident response capabilities should trigger when credential exposure alerts activate.
Conclusion
Internal logging and monitoring services represent both critical infrastructure and significant attack vectors. DTS Solution Red Team consistently demonstrates how these systems can provide rapid paths to comprehensive infrastructure compromise. Organizations must balance the visibility benefits of centralized logging with the security risks of credential exposure.
HawkEye assessments reveal that the majority of organizations have significant security gaps in their logging infrastructure. These gaps range from basic access control failures to complex data exposure scenarios involving multiple interconnected systems.
Effective security requires treating logging infrastructure with the same rigor as other critical systems. This includes regular security assessments, proper access controls, data sanitization, and monitoring for suspicious activities. Organizations that fail to secure their logging infrastructure essentially provide attackers with roadmaps to their most critical assets.
The path forward involves implementing comprehensive logging security programs that address both technical and operational aspects. This includes automated tools for credential detection, proper configuration management, and regular assessment of logging infrastructure security posture. Only through such comprehensive approaches can organizations realize the benefits of centralized logging while minimizing associated security risks.
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