mitigations.md

Mitigation Strategies Analysis for google/guava

Mitigation Strategy: Maintain Up-to-Date Guava Version

• Mitigation Strategy: Keep Guava Library Updated
• Description:
  1. Regularly Check for Guava Updates: Set a recurring schedule (e.g., monthly) to check for new Guava releases on Maven Central or Guava's official GitHub repository. Focus specifically on Guava library updates.
  2. Subscribe to Guava Security Notifications (if available): Check if Guava project provides any specific security mailing lists or notification channels. Subscribe to them to receive direct alerts about Guava vulnerabilities.
  3. Review Guava Release Notes: When a new Guava version is available, carefully review Guava's release notes, paying close attention to security fixes and vulnerability disclosures specifically mentioned for Guava.
  4. Update Guava Dependency: Update the Guava dependency version in your project's dependency management file (e.g., pom.xml for Maven, build.gradle for Gradle) to the latest stable Guava version.
  5. Test Guava Integration: After updating Guava, run comprehensive unit, integration, and system tests to ensure compatibility with the new Guava version and that no regressions are introduced in code that uses Guava functionalities.
• List of Threats Mitigated:
  • Known Guava Vulnerabilities (High Severity): Exploits of publicly disclosed vulnerabilities specifically within the Guava library in older versions. Severity is high as these vulnerabilities are often well-documented and easily exploitable.
• Impact:
  • Known Guava Vulnerabilities (High): High impact. Directly addresses and eliminates known, potentially critical vulnerabilities within Guava itself.
• Currently Implemented:
  • Partially Implemented: Dependency version is managed using Maven in pom.xml. Developers generally update dependencies when new features are needed, but proactive security-focused updates of Guava are not consistently scheduled.
  • Location: Project's pom.xml file and developer's update workflow.
• Missing Implementation:
  • Automated Guava Update Checks: Lack of automated tools or processes to regularly check specifically for and alert on new Guava releases, especially security-related updates for Guava.
  • Scheduled Guava Security Updates: No formal schedule or policy for proactively updating Guava dependency specifically for security reasons.
  • Subscription to Guava Security Notifications: Team is not actively subscribed to Guava specific security mailing lists or vulnerability databases to receive timely notifications about Guava vulnerabilities.

Mitigation Strategy: Secure Serialization Practices (and Avoiding Direct Guava Object Serialization)

• Mitigation Strategy: Minimize and Secure Serialization/Deserialization of Guava Objects
• Description:
  1. Prefer DTOs over Guava Objects for Serialization: When transferring data, especially over networks or storing it persistently, prefer using simple Data Transfer Objects (DTOs) that represent your application's data model instead of directly serializing Guava collection types or other complex Guava objects.
  2. Use Secure Serialization Formats (Especially with Guava Objects): If serialization of Guava objects is necessary, opt for safer formats like JSON or Protocol Buffers over Java's native serialization, especially when dealing with untrusted data. These formats are generally less susceptible to deserialization vulnerabilities compared to Java serialization of complex objects like those in Guava.
  3. Input Validation on Deserialized Data (Including Data Populating Guava Objects): Regardless of the serialization format, always perform thorough input validation on data received after deserialization, especially when this data will be used to populate Guava objects after deserialization. Validate data types, ranges, and formats to prevent injection attacks or unexpected behavior when working with Guava objects.
  4. Object Input Stream Filtering (Java Serialization of Guava Objects - if unavoidable): If Java serialization of Guava objects is unavoidable when dealing with untrusted data, utilize Java's Object Input Stream Filtering (available in newer Java versions) to restrict the classes that can be deserialized, mitigating deserialization gadget attacks that might target Guava classes or classes used within Guava.
• List of Threats Mitigated:
  • Deserialization Vulnerabilities related to Guava Objects (High Severity): Prevents or mitigates deserialization vulnerabilities that could arise from directly deserializing Guava objects, especially when handling untrusted data. These vulnerabilities can lead to Remote Code Execution (RCE) if Guava classes are involved in gadget chains.
  • Information Leakage from Serialized Guava Objects (Medium Severity): Reduces the risk of unintentionally exposing internal implementation details of Guava objects through serialization, which could potentially be exploited.
• Impact:
  • Deserialization Vulnerabilities related to Guava Objects (High): High impact. Significantly reduces the risk of critical deserialization vulnerabilities, especially if the application handles untrusted serialized data and potentially deserializes Guava objects.
  • Information Leakage from Serialized Guava Objects (Medium): Medium impact. Reduces the risk of information leakage from Guava object serialization, although the direct exploitability might be lower than RCE.
• Currently Implemented:
  • Partially Implemented: JSON is primarily used for API communication and data persistence, which is inherently safer than Java serialization. DTOs are generally used for data transfer within the application, reducing direct serialization of complex Guava objects.
  • Location: API endpoints, data persistence layers, internal service communication.
• Missing Implementation:
  • Explicit Policy Against Direct Guava Object Serialization: No explicit coding guidelines or policies discouraging direct serialization of complex Guava objects, especially when handling external data.
  • Object Input Stream Filtering (Java Serialization of Guava Objects): If Java serialization is used in any legacy components or internal processes and potentially involves Guava objects, Object Input Stream Filtering is not implemented to restrict deserialization classes.
  • Formal Deserialization Security Review (Focus on Guava Objects): No formal security review process specifically focusing on deserialization practices and potential vulnerabilities related to Guava object serialization.

Mitigation Strategy: Regular Expression Denial of Service (ReDoS) Prevention with Guava CharMatcher and Splitter

• Mitigation Strategy: ReDoS Resistant Regular Expression Practices with Guava Utilities
• Description:
  1. Regex Complexity Review in Guava Usage: Carefully review all regular expressions used specifically with Guava's CharMatcher and Splitter, especially those processing user-supplied input. Identify potentially complex or nested regex patterns that could be vulnerable to ReDoS when used with these Guava utilities.
  2. Regex Performance Testing with Guava Utilities: Test regular expressions used with Guava's CharMatcher and Splitter against a variety of inputs, including long strings, strings with repeating patterns, and edge cases. Use online regex testers or dedicated tools to measure execution time and identify potential performance bottlenecks or ReDoS vulnerabilities in the context of Guava utility usage.
  3. Regex Simplification for Guava Usage: Simplify complex regular expressions used with Guava's CharMatcher and Splitter where possible. Break down complex regex into simpler, more efficient patterns or use alternative string manipulation methods if feasible within the Guava utility context.
  4. Input Length Limits for Guava Regex Processing: Implement input length limits for strings processed by regular expressions used with Guava's CharMatcher and Splitter, especially when dealing with user-provided input. This can limit the maximum execution time even if a ReDoS vulnerability exists when using Guava regex utilities.
  5. Timeouts for Guava Regex Execution: In critical sections where Guava's CharMatcher or Splitter are used with regular expressions, consider implementing timeouts for regular expression execution to prevent unbounded CPU consumption in case of ReDoS attacks.
• List of Threats Mitigated:
  • Regular Expression Denial of Service (ReDoS) via Guava Utilities (Medium to High Severity): Prevents or mitigates ReDoS attacks that can exhaust server resources (CPU) by exploiting poorly designed regular expressions used with Guava's CharMatcher and Splitter. Severity depends on the application's resource limits and the impact of service disruption.
• Impact:
  • Regular Expression Denial of Service (Medium to High): Medium to High impact. Reduces the risk of service disruption due to ReDoS attacks originating from Guava utility usage, improving application availability and stability.
• Currently Implemented:
  • Partially Implemented: Developers are generally aware of performance considerations when writing code, but specific ReDoS awareness and proactive regex testing in the context of Guava's CharMatcher and Splitter are not consistently practiced.
  • Location: Codebase where CharMatcher and Splitter are used with regular expressions, primarily in input processing and data parsing modules that utilize Guava.
• Missing Implementation:
  • ReDoS Awareness Training (Guava Specific): Lack of formal training or guidelines for developers on ReDoS vulnerabilities and secure regex design specifically in the context of using Guava's CharMatcher and Splitter.
  • Automated ReDoS Testing (Guava Context): No automated tools or processes to specifically test regular expressions used with Guava's CharMatcher and Splitter for ReDoS vulnerabilities during development or testing phases.
  • Regex Complexity Analysis (Guava Usage): No tools or processes to automatically analyze regex complexity and flag potentially vulnerable patterns specifically when used with Guava's CharMatcher and Splitter.

Mitigation Strategy: Secure Guava Cache Configuration and Usage

• Mitigation Strategy: Secure Caching Practices with Guava Cache
• Description:
  1. Review Guava Cache Configuration: Carefully review the configuration of all Guava caches used in the application, especially those storing sensitive data. Pay attention to expiration policies (time-based, size-based), maximum size, and eviction strategies configured for Guava caches.
  2. Minimize Caching of Sensitive Data in Guava Cache: Avoid caching highly sensitive data in Guava caches if possible. If caching is necessary, consider the sensitivity level and potential impact of data leakage from the Guava cache.
  3. Encrypt Sensitive Data in Guava Cache (If Possible): If Guava's caching implementation or a wrapper allows for encryption at rest within the cache, implement encryption for sensitive data to protect it from unauthorized access if the Guava cache storage is compromised.
  4. Implement Access Control for Guava Cache: If the Guava cache is accessible from different parts of the application or different user contexts, implement access control mechanisms to restrict access to authorized components or users only for the Guava cache.
  5. Regularly Clear Guava Cache (Sensitive Data): For Guava caches containing sensitive data, consider implementing a mechanism to periodically clear the cache, especially when data sensitivity is time-bound or access is no longer needed in the Guava cache.
• List of Threats Mitigated:
  • Information Leakage through Guava Cache (Medium to High Severity): Prevents unauthorized access to sensitive data stored in the Guava cache due to misconfiguration, excessive caching, or lack of access control. Severity depends on the sensitivity of the cached data.
  • Excessive Memory Consumption by Guava Cache (Medium Severity): Prevents unbounded Guava cache growth due to misconfiguration, leading to potential Denial of Service due to memory exhaustion.
• Impact:
  • Information Leakage through Guava Cache (Medium to High): Medium to High impact. Reduces the risk of sensitive data leakage from the Guava cache, protecting confidentiality.
  • Excessive Memory Consumption by Guava Cache (Medium): Medium impact. Improves application stability and prevents potential denial of service due to uncontrolled Guava cache growth.
• Currently Implemented:
  • Partially Implemented: Guava caches are used for performance optimization in several modules. Expiration policies are generally configured, but security considerations for cached data in Guava caches are not consistently addressed.
  • Location: Caching modules in various services and components of the application that utilize Guava Cache.
• Missing Implementation:
  • Security Review of Guava Cache Configurations: No formal security review process specifically focusing on Guava cache configurations and potential security implications.
  • Data Sensitivity Classification for Guava Caching: No formal classification of data sensitivity to guide caching decisions and security measures specifically for Guava caches.
  • Encryption for Sensitive Data in Guava Cache: Encryption at rest for sensitive data in Guava caches is not implemented.
  • Access Control for Guava Cache Access: Access control mechanisms for Guava caches are not consistently implemented, potentially allowing broader access than necessary.

Mitigation Strategy: Input Validation Before Using Guava Utilities

• Mitigation Strategy: Validate Inputs Before Guava Utility Usage
• Description:
  1. Identify Guava Utility Input Sources: Identify all points in the code where user-provided input or data from external systems is used as input to Guava utility methods (e.g., Splitter, Joiner, CharMatcher, collection utilities).
  2. Implement Input Validation Before Guava Utility Usage: Before passing input data to Guava utilities, implement robust input validation. Validate data types, formats, ranges, lengths, and character sets according to expected values and security requirements before using Guava utilities.
  3. Sanitize Inputs (If Necessary) Before Guava Utility Usage: If input data needs to be sanitized before being used with Guava utilities (e.g., escaping special characters), implement appropriate sanitization techniques to prevent injection vulnerabilities or unexpected behavior when using Guava utilities.
  4. Handle Validation Errors Gracefully (Guava Utility Context): Implement proper error handling for input validation failures that occur before using Guava utilities. Return informative error messages to the user or log errors appropriately for debugging and security monitoring related to Guava utility input validation.
• List of Threats Mitigated:
  • Injection Vulnerabilities via Guava Utility Misuse (High to Critical Severity): Prevents injection vulnerabilities (e.g., Command Injection, SQL Injection, Cross-Site Scripting) that could arise if untrusted, unvalidated input is used with Guava utilities in security-sensitive operations like constructing commands, queries, or outputting data to web pages through Guava utility usage. Severity depends on the context and the type of injection vulnerability.
  • Unexpected Behavior and Errors from Guava Utility Misuse (Low to Medium Severity): Prevents unexpected application behavior, errors, or crashes caused by invalid or malformed input being processed by Guava utilities.
• Impact:
  • Injection Vulnerabilities via Guava Utility Misuse (High to Critical): High to Critical impact. Significantly reduces the risk of critical injection vulnerabilities related to misuse of Guava utilities, protecting application integrity and user data.
  • Unexpected Behavior and Errors from Guava Utility Misuse (Low to Medium): Medium impact. Improves application robustness and prevents unexpected errors caused by invalid input when using Guava utilities.
• Currently Implemented:
  • Partially Implemented: Input validation is generally practiced in some parts of the application, especially at API boundaries. However, consistent and thorough input validation specifically before using Guava utilities throughout the codebase is not enforced.
  • Location: Input processing modules, API controllers, data parsing components that utilize Guava utilities.
• Missing Implementation:
  • Consistent Input Validation Policy (Guava Utility Focused): Lack of a consistent policy or guidelines for input validation across the entire application, specifically emphasizing validation before using Guava utilities.
  • Automated Input Validation Checks (Guava Utility Context): No automated tools or processes to enforce input validation rules or detect missing validation in code using Guava utilities.
  • Security Code Reviews Focusing on Input Validation (Guava Utility Usage): Security code reviews do not consistently focus on verifying input validation practices, especially in relation to Guava utility usage.