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Mitigation Strategies Analysis for google/ksp

Mitigation Strategy: Strictly Control KSP Processor Dependencies

Mitigation Strategy: Strictly Control KSP Processor Dependencies

Here are mitigation strategies that directly involve Kotlin Symbol Processing (KSP), focusing on threats introduced by KSP itself.

  • Description:

    • Step 1: Create a centralized list of approved KSP processors. This list should be maintained and accessible to all developers.
    • Step 2: Define a process for requesting and approving new KSP processors. This process should involve security review and justification for the new dependency specifically focusing on the processor's code and potential actions during annotation processing.
    • Step 3: Integrate the approved list into the build system. Configure dependency management tools (like Gradle) to only allow dependencies from the approved list when resolving KSP processor dependencies.
    • Step 4: Regularly review and update the approved list, removing outdated or insecure processors and adding new vetted ones as needed, re-evaluating processors in the context of KSP specific risks.
    • Step 5: Educate developers about the importance of using only approved processors and the process for requesting new ones, emphasizing the unique security considerations of KSP processors.

  • List of Threats Mitigated:

    • Malicious Processor Injection: - Severity: High. An attacker could introduce a malicious KSP processor as a dependency to inject malicious code into the application during KSP annotation processing and code generation.
    • Vulnerable Processor Dependency: - Severity: Medium. Using an outdated or vulnerable KSP processor dependency can introduce vulnerabilities into the build process or generated code specifically through flaws in the processor's logic or generated output.
    • Supply Chain Attack via Processor: - Severity: High. Compromised or malicious upstream processor repositories could be used to distribute malicious processors that execute during KSP processing.

  • Impact:

    • Malicious Processor Injection: High Reduction. Significantly reduces the risk by preventing the introduction of unvetted processors into the KSP processing pipeline.
    • Vulnerable Processor Dependency: Medium Reduction. Reduces risk by focusing on approved and presumably more secure processors, but regular auditing is still needed to catch vulnerabilities in even approved processors.
    • Supply Chain Attack via Processor: Medium Reduction. Reduces risk by limiting the sources of processors and implementing a review process, but doesn't eliminate the risk entirely if approved sources are compromised at the processor level.

  • Currently Implemented: Partially implemented. We have a general dependency review process, but it's not specifically tailored for KSP processors and doesn't have a formal "approved list" for KSP processors specifically. Dependency management is handled by Gradle.

  • Missing Implementation: Formal "approved list" of KSP processors, specific security review process for KSP processors focusing on processor code and actions, integration of the approved list into the build system to enforce its use for KSP processor dependencies.

Mitigation Strategy: Conduct Security Code Reviews of Custom KSP Processors

Mitigation Strategy: Conduct Security Code Reviews of Custom KSP Processors

  • Description:

    • Step 1: Establish a mandatory security code review process for all custom KSP processors developed in-house.
    • Step 2: Train developers on secure coding practices for KSP processors, focusing on common vulnerabilities like code injection in generated code, insecure data handling within the processor, and resource exhaustion during processing.
    • Step 3: Assign security-conscious developers or security experts to conduct code reviews of custom processors.
    • Step 4: During code reviews, specifically look for potential security vulnerabilities in the processor logic, code generation, and data handling within the KSP processor itself.
    • Step 5: Use code review checklists or guidelines tailored for KSP processor security.
    • Step 6: Document the code review process and findings, and ensure that identified security issues are addressed before the processor is deployed and used in builds.

  • List of Threats Mitigated:

    • Vulnerabilities in Custom Processor Logic: - Severity: High. Custom processors might contain vulnerabilities in their logic that could be exploited during KSP processing or in the generated code.
    • Insecure Code Generation: - Severity: High. Custom processors might generate insecure code if not carefully designed and implemented in their code generation logic.
    • Data Handling Vulnerabilities in Processors: - Severity: Medium. Processors might mishandle sensitive data during processing, leading to information leaks or other vulnerabilities within the processor's execution context.

  • Impact:

    • Vulnerabilities in Custom Processor Logic: High Reduction. Significantly reduces the risk by proactively identifying and fixing vulnerabilities in processor code before it's used in builds.
    • Insecure Code Generation: High Reduction. Helps ensure that generated code is secure by reviewing the generation logic within the processor.
    • Data Handling Vulnerabilities in Processors: Medium Reduction. Improves data handling security within processors, but depends on the thoroughness of the review of the processor code.

  • Currently Implemented: Partially implemented. We have general code review practices, but not specifically focused on security aspects of custom KSP processors, and no dedicated security expertise is consistently involved in processor reviews specifically for KSP processors.

  • Missing Implementation: Mandatory security code reviews for custom KSP processors, security-focused training for processor developers on KSP specific security concerns, security code review checklists for KSP processors, dedicated security expertise in processor reviews for KSP processors.

Mitigation Strategy: Static Analysis of KSP Processor Code

Mitigation Strategy: Static Analysis of KSP Processor Code

  • Description:

    • Step 1: Select and integrate a static analysis tool that can analyze Kotlin code, including KSP processors. Tools like SonarQube, Detekt, or specialized security-focused static analyzers can be used to scan processor code.
    • Step 2: Configure the static analysis tool to scan custom KSP processor code for potential security vulnerabilities and coding best practices violations within the processor implementation.
    • Step 3: Integrate the static analysis tool into the CI/CD pipeline to automatically scan processor code during builds or processor development phase.
    • Step 4: Set up alerts and notifications for detected security issues in processor code.
    • Step 5: Establish a process for reviewing and addressing static analysis findings related to processors.
    • Step 6: Regularly update the static analysis tool and its rules to ensure it remains effective against new vulnerabilities in processor code.

  • List of Threats Mitigated:

    • Vulnerabilities in Custom Processor Logic: - Severity: Medium. Static analysis can automatically detect many common coding errors and potential vulnerabilities in processor code that could lead to security issues during KSP processing or in generated code.
    • Insecure Code Generation Patterns: - Severity: Medium. Static analysis can identify patterns in processor code that might lead to insecure code generation by the processor.
    • Coding Best Practices Violations: - Severity: Low. Static analysis can enforce coding standards and best practices in processor code, indirectly improving security.

  • Impact:

    • Vulnerabilities in Custom Processor Logic: Medium Reduction. Reduces the risk by automatically detecting many common vulnerabilities in processor code, but might not catch all types of security flaws.
    • Insecure Code Generation Patterns: Medium Reduction. Helps identify and prevent insecure code generation patterns in processors, but might require custom rules for KSP-specific issues.
    • Coding Best Practices Violations: Low Reduction. Indirectly improves security by promoting better code quality in processors.

  • Currently Implemented: Partially implemented. We use SonarQube for general static analysis, but it might not be specifically configured or optimized for KSP processor code and security vulnerabilities within processors.

  • Missing Implementation: Specific configuration of static analysis tools for KSP processor security, integration of static analysis results into the build failure criteria for processor code issues, process for reviewing and addressing static analysis findings for processors.

Mitigation Strategy: Review Generated Code for Security Vulnerabilities

Mitigation Strategy: Review Generated Code for Security Vulnerabilities

  • Description:

    • Step 1: Treat the code generated by KSP processors as part of the application's codebase and subject it to security reviews.
    • Step 2: Include generated code in regular code reviews, especially when changes are made to KSP processors or their configurations that affect code generation.
    • Step 3: Train developers to recognize common code vulnerabilities in generated code, such as injection flaws (SQL injection, command injection, etc.), cross-site scripting (XSS), insecure data handling, and exposed sensitive information in the context of generated Kotlin/Java code.
    • Step 4: Use code review checklists or guidelines that include specific checks for security vulnerabilities in generated code from KSP processors.
    • Step 5: Document the code review process and findings for generated code, and ensure that identified security issues are addressed in the processor or the generated code itself.

  • List of Threats Mitigated:

    • Security Vulnerabilities in Generated Code: - Severity: High. KSP processors might unintentionally generate code with security vulnerabilities if not carefully designed and tested in their code generation logic.
    • Injection Flaws in Generated Code: - Severity: High. Processors might generate code susceptible to injection attacks if input validation or output encoding is not properly handled in the processor's code generation.
    • Exposure of Sensitive Information in Generated Code: - Severity: Medium. Processors might inadvertently expose sensitive information in the generated code, such as hardcoded credentials or API keys if the processor logic includes such flaws.

  • Impact:

    • Security Vulnerabilities in Generated Code: High Reduction. Significantly reduces the risk by proactively identifying and fixing vulnerabilities in the generated output before deployment.
    • Injection Flaws in Generated Code: High Reduction. Helps prevent injection vulnerabilities by reviewing the generated code for proper input handling and output encoding resulting from processor actions.
    • Exposure of Sensitive Information in Generated Code: Medium Reduction. Reduces the risk of accidental exposure of sensitive information in generated code, but depends on the thoroughness of the review.

  • Currently Implemented: Partially implemented. Generated code is implicitly included in general code reviews, but there is no specific focus on security vulnerabilities in generated code from KSP, and developers might not be specifically trained to identify them in generated code.

  • Missing Implementation: Explicitly include generated code from KSP processors in security code reviews, security-focused training for developers on identifying vulnerabilities in generated code specifically from KSP, code review checklists specifically for generated code security from KSP.

Mitigation Strategy: Implement Static Analysis on Generated Code

Mitigation Strategy: Implement Static Analysis on Generated Code

  • Description:

    • Step 1: Extend the static analysis tools used for application code to also scan the code generated by KSP processors.
    • Step 2: Configure the static analysis tools to detect common code vulnerabilities in the generated code, such as injection flaws, XSS, insecure data handling, etc. in the context of Kotlin/Java code generated by KSP.
    • Step 3: Integrate the static analysis of generated code into the CI/CD pipeline.
    • Step 4: Set up alerts and notifications for detected security issues in generated code from KSP.
    • Step 5: Establish a process for reviewing and addressing static analysis findings in generated code from KSP.
    • Step 6: Regularly update the static analysis tool and its rules to ensure it remains effective against new vulnerabilities in generated code from KSP.

  • List of Threats Mitigated:

    • Security Vulnerabilities in Generated Code: - Severity: Medium. Static analysis can automatically detect many common code vulnerabilities in generated code produced by KSP processors.
    • Injection Flaws in Generated Code: - Severity: Medium. Static analysis can identify patterns in generated code that might be susceptible to injection attacks due to processor logic.
    • Unintentional Introduction of Vulnerabilities in Generated Code: - Severity: Medium. Automated scanning helps catch vulnerabilities that might be missed in manual reviews of KSP generated code.

  • Impact:

    • Security Vulnerabilities in Generated Code: Medium Reduction. Reduces the risk by automatically detecting many common vulnerabilities in KSP generated code, but might not catch all types of security flaws.
    • Injection Flaws in Generated Code: Medium Reduction. Helps identify and prevent injection vulnerabilities in generated code from KSP, but might require custom rules for KSP-specific generation patterns.
    • Unintentional Introduction of Vulnerabilities in Generated Code: Medium Reduction. Provides an additional layer of automated security checks for KSP generated code.

  • Currently Implemented: Not implemented. Static analysis tools are not currently configured to specifically scan the generated code output by KSP processors.

  • Missing Implementation: Configuration of static analysis tools to scan generated code from KSP, integration of generated code static analysis into the CI/CD pipeline, process for reviewing and addressing static analysis findings in generated code from KSP.

Mitigation Strategy: Secure Code Generation Practices in Processors

Mitigation Strategy: Secure Code Generation Practices in Processors

  • Description:

    • Step 1: Develop and document secure code generation guidelines for KSP processor developers.
    • Step 2: Train processor developers on secure code generation practices, emphasizing principles like input validation within the processor, output encoding in generated code, least privilege for generated code, and avoiding hardcoded secrets in processor logic or generated code.
    • Step 3: Implement input validation and sanitization within the processor logic to prevent injection vulnerabilities in the generated output.
    • Step 4: Use output encoding techniques (e.g., HTML encoding, URL encoding) in generated code where necessary to prevent XSS and other output-related vulnerabilities in code generated by processors for web contexts.
    • Step 5: Avoid hardcoding sensitive information (credentials, API keys, etc.) in processor code or generated output. Use secure configuration management or secrets management solutions instead for processors and generated code.
    • Step 6: Follow the principle of least privilege when generating code, ensuring that generated code only has the necessary permissions and access rights as dictated by the processor logic.

  • List of Threats Mitigated:

    • Injection Flaws in Generated Code: - Severity: High. Lack of input validation and sanitization in processors can lead to injection vulnerabilities in generated code produced by those processors.
    • Cross-Site Scripting (XSS) in Generated Code: - Severity: High. Improper output encoding in processors can lead to XSS vulnerabilities in generated code used in web contexts generated by those processors.
    • Exposure of Sensitive Information in Generated Code: - Severity: Medium. Hardcoding secrets in processors can lead to their exposure in generated code generated by those processors.
    • Privilege Escalation via Generated Code: - Severity: Medium. Generating code with excessive privileges can create opportunities for privilege escalation attacks if the processor generates code with overly broad permissions.

  • Impact:

    • Injection Flaws in Generated Code: High Reduction. Significantly reduces the risk by preventing injection vulnerabilities at the source (processor logic and code generation practices).
    • Cross-Site Scripting (XSS) in Generated Code: High Reduction. Helps prevent XSS vulnerabilities by ensuring proper output encoding in generated code generated by processors.
    • Exposure of Sensitive Information in Generated Code: Medium Reduction. Reduces the risk of accidental exposure of secrets, but relies on developers consistently following guidelines when writing processors.
    • Privilege Escalation via Generated Code: Medium Reduction. Helps prevent privilege escalation by promoting least privilege in code generation within processors.

  • Currently Implemented: Partially implemented. We have general secure coding guidelines, but not specific guidelines for secure KSP processor development and code generation practices. Training on secure processor development is missing.

  • Missing Implementation: Documented secure code generation guidelines for KSP processors, training for processor developers on secure practices specific to KSP, enforcement of these guidelines through code reviews and static analysis of processor code.

Mitigation Strategy: Principle of Least Privilege in Code Generation

Mitigation Strategy: Principle of Least Privilege in Code Generation

  • Description:

    • Step 1: Design KSP processors to generate code with the minimum necessary privileges and permissions.
    • Step 2: Avoid generating code that requires excessive access to system resources, sensitive data, or privileged operations unless absolutely necessary as determined by the processor's intended function.
    • Step 3: When generating code that interacts with APIs or services, ensure that the generated code uses the least privileged credentials or access tokens required for its functionality as dictated by the processor's design.
    • Step 4: Review the generated code to verify that it adheres to the principle of least privilege and does not request or utilize unnecessary permissions based on the processor's intended output.
    • Step 5: Document the intended privileges and permissions of the generated code and the rationale for them in the context of the KSP processor's purpose.

  • List of Threats Mitigated:

    • Privilege Escalation via Generated Code: - Severity: Medium. Generating code with excessive privileges can create opportunities for privilege escalation attacks if vulnerabilities are found in the generated code.
    • Lateral Movement after Compromise: - Severity: Medium. If generated code with broad permissions is compromised, it can facilitate lateral movement to other parts of the system due to the excessive permissions granted by the processor.
    • Data Breach due to Excessive Permissions: - Severity: Medium. Generated code with unnecessary access to sensitive data increases the risk of data breaches if vulnerabilities are exploited in that generated code.

  • Impact:

    • Privilege Escalation via Generated Code: Medium Reduction. Reduces the risk of privilege escalation by limiting the privileges of generated code produced by processors.
    • Lateral Movement after Compromise: Medium Reduction. Limits the potential impact of a compromise by restricting the permissions of the compromised code generated by processors.
    • Data Breach due to Excessive Permissions: Medium Reduction. Reduces the risk of data breaches by limiting access to sensitive data in code generated by processors.

  • Currently Implemented: Partially implemented. The principle of least privilege is generally understood, but not explicitly enforced or documented in the context of KSP code generation.

  • Missing Implementation: Formal guidelines on applying least privilege in KSP code generation, code review checklists to verify least privilege in generated code from KSP, documentation of intended privileges for generated code in relation to KSP processors.

Mitigation Strategy: Set Resource Limits for KSP Processor Execution

Mitigation Strategy: Set Resource Limits for KSP Processor Execution

  • Description:

    • Step 1: Identify the build system or environment where KSP processors are executed (e.g., CI/CD agents, developer machines).
    • Step 2: Configure resource limits (e.g., CPU time, memory, file system access) for the processes executing KSP processors. This might involve using containerization, process control groups (cgroups), or build system specific configurations applied specifically to KSP processor tasks.
    • Step 3: Set reasonable resource limits based on the expected resource consumption of KSP processors in the project.
    • Step 4: Monitor resource usage during builds to ensure that processors are operating within the defined limits and adjust limits as needed for KSP processor processes.
    • Step 5: Implement alerts or notifications if processors exceed resource limits, potentially indicating a malicious processor or a performance issue related to KSP processing.

  • List of Threats Mitigated:

    • Denial of Service (DoS) via Malicious Processor: - Severity: High. A malicious KSP processor could be designed to consume excessive resources (CPU, memory) to cause a denial of service during the build process by overloading the system with processor activity.
    • Resource Exhaustion by Poorly Written Processor: - Severity: Medium. Even non-malicious but poorly written processors could unintentionally consume excessive resources, leading to build failures or slowdowns due to inefficient processor code.

  • Impact:

    • Denial of Service (DoS) via Malicious Processor: Medium Reduction. Reduces the impact of DoS attacks by limiting the resources a malicious processor can consume, preventing complete build system shutdown caused by KSP processor overload.
    • Resource Exhaustion by Poorly Written Processor: Medium Reduction. Prevents resource exhaustion caused by poorly written processors, improving build stability and reliability related to KSP processing.

  • Currently Implemented: Not implemented. Resource limits are not currently specifically configured or enforced for KSP processor execution in our build environment.

  • Missing Implementation: Configuration of resource limits for KSP processor execution in the build environment, monitoring of processor resource usage specifically for KSP processes, alerts for resource limit violations by KSP processors.

Mitigation Strategy: Monitor KSP Processor Execution Time

Mitigation Strategy: Monitor KSP Processor Execution Time

  • Description:

    • Step 1: Implement monitoring of the execution time of KSP processors during builds. This can be done by logging timestamps before and after processor execution or using build system profiling tools specifically for KSP processor tasks.
    • Step 2: Establish baseline execution times for KSP processors under normal conditions.
    • Step 3: Set up alerts or notifications if processor execution times significantly deviate from the baseline or exceed predefined thresholds for KSP processor tasks.
    • Step 4: Investigate any alerts related to unusually long processor execution times to determine the cause (e.g., malicious processor, performance issue, configuration problem related to KSP processing).
    • Step 5: Regularly review and adjust baseline execution times and thresholds as the project evolves and processor usage changes for KSP processors.

  • List of Threats Mitigated:

    • Malicious Processor Activity Detection: - Severity: Medium. Malicious processors might exhibit unusual behavior, including significantly longer execution times compared to legitimate processors during KSP processing.
    • Performance Degradation Detection: - Severity: Low. Long processor execution times can indicate performance issues that might indirectly impact security by slowing down development and release cycles due to inefficient KSP processing.

  • Impact:

    • Malicious Processor Activity Detection: Medium Reduction. Provides an early warning system for potential malicious processor activity based on execution time anomalies during KSP processing.
    • Performance Degradation Detection: Low Reduction. Helps identify and address performance issues in KSP processing, indirectly improving overall system stability.

  • Currently Implemented: Not implemented. We do not currently monitor or log the execution time of individual KSP processors during builds.

  • Missing Implementation: Implementation of execution time monitoring for KSP processors, establishment of baseline execution times for KSP processors, alerts for anomalous execution times of KSP processors, process for investigating alerts related to KSP processor execution time.

Mitigation Strategy: Implement Timeouts for KSP Processor Tasks

Mitigation Strategy: Implement Timeouts for KSP Processor Tasks

  • Description:

    • Step 1: Configure timeouts for KSP processor tasks within the build system. This might involve setting timeouts in Gradle build scripts or CI/CD pipeline configurations specifically for KSP processor execution.
    • Step 2: Set reasonable timeout values based on the expected execution time of KSP processors in the project, allowing sufficient time for normal processing but preventing indefinite hangs of KSP processor tasks.
    • Step 3: Ensure that build processes are configured to automatically terminate KSP processor tasks that exceed the defined timeouts.
    • Step 4: Implement logging and error handling to capture timeout events and provide informative error messages to developers when KSP processor tasks timeout.
    • Step 5: Regularly review and adjust timeout values as needed based on project changes and processor performance of KSP processors.

  • List of Threats Mitigated:

    • Denial of Service (DoS) via Malicious Processor: - Severity: Medium. A malicious processor could be designed to hang indefinitely, causing the build process to stall and leading to a denial of service due to a stuck KSP processor.
    • Build Process Hangs due to Processor Errors: - Severity: Low. Non-malicious but buggy processors could also cause build processes to hang indefinitely due to errors or infinite loops within KSP processor code.

  • Impact:

    • Denial of Service (DoS) via Malicious Processor: Medium Reduction. Prevents complete build process stalls caused by malicious processors by enforcing timeouts on KSP processor tasks.
    • Build Process Hangs due to Processor Errors: Medium Reduction. Improves build stability and reliability by preventing hangs caused by buggy processors during KSP processing.

  • Currently Implemented: Partially implemented. We have general build timeouts in our CI/CD pipeline, but not specifically configured for individual KSP processor tasks.

  • Missing Implementation: Specific timeout configurations for KSP processor tasks, logging and error handling for processor timeouts of KSP tasks, review and adjustment of timeout values for KSP processors.