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attack-surface.md

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Attack Surface Analysis for jetbrains/compose-multiplatform

Attack Surface: Shared Code Vulnerabilities (Amplified Impact)

  • Description: Flaws in the code shared across all platforms (commonMain). The amplified impact is the key Compose Multiplatform-specific aspect.
  • Compose Multiplatform Contribution: Code sharing is the core of Compose Multiplatform. A single vulnerability affects all target platforms (Android, iOS, Desktop, Web), making this a significantly higher risk than platform-specific vulnerabilities.
  • Example: A flawed authentication bypass in the shared logic would allow unauthorized access on all platforms. A data validation error could lead to data corruption across all platforms.
  • Impact: Compromise of application data, functionality, and user accounts across all platforms. Data breaches, unauthorized access, denial of service.
  • Risk Severity: Critical (if affecting authentication, authorization, or critical data) / High (for most other shared logic flaws).
  • Mitigation Strategies:
    • Rigorous Code Review (Multiplatform Focus): Multiple reviewers, each with expertise in different platform security, must review the shared code.
    • Centralized, Robust Input Validation: Assume all input is malicious. Implement strong input validation and output encoding in the shared code.
    • Secure Dependency Management (Shared Dependencies): Use dependency scanning tools (e.g., OWASP Dependency-Check, Snyk) and regularly audit shared dependencies.
    • Static Analysis (Multiplatform Configuration): Configure static analysis tools to specifically target Kotlin Multiplatform code and common vulnerabilities.
    • Fuzz Testing (Shared Code): Apply fuzzing to the shared code to uncover edge cases and unexpected vulnerabilities.
    • Comprehensive Unit/Integration Tests (Security-Focused): Include security-specific test cases in the shared code's test suite.

Attack Surface: Inconsistent expect/actual Implementations

  • Description: Security-relevant differences in behavior between platform-specific implementations (actual) of a shared interface (expect).
  • Compose Multiplatform Contribution: The expect/actual mechanism is fundamental to Compose Multiplatform's platform abstraction. Inconsistencies create platform-specific vulnerabilities that bypass the shared code's intended security.
  • Example: An expect function for secure storage might have an actual implementation on Android that uses encrypted SharedPreferences, but the iOS actual implementation might mistakenly use unencrypted UserDefaults, leading to data leakage on iOS.
  • Impact: Platform-specific vulnerabilities that circumvent the shared code's security controls. Privilege escalation, data leaks, or other platform-specific exploits.
  • Risk Severity: High (potential for significant platform-specific compromise, bypassing shared security).
  • Mitigation Strategies:
    • Precise expect Interface Definition: The expect declaration must explicitly define all security requirements and expected behavior.
    • Cross-Platform Code Review: Reviewers must compare actual implementations side-by-side, looking for any behavioral differences, not just functional ones.
    • Platform-Specific Security Tests: Create separate test suites for each actual implementation, verifying its security behavior against the expect definition.
    • Documentation of Security Assumptions: Clearly document all security-related assumptions and requirements for each actual implementation.

Attack Surface: Unsafe Native Interoperability (Through Shared or actual Code)

  • Description: Vulnerabilities arising from interactions between Kotlin code (either shared or in actual implementations) and native code (C/C++, Objective-C, Swift, etc.).
  • Compose Multiplatform Contribution: Compose Multiplatform uses Kotlin/Native for interoperability with platform-specific APIs and libraries. This necessitates native interop, introducing the inherent risks.
  • Example: A shared Kotlin function uses Kotlin/Native to call a C library for cryptographic operations. If the C library has a vulnerability (e.g., a buffer overflow), it can be triggered through the shared Kotlin code, affecting all platforms. Alternatively, an actual implementation might use a vulnerable native library specific to that platform.
  • Impact: Memory corruption, arbitrary code execution, denial of service, and other classic native code vulnerabilities. Potential for complete system compromise.
  • Risk Severity: Critical / High (depending on the nature and extent of the native interaction).
  • Mitigation Strategies:
    • Minimize Native Interop: Prefer platform-specific Kotlin APIs whenever possible to reduce the reliance on native code.
    • Memory Safety (Kotlin/Native): Use Kotlin's memory management features meticulously when interacting with native code. Avoid manual memory management in Kotlin/Native if at all possible.
    • Secure Coding Practices (Native Code): If writing native code, adhere strictly to secure coding guidelines for the chosen language (e.g., C/C++). Consider memory-safe languages (e.g., Rust) where feasible.
    • Auditing of Native Libraries: Thoroughly vet all third-party native libraries used (both shared and platform-specific) for security vulnerabilities.
    • Input Validation (Before Native Calls): Rigorously validate all data passed to native code from Kotlin.
    • Sandboxing: If possible, sandbox native code.

Attack Surface: Compose Runtime Exploits

  • Description: Vulnerabilities within the Compose runtime library itself.
  • Compose Multiplatform Contribution: Compose Multiplatform uses a custom runtime, which is a direct attack vector.
  • Example: A hypothetical vulnerability in state management could allow UI manipulation or data leaks. A DoS could be triggered by forcing excessive recomposition.
  • Impact: UI manipulation, data leaks, DoS, potentially arbitrary code execution (in severe, unlikely cases).
  • Risk Severity: High.
  • Mitigation Strategies:
    • Keep Compose Updated: This is the primary mitigation. Regularly update to the latest Compose Multiplatform version to get security patches.
    • Monitor Security Advisories: Subscribe to JetBrains' security advisories for Compose.
    • Avoid Excessive Complexity: While not a complete solution, limiting extremely complex UI interactions can reduce the attack surface within the runtime.

Attack Surface: Deserialization issues in shared code

  • Description: Vulnerabilities arising from deserialization of untrusted data in shared code.
  • Compose Multiplatform Contribution: If shared code is using kotlinx.serialization, there is a risk of Deserialization vulnerabilities.
  • Example: Application is using kotlinx.serialization to deserialize data from untrusted source. Attacker can send malicious data that will be deserialized and executed.
  • Impact: Arbitrary code execution.
  • Risk Severity: Critical.
  • Mitigation Strategies:
    • Avoid deserialization of untrusted data: If possible, avoid deserialization of untrusted data.
    • Use allow lists: If deserialization of untrusted data is necessary, use allow lists to restrict which classes can be deserialized.
    • Validate data after deserialization: Validate data after deserialization to ensure that it is valid.
    • Use secure deserialization libraries: Use secure deserialization libraries that are not vulnerable to deserialization attacks.