attack-surface.md

Attack Surface Analysis for reactivex/rxkotlin

Attack Surface: Dependency Vulnerabilities

• Description: Exploitation of known security vulnerabilities in RxKotlin's dependencies, particularly RxJava and potentially transitive dependencies.
• RxKotlin Contribution: RxKotlin directly depends on RxJava. Vulnerabilities in RxJava are inherited by applications using RxKotlin. RxKotlin's dependency management practices and the age of the RxKotlin version used can influence this risk. While not a vulnerability in RxKotlin code, the dependency is a direct and unavoidable aspect of using RxKotlin.
• Example: A known vulnerability in a specific version of RxJava allows for remote code execution when processing maliciously crafted reactive streams. An application using RxKotlin and that vulnerable RxJava version becomes susceptible to this RCE if it processes untrusted data through reactive streams.
• Impact: Remote Code Execution (RCE), Denial of Service (DoS), Information Disclosure, depending on the specific vulnerability. RCE vulnerabilities are critical.
• Risk Severity: High to Critical, depending on the nature of the vulnerability. RCE vulnerabilities are critical.
• Mitigation Strategies:
  • Dependency Scanning: Regularly scan project dependencies (including transitive ones) using tools like OWASP Dependency-Check or Snyk to identify known vulnerabilities.
  • Dependency Updates: Keep RxKotlin and RxJava dependencies updated to the latest stable versions that include security patches.
  • Vulnerability Monitoring: Subscribe to security advisories for RxJava and related libraries to be informed of new vulnerabilities promptly.

Attack Surface: Unbounded Streams and Resource Exhaustion

• Description: Creation of reactive streams that emit data continuously or at a very high rate without proper backpressure handling or termination, leading to excessive resource consumption.
• RxKotlin Contribution: RxKotlin's ease of creating Observables and Flowables, combined with potentially overlooked backpressure mechanisms, can lead to developers unintentionally creating unbounded streams. Operators like interval, repeat, or sources from external systems without rate limiting are directly used within RxKotlin. The reactive paradigm itself, facilitated by RxKotlin, makes this type of issue more prevalent.
• Example: An application creates an Observable using Observable.interval(1.milliseconds()) to periodically poll an external API. If the subscription to this Observable is not properly disposed of and backpressure is not implemented downstream, it can lead to an ever-increasing number of events being generated and processed, eventually exhausting memory and CPU. An attacker might intentionally trigger actions that create many such unbounded streams.
• Impact: Denial of Service (DoS), application crashes, performance degradation.
• Risk Severity: Medium to High, depending on the criticality of the affected service and ease of exploitation.
• Mitigation Strategies:
  • Backpressure Implementation: Always implement appropriate backpressure strategies (e.g., BUFFER, DROP, LATEST, onBackpressureBuffer, onBackpressureDrop, onBackpressureLatest, custom strategies) when dealing with potentially fast producers or slow consumers in reactive streams.
  • Stream Termination: Ensure all reactive streams have clear termination conditions (e.g., using operators like take, takeUntil, timeout, or proper disposal of subscriptions).
  • Resource Limits: Implement resource limits (e.g., memory limits, thread pool size limits) to prevent unbounded resource consumption from crashing the entire application.

Attack Surface: Error Handling Misconfigurations

• Description: Inadequate or insecure error handling in reactive streams, leading to masked errors, unexpected application states, or information disclosure through error messages.
• RxKotlin Contribution: RxKotlin's error handling mechanisms (e.g., onErrorReturn, onErrorResumeNext, onErrorComplete) require careful configuration. Misuse, facilitated by the specific error handling operators provided by RxKotlin, can lead to swallowing errors silently or exposing sensitive information in error messages. The reactive error handling paradigm is central to RxKotlin.
• Example: An application uses onErrorReturn to catch exceptions in a database query within a reactive stream and returns a default value. However, the original exception details, which might contain database connection strings or sensitive query information, are logged at a debug level, which is inadvertently exposed in production logs accessible to unauthorized personnel. Or, critical errors are silently ignored, leading to inconsistent application state.
• Impact: Information Disclosure, unexpected application behavior, potential bypass of security checks, data corruption.
• Risk Severity: Medium to High, depending on the sensitivity of disclosed information and the impact of unexpected behavior.
• Mitigation Strategies:
  • Comprehensive Error Handling: Implement robust error handling for all reactive streams, ensuring errors are logged, handled gracefully, and do not propagate silently.
  • Secure Error Logging: Log error details securely, avoiding the inclusion of sensitive information in log messages, especially in production environments. Sanitize or redact sensitive data before logging.
  • Error Propagation Control: Carefully control error propagation using operators like onErrorResumeNext or onErrorReturn to prevent errors from masking underlying issues or leading to unexpected application states.

Attack Surface: Side Effects in Operators

• Description: Performing side effects (e.g., I/O operations, state mutations) within RxKotlin operators like map, filter, doOnNext, which can introduce vulnerabilities if not carefully managed, especially in concurrent environments.
• RxKotlin Contribution: RxKotlin operators are designed for functional transformations. While side effects are sometimes necessary, their use within these specific RxKotlin operators can complicate reasoning about stream behavior and introduce concurrency issues if not handled with care. The functional reactive style encouraged by RxKotlin can sometimes clash with necessary side effects, leading to misuse within operators.
• Example: Multiple reactive streams concurrently use doOnNext to increment a shared counter variable without proper synchronization. This can lead to race conditions where the counter value becomes inconsistent, potentially affecting application logic that relies on this counter for security decisions or access control. Or, doOnNext is used to write to a file, and concurrent streams lead to file corruption or race conditions in file access.
• Impact: Race conditions, data corruption, inconsistent application state, potential bypass of security checks.
• Risk Severity: Medium to High, depending on the criticality of the affected data or application logic.
• Mitigation Strategies:
  • Minimize Side Effects in Operators: Prefer pure functional transformations within operators like map, filter, and flatMap. Move side effects to dedicated operators like doOnNext, doOnError, doOnComplete, and handle them carefully.
  • Synchronization for Shared State: If side effects involve shared mutable state, use proper synchronization mechanisms (e.g., locks, atomic variables, concurrent data structures) to prevent race conditions.

Attack Surface: Concurrency Issues in Operators

• Description: Race conditions, deadlocks, or other concurrency problems arising from the misuse of RxKotlin's concurrency operators (subscribeOn, observeOn, custom Schedulers) or incorrect assumptions about thread safety within reactive pipelines.
• RxKotlin Contribution: RxKotlin provides powerful concurrency tools, but incorrect usage of these specific RxKotlin operators can introduce subtle concurrency bugs. The concurrency model and operators are core to RxKotlin and directly contribute to this attack surface if misused.
• Example: Multiple reactive streams concurrently access and modify shared mutable state without proper synchronization, even when using observeOn to switch threads. This can lead to race conditions and data corruption. Or, improper use of subscribeOn and observeOn in nested reactive pipelines creates complex threading scenarios that are hard to reason about and can lead to deadlocks under certain conditions.
• Impact: Race conditions, deadlocks, data corruption, inconsistent application state, unexpected behavior, potential bypass of security checks.
• Risk Severity: Medium to High, depending on the criticality of the affected data and application logic.
• Mitigation Strategies:
  • Understand Concurrency Operators: Thoroughly understand the behavior of subscribeOn, observeOn, and different Schedulers and how they affect thread execution in reactive pipelines.
  • Thread Safety Awareness: Be aware of thread safety considerations when working with shared state in reactive streams, even when using concurrency operators.
  • Synchronization for Shared State: If shared mutable state is necessary, use appropriate synchronization mechanisms (locks, atomic variables, concurrent data structures) to protect it from race conditions.

Attack Surface: Shared State Mutation in Reactive Pipelines

• Description: Unintentional or insecure mutation of shared state within reactive pipelines, leading to race conditions, inconsistent data, or unexpected side effects.
• RxKotlin Contribution: While RxKotlin promotes functional reactive programming, it's still possible to introduce shared mutable state into reactive pipelines. The reactive pipelines themselves, constructed using RxKotlin, become the context where these shared state issues manifest and are potentially harder to debug due to the asynchronous nature.
• Example: A reactive pipeline processes user requests and updates a shared cache. If multiple requests are processed concurrently and the cache update logic is not thread-safe, race conditions can occur, leading to inconsistent cache data. This inconsistent cache data might be used for authorization decisions, potentially allowing unauthorized access.
• Impact: Race conditions, data corruption, inconsistent application state, potential bypass of security checks, unauthorized access.
• Risk Severity: Medium to High, depending on the criticality of the shared state and its impact on security or application logic.
• Mitigation Strategies:
  • Minimize Shared Mutable State: Design reactive pipelines to minimize the use of shared mutable state. Favor immutable data and functional transformations.
  • Encapsulate Shared State: If shared state is necessary, encapsulate it within a dedicated component or service and control access to it through well-defined interfaces and synchronization mechanisms.
  • Thread-Safe Data Structures: Use thread-safe data structures (e.g., ConcurrentHashMap, AtomicReference) for shared state to minimize the risk of race conditions.

Attack Surface: Vulnerable Custom Operators

• Description: Security vulnerabilities introduced in custom RxKotlin operators developed by the application team.
• RxKotlin Contribution: RxKotlin allows developers to create custom operators to extend its functionality. If these custom operators, which are extensions of RxKotlin itself, are not developed with security in mind, they can introduce new vulnerabilities directly within the reactive pipeline.
• Example: A custom operator is created to filter events based on user-provided criteria. If this operator does not properly sanitize or validate the user-provided criteria, it might be vulnerable to injection attacks or allow users to bypass intended filtering logic. Or, a custom operator introduces a resource leak or a concurrency issue due to incorrect implementation.
• Impact: Varies depending on the vulnerability in the custom operator. Could range from Information Disclosure to Remote Code Execution, Denial of Service, or Data Corruption.
• Risk Severity: Low to Critical, depending on the nature of the vulnerability in the custom operator and its usage.
• Mitigation Strategies:
  • Secure Coding Practices for Custom Operators: Apply secure coding practices when developing custom RxKotlin operators, including input validation, output sanitization, error handling, and concurrency considerations.
  • Code Reviews for Custom Operators: Thoroughly review the code of custom operators, paying close attention to security aspects and potential vulnerabilities.
  • Testing Custom Operators: Implement comprehensive unit and integration tests for custom operators, including tests for security-related scenarios and edge cases.