attack-surface.md

Attack Surface Analysis for reactivex/rxjava

Attack Surface: Uncontrolled Resource Consumption (Memory)

• Description: An attacker triggers excessive memory allocation within RxJava streams, leading to application crashes or unresponsiveness.
  • RxJava Contribution: RxJava's Observable (without backpressure) and improper use of buffering operators can easily lead to unbounded memory consumption.
  • Example: An attacker sends a flood of requests that trigger an Observable to emit a massive number of items without any Flowable backpressure mechanisms, windowing, or buffering with size limits.
  • Impact: Application crash (OutOfMemoryError), denial of service.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Mandatory Flowable with Backpressure: Strictly enforce the use of Flowable and appropriate backpressure strategies (onBackpressureBuffer, onBackpressureDrop, onBackpressureLatest) for any stream that could potentially have a high emission rate.
    • Bounded Buffers: If buffering is required, always use operators like buffer(int count) with a strictly enforced, reasonable size limit. Never use unbounded buffers.
    • Windowing/Throttling: Employ operators like window, throttleFirst, throttleLast, debounce to control the emission rate and prevent overwhelming downstream consumers.
    • Input Validation: Rigorous input validation is crucial to prevent attackers from controlling the size or frequency of emissions.
    • Resource Monitoring: Implement proactive monitoring to detect excessive memory usage and trigger alerts before a crash occurs.

Attack Surface: Uncontrolled Resource Consumption (Threads)

• Description: An attacker causes the creation of an excessive number of threads through RxJava's scheduler mechanisms, leading to thread starvation.
  • RxJava Contribution: Incorrect use of subscribeOn and observeOn, particularly misusing Schedulers.io() for CPU-bound tasks, can result in uncontrolled thread creation.
  • Example: An attacker triggers numerous concurrent operations, each incorrectly using subscribeOn(Schedulers.io()) for a CPU-intensive task, creating far more threads than the system can handle.
  • Impact: Thread starvation, application slowdown, denial of service.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict Scheduler Usage Guidelines:
      • Schedulers.io(): Only for I/O-bound operations.
      • Schedulers.computation(): Always for CPU-bound operations.
      • Schedulers.single(): For sequential execution requirements.
      • Custom Schedulers: Define custom thread pools with appropriate, limited sizes for specific, well-defined tasks.
    • Prohibit Schedulers.io() for CPU-Bound Tasks: Enforce a strict code review policy and potentially static analysis rules to prevent the misuse of Schedulers.io().
    • Concurrency Limits: Use operators like flatMap with a maxConcurrency parameter to strictly control the number of concurrent subscriptions.
    • Thread Pool Monitoring: Implement monitoring of thread pool usage and enforce hard limits to prevent exhaustion.

Attack Surface: Denial of Service via Long-Running Operations

• Description: An attacker exploits RxJava operations that take a long time to complete (without timeouts), tying up resources.
  • RxJava Contribution: RxJava streams can easily encapsulate long-running operations; the absence of timeouts makes them vulnerable.
  • Example: An attacker sends a request that triggers an Observable making a network call to a malicious server, which intentionally delays the response indefinitely.
  • Impact: Resource exhaustion, denial of service.
  • Risk Severity: High
  • Mitigation Strategies:
    • Mandatory Timeouts: Enforce the use of the timeout() operator on any Observable or Flowable that involves a potentially long-running operation (especially network calls). Set reasonable, context-specific timeout durations.
    • Retry Logic with Limits and Backoff: If retries are necessary, use retry() judiciously with a strictly limited number of retries and a backoff strategy to avoid exacerbating DoS conditions.
    • Circuit Breaker Pattern (Consider): Evaluate the use of a circuit breaker pattern (often implemented with external libraries) to prevent repeated calls to failing services, providing resilience.

Attack Surface: Logic Errors due to Concurrency Issues

• Description: Race conditions and data corruption due to improper handling of shared mutable state within concurrent RxJava streams.
  • RxJava Contribution: RxJava's asynchronous nature necessitates careful management of shared state to avoid concurrency problems.
  • Example: Multiple subscribers to an Observable modify a shared mutable data structure (e.g., a List) without any synchronization, leading to data inconsistencies.
  • Impact: Data corruption, unpredictable application behavior, potential security vulnerabilities (depending on the data).
  • Risk Severity: High
  • Mitigation Strategies:
    • Immutability First: Prioritize immutable data structures and functional programming principles to eliminate shared mutable state whenever possible.
    • Strict Synchronization: If shared mutable state is unavoidable, use appropriate synchronization:
      • synchronized blocks or methods.
      • Atomic variables (e.g., AtomicInteger, AtomicReference).
      • Concurrent data structures (e.g., ConcurrentHashMap).
    • Controlled observeOn and subscribeOn: Use these operators carefully to manage which threads access shared state, and consider serialize() to enforce sequential processing of emissions if necessary.
    • Mandatory Code Reviews: Enforce thorough code reviews with a specific focus on identifying and mitigating potential concurrency issues.

Attack Surface: Unhandled Errors and Application Crashes

• Description: Errors within RxJava streams are not handled, leading to application crashes or inconsistent states.
  • RxJava Contribution: RxJava requires explicit error handling; unhandled errors can terminate the stream and potentially the application.
  • Example: An Observable making a network request encounters an error, but no onError handler is provided, causing the application to crash.
  • Impact: Application crash, denial of service, data inconsistency.
  • Risk Severity: High
  • Mitigation Strategies:
    • Mandatory onError Handlers: Enforce the presence of an onError handler in all subscriptions (e.g., subscribe(onNext, onError, onComplete)).
    • Comprehensive Error Handling Operators: Utilize a range of error handling operators:
      • onErrorResumeNext: Switch to a fallback Observable.
      • onErrorReturnItem: Emit a default value.
      • retry (with caution and limits): Retry the operation.
      • doOnError: Perform a side effect (e.g., logging).
    • Centralized Error Handling: Implement a centralized error handling strategy for RxJava streams to ensure consistent error management.
    • Robust Logging: Log all errors with sufficient detail (including stack traces) for effective debugging and monitoring.