threat-modeling.md

Threat Model Analysis for reactivex/rxkotlin

Threat: Deadlock Exploitation via Malicious Input

• Description: An attacker crafts malicious input that triggers specific combinations of subscribeOn, observeOn, and blocking operations within the reactive stream. The attacker aims to create a deadlock situation by causing threads to wait indefinitely for each other, effectively performing a Denial of Service (DoS). The attacker might exploit vulnerabilities in external libraries interacted with via RxKotlin, but the root cause is improper RxKotlin threading management.
• Impact: Application becomes unresponsive, leading to a Denial of Service (DoS). Users cannot interact with the application.
• Affected RxKotlin Component: subscribeOn, observeOn, Scheduler implementations, and any operators that interact with external resources (e.g., custom operators wrapping blocking calls). The core issue is the misuse of RxKotlin's concurrency features.
• Risk Severity: High
• Mitigation Strategies:
  • Input Validation: Strictly validate and sanitize all user input before it enters the reactive stream. This prevents malicious data from triggering unexpected behavior, although the primary mitigation is correct RxKotlin usage.
  • Timeout Mechanisms: Use the timeout operator to set a maximum duration for operations within the stream. This prevents indefinite blocking, a key component of deadlocks.
  • Non-Blocking Operations: Favor non-blocking I/O operations and asynchronous APIs whenever possible. Avoid blocking calls within the reactive stream. This is a direct RxKotlin best practice.
  • Resource Management: Carefully manage Schedulers. Avoid creating unbounded Schedulers. Use the built-in Schedulers (e.g., Schedulers.io(), Schedulers.computation()) appropriately. This is specific to RxKotlin's threading model.
  • Code Review: Conduct thorough code reviews to identify potential deadlock scenarios within the RxKotlin code.
  • Testing: Implement stress tests and penetration tests that specifically target concurrency and resource management within the context of RxKotlin usage.

Threat: Race Condition Exploitation for Data Corruption

• Description: An attacker sends concurrent requests or manipulates input in a way that exploits race conditions in the handling of shared mutable state within the reactive stream. The attacker aims to corrupt data or cause inconsistent application state. This is most likely if shared mutable state is accessed without proper synchronization within the RxKotlin operators.
• Impact: Data corruption, inconsistent application state, potentially leading to security vulnerabilities or incorrect application behavior. The specific impact depends on the nature of the corrupted data.
• Affected RxKotlin Component: Operators that process data concurrently (e.g., flatMap, concatMap, merge), especially when combined with shared mutable state accessed within the stream's lambda expressions. The vulnerability stems from how RxKotlin handles concurrency.
• Risk Severity: High
• Mitigation Strategies:
  • Immutability: Prefer immutable data structures within the reactive stream. Avoid shared mutable state. This is a direct recommendation for safe RxKotlin usage.
  • Synchronization: If shared mutable state is unavoidable, use appropriate synchronization mechanisms (e.g., synchronized blocks, atomic variables, concurrent data structures) outside the reactive stream. Crucially, avoid blocking within the stream, which would negate RxKotlin's benefits.
  • Serialization: Use the serialize operator to ensure that emissions from an Observable are processed sequentially, even if they originate from different threads. This is a specific RxKotlin operator to address concurrency issues.
  • Code Review: Carefully review code for potential race conditions, especially when dealing with shared resources within the context of RxKotlin operators.
  • Testing: Implement concurrency tests to identify and address race conditions specifically triggered by RxKotlin's asynchronous nature.

Threat: Backpressure Bypass Leading to Resource Exhaustion

• Description: An attacker sends a flood of data to an Observable that is not properly configured for backpressure using RxKotlin's mechanisms. The attacker aims to overwhelm the application, causing memory exhaustion or other resource depletion, leading to a Denial of Service (DoS). The core issue is the failure to use RxKotlin's backpressure features.
• Impact: Application crashes or becomes unresponsive due to resource exhaustion (memory, CPU, etc.), resulting in a Denial of Service (DoS).
• Affected RxKotlin Component: Observable (when used without backpressure handling), any custom operators that produce data without considering backpressure as defined by RxKotlin.
• Risk Severity: High
• Mitigation Strategies:
  • Use Flowable: Use Flowable instead of Observable when backpressure is required. This is a fundamental choice within RxKotlin.
  • Backpressure Operators: Implement appropriate backpressure strategies using operators like onBackpressureBuffer, onBackpressureDrop, onBackpressureLatest, sample, throttleFirst, throttleLast, or debounce. These are all RxKotlin-specific operators.
  • Rate Limiting: While rate limiting can be done outside RxKotlin, using RxKotlin operators like throttle provides a reactive way to manage the flow within the stream.
  • Input Validation: Limit the size and frequency of data accepted from external sources. While this is a general practice, it's relevant here as a supporting measure to RxKotlin's backpressure.
  • Monitoring: Monitor resource usage (memory, CPU) to detect potential backpressure issues related to RxKotlin stream processing.

Threat: Code Injection via Unsafe Operator Usage

• Description: If custom RxKotlin operators are created that dynamically execute code based on user input without proper sanitization, an attacker could inject malicious code. This is a direct threat related to extending RxKotlin functionality incorrectly.
• Impact: Arbitrary code execution, potentially leading to complete system compromise.
• Affected RxKotlin Component: Custom operators that handle user input or interact with external systems without proper sanitization. This is specifically about new code written to work with RxKotlin.
• Risk Severity: Critical
• Mitigation Strategies:
  • Input Validation: Strictly validate and sanitize all user input before it is used within custom RxKotlin operators.
  • Avoid Dynamic Code Execution: Avoid dynamically generating or executing code based on user input within the context of custom RxKotlin operators.
  • Code Review: Thoroughly review custom RxKotlin operators for potential injection vulnerabilities.
  • Security Audits: Conduct regular security audits to identify and address potential vulnerabilities, paying special attention to extensions of RxKotlin.
  • Principle of Least Privilege: Ensure that the application runs with the minimum necessary privileges.