threat-modeling.md

Threat Model Analysis for badoo/reaktive

Threat: Unbounded Stream Denial of Service (DoS)

Description: An attacker can exploit the nature of reactive streams to cause a Denial of Service. By manipulating data sources or input to the application, they can trigger the creation of reactive streams that emit data at an uncontrolled and excessive rate, or grow indefinitely without proper backpressure. This overwhelms the application's processing capabilities (CPU, memory) as Reaktive attempts to process the unbounded stream. The attacker aims to make the application unresponsive or crash by exhausting its resources through uncontrolled reactive stream processing.

Impact: Application becomes completely unresponsive, leading to service unavailability. Server crashes due to memory exhaustion or CPU overload. Critical business functions become disrupted.

Reaktive Component Affected: Observable, Subject, Flowable, reactive stream operators (especially those involved in stream composition and transformation), Schedulers (indirectly, as they manage execution of stream operations).

Risk Severity: High

Mitigation Strategies:

• Implement robust backpressure strategies: Utilize Reaktive's backpressure operators like buffer, sample, throttleLatest, debounce, take, and limit to control the rate of data processing in streams.
• Validate and sanitize input data: Thoroughly validate and sanitize all data sources feeding into reactive streams to prevent malicious or unexpected input from creating unbounded streams.
• Set resource limits: Define maximum buffer sizes and stream lengths where appropriate to prevent streams from growing indefinitely.
• Resource monitoring and alerting: Implement monitoring of resource usage (memory, CPU) for reactive streams in production environments and set up alerts to detect and respond to unusual spikes indicative of potential DoS attacks.
• Rate limiting at data source: If feasible, apply rate limiting or throttling at the source of data that feeds into reactive streams to control the incoming data rate.

Threat: Race Condition Exploitation in Concurrent Reactive Flows

Description: Reaktive's reactive programming paradigm inherently involves concurrency. If developers incorrectly manage shared mutable state accessed by multiple concurrent reactive streams or operators, it can lead to race conditions. An attacker, by carefully timing requests or events, can exploit these race conditions to manipulate application state in unintended and potentially harmful ways. This could involve corrupting data, bypassing security checks, or causing unpredictable application behavior. The attacker leverages the concurrent nature of Reaktive streams and flaws in state management to achieve malicious goals.

Impact: Data corruption leading to business logic errors or data integrity issues. Inconsistent application state causing unpredictable and potentially exploitable behavior. Security bypasses if race conditions affect authorization or validation logic. Potential for critical application failures or vulnerabilities.

Reaktive Component Affected: Reactive streams interacting with shared mutable state, Subjects (when used as shared state and accessed concurrently), Schedulers (as they manage concurrency), custom reactive operators that introduce or manage shared state unsafely.

Risk Severity: High

Mitigation Strategies:

• Minimize shared mutable state: Adopt functional reactive programming principles and minimize the use of shared mutable state within reactive streams. Favor immutable data structures and pure functions.
• Implement proper synchronization: When shared mutable state is unavoidable, use robust synchronization mechanisms to protect concurrent access. Consider using thread-safe data structures or concurrency primitives provided by Kotlin or Java. Carefully review Reaktive's documentation for any concurrency utilities it might offer (though Reaktive itself is more about reactive streams than low-level concurrency primitives).
• Thorough concurrency testing: Conduct rigorous testing, including concurrency and stress testing, to identify and eliminate potential race conditions in reactive flows.
• Code reviews focused on concurrency: Perform code reviews specifically focused on identifying potential race conditions and unsafe access to shared state within reactive stream implementations.
• Consider reactive state management patterns: Explore and utilize established reactive state management patterns (like using Subjects as event buses but carefully managing their state updates) to reduce the risk of race conditions compared to ad-hoc shared mutable state management.