threat-modeling.md

Threat Model Analysis for dotnet/reactive

Threat: Race Condition in Asynchronous Operations

Description: An attacker might exploit race conditions in reactive pipelines by sending concurrent events designed to trigger simultaneous access and modification of shared state. This can lead to data corruption, inconsistent application state, or unexpected behavior. For example, an attacker could rapidly submit multiple requests to update a user's profile concurrently, causing data overwrites and loss of information. Impact: Data corruption, inconsistent application state, business logic bypass, potential for privilege escalation if state management is related to authorization. Affected Reactive Component: Observers, Subjects, Operators that manage or access shared state (e.g., Scan, custom operators). Risk Severity: High Mitigation Strategies:

• Use thread-safe data structures for shared state.
• Minimize shared mutable state within reactive pipelines.
• Employ immutable data structures where possible.
• Utilize operators like Publish with proper synchronization if shared state is necessary.
• Implement proper synchronization mechanisms (though minimize explicit locking in reactive flows).
• Thoroughly test concurrent scenarios and race conditions.

Threat: Deadlock due to Scheduler Misuse

Description: An attacker might indirectly cause a deadlock by triggering specific sequences of events that lead to blocking operations within reactive pipelines, especially if schedulers are misused or blocking calls are made within observers. For example, if a reactive stream processing user requests blocks the thread pool scheduler while waiting for another event, and all thread pool threads are exhausted, a deadlock can occur, leading to denial of service. Impact: Denial of Service (DoS), application hangs, resource exhaustion. Affected Reactive Component: Schedulers, Observers, Operators performing blocking operations. Risk Severity: High Mitigation Strategies:

• Avoid blocking operations within reactive streams.
• Use asynchronous operations and non-blocking schedulers.
• Carefully manage scheduler context using ObserveOn and SubscribeOn.
• Avoid circular dependencies in asynchronous operations.
• Monitor thread pool usage and identify potential blocking operations.

Threat: Resource Exhaustion due to Unbounded Streams (Memory Leak)

Description: An attacker might exploit unbounded reactive streams by continuously sending events at a rate faster than the consumer can process them, especially if backpressure is not implemented. This can lead to unbounded buffering and memory leaks, eventually causing application crashes or denial of service. For example, flooding a public reactive endpoint with events without backpressure can exhaust server memory. Impact: Denial of Service (DoS), application crashes, memory exhaustion, performance degradation. Affected Reactive Component: Observables, Subjects, Buffering operators (if misused), Subscriptions. Risk Severity: High Mitigation Strategies:

• Implement backpressure mechanisms using operators like Buffer, Window, Sample, Throttle, Debounce, or reactive stream frameworks with built-in backpressure.
• Ensure proper disposal of subscriptions using Dispose() or using statements to prevent resource leaks.
• Monitor memory usage of reactive pipelines.
• Implement rate limiting on input streams.

Threat: Denial of Service (DoS) via Stream Overload

Description: An attacker can intentionally flood a reactive stream with a large volume of events, overwhelming the system's resources (CPU, memory, network) and causing a denial of service. This is particularly effective if the stream is publicly accessible or lacks proper input validation and rate limiting. For example, bombarding a server endpoint exposed as a reactive stream with malicious events can exhaust server resources and prevent legitimate users from accessing the application. Impact: Denial of Service (DoS), application unavailability, performance degradation, resource exhaustion. Affected Reactive Component: Observables exposed as endpoints, Subjects, Schedulers, Input validation mechanisms. Risk Severity: High Mitigation Strategies:

• Implement rate limiting on reactive streams exposed to external sources.
• Implement input validation and sanitization to filter malicious events.
• Use resource quotas and throttling mechanisms.
• Employ backpressure to control event processing rate.
• Consider using message queues or buffering mechanisms to decouple producers and consumers.
• Implement monitoring and alerting for stream overload conditions.

Threat: Vulnerabilities in Custom Reactive Operators

Description: An attacker might exploit vulnerabilities introduced in custom reactive operators if they are not implemented securely. Custom operators can introduce new attack vectors if they contain logic flaws, bypass vulnerabilities, or mishandle data. For example, a custom operator designed for data validation might contain a bypass vulnerability, allowing invalid or malicious data to pass through the reactive pipeline. Impact: Business logic bypass, data corruption, injection vulnerabilities (if operators interact with external systems), potential for privilege escalation depending on the operator's function. Affected Reactive Component: Custom Operators. Risk Severity: High (when vulnerabilities are critical) Mitigation Strategies:

• Apply secure coding practices when developing custom operators.
• Thoroughly test custom operators for security vulnerabilities, including input validation, boundary conditions, and error handling.
• Conduct code reviews and security audits of custom operator implementations.
• Follow least privilege principles when designing custom operators' access to resources.