attack-surface.md

Attack Surface Analysis for nodejs/readable-stream

Attack Surface: Uncontrolled Data Consumption (Denial of Service)

Description: An attacker can exploit the application's stream processing by sending an excessively large or complex data stream, overwhelming resources and leading to a denial of service. readable-stream Contribution: readable-stream provides the core mechanism for applications to consume and process streams. If applications using readable-stream fail to implement backpressure or resource limits, they become vulnerable to consuming unbounded amounts of data, facilitated directly by the library's stream consumption capabilities. Example: A server uses readable-stream to handle file uploads. An attacker initiates a large file upload without proper size limits or backpressure implementation in the stream pipeline. The server attempts to buffer the incoming data, leading to memory exhaustion and server unresponsiveness. Impact: Denial of Service, application unavailability, server crash due to resource exhaustion (memory, CPU). Risk Severity: High Mitigation Strategies:

• Implement Backpressure: Utilize pipe with backpressure mechanisms or manually control stream flow using stream.pause() and stream.resume() based on processing capacity.
• Set Resource Limits: Enforce limits on the size of incoming data streams at the application level (e.g., maximum request body size, file size limits).
• Bounded Buffering: If buffering is necessary, use bounded buffers with predefined maximum sizes to prevent unbounded memory consumption.
• Resource Monitoring and Alerting: Implement monitoring of resource usage (CPU, memory) and set up alerts to detect and respond to potential DoS attacks.

Attack Surface: Data Injection and Manipulation through Stream Input

Description: Malicious data injected into a readable-stream from an untrusted source can compromise the application if processed without proper sanitization, leading to various injection vulnerabilities. readable-stream Contribution: readable-stream serves as the primary interface for reading data from various sources, including potentially untrusted ones like network sockets or external files. The library itself is responsible for efficiently delivering this data to the application, making it the direct conduit for potentially malicious input. Example: An application uses readable-stream to process commands from a network connection. An attacker sends a stream containing malicious commands embedded within the data. If the application directly interprets and executes these commands read from the readable-stream without validation, it can lead to command injection vulnerabilities. Impact: Command Injection, Cross-Site Scripting (XSS) if stream data is used in web contexts, Data Breaches, Remote Code Execution, depending on how the injected data is processed by the application. Risk Severity: Critical Mitigation Strategies:

• Strict Input Validation and Sanitization: Thoroughly validate and sanitize all data read from readable-stream immediately after reading and before any further processing or interpretation. This must be context-aware and tailored to the expected data format and usage.
• Treat Untrusted Sources as Hostile: Always assume data originating from external or untrusted sources is potentially malicious and handle it with extreme caution.
• Principle of Least Privilege: Minimize the privileges of the application components processing stream data to limit the potential damage from successful exploitation.
• Content Security Policy (CSP): For web applications, implement a strong Content Security Policy to mitigate potential XSS vulnerabilities if stream data is used in web contexts.

Attack Surface: Stream Error Handling Leading to Resource Leaks or Failures

Description: Insufficient or incorrect error handling when working with readable-stream can lead to resource leaks, application crashes, or unexpected state transitions, potentially exploitable for denial of service or other attacks. readable-stream Contribution: readable-stream emits 'error' events to signal issues during stream operations. Applications must properly listen for and handle these events. Failure to do so, or incorrect error handling logic when reacting to readable-stream errors, directly stems from how the application interacts with the library's error reporting mechanism. Example: A stream pipeline involving readable-stream experiences a parsing error in a transform stream. If the application does not attach an 'error' handler to the pipeline or the readable stream source, the error might propagate unhandled, leading to an application crash or leaving resources (like open file descriptors or network connections associated with the stream) uncleared, causing resource leaks over time and potentially leading to DoS. Impact: Resource Leaks, Application Instability, Denial of Service, potential for bypassing security checks that rely on successful stream completion. Risk Severity: High Mitigation Strategies:

• Implement Robust Error Handlers: Attach 'error' event handlers to all relevant streams in a pipeline, especially the initial readable-stream source and any transform streams. Handle errors gracefully, log them for debugging, and ensure proper resource cleanup in error scenarios.
• Proper Stream Lifecycle Management: Listen for 'end' and 'close' events in addition to 'error' to ensure resources are released and operations are finalized correctly, even if errors occur.
• Circuit Breaker Pattern: Consider implementing a circuit breaker pattern in stream pipelines to prevent cascading failures and isolate errors, improving resilience.
• Thorough Error Scenario Testing: Extensively test stream error handling logic by simulating various error conditions (e.g., network interruptions, malformed data, file access errors) to ensure the application behaves predictably and securely under failure conditions.