attack-surface.md

Attack Surface Analysis for reactivex/rxjava

Attack Surface: Backpressure Vulnerabilities

• Description: Resource exhaustion leading to Denial of Service (DoS) due to uncontrolled data flow in reactive streams. Producers overwhelm consumers, causing excessive buffering and memory exhaustion.
• RxJava Contribution: RxJava's asynchronous nature and reactive streams, if not properly backpressured, inherently create the potential for producers to outpace consumers. Lack of or incorrect backpressure implementation directly exposes this vulnerability in RxJava applications.
• Example: A real-time data processing pipeline built with RxJava ingests data from a high-throughput source (e.g., network sensor). If backpressure is not implemented, a sudden surge in sensor data can cause the RxJava application to buffer all incoming data in memory, leading to OutOfMemoryError and application crash, effectively causing a DoS.
• Impact: Denial of Service (DoS), complete application outage, service unavailability.
• Risk Severity: Critical
• Mitigation Strategies:
  • Mandatory Backpressure Implementation: Treat backpressure implementation as a critical security requirement for all RxJava streams handling external or high-volume data sources.
  • Choose Appropriate Backpressure Strategy: Select the most suitable backpressure strategy (onBackpressureBuffer, onBackpressureDrop, onBackpressureLatest, onBackpressureBuffer(OverflowStrategy.DROP_OLDEST)) based on the application's data processing needs and tolerance for data loss.
  • Proactive Resource Monitoring & Alerting: Implement robust monitoring of memory usage and other resources. Set up alerts to trigger when resource consumption approaches critical levels, indicating potential backpressure issues.
  • Rigorous Load Testing with Backpressure Focus: Conduct thorough load and stress testing specifically designed to evaluate backpressure handling under extreme conditions and identify weaknesses.

Attack Surface: Error Handling Leading to Information Disclosure or DoS

• Description: Exposure of sensitive information through improperly handled exceptions in RxJava streams, or application instability/DoS due to unhandled stream termination.
• RxJava Contribution: RxJava's error propagation model can lead to stream termination if exceptions are not explicitly caught. Default error handling might inadvertently expose detailed stack traces or internal application state, aiding attackers in reconnaissance. Unhandled stream termination can lead to application-level DoS.
• Example: An RxJava stream processing user authentication requests. If an unexpected error occurs during authentication (e.g., database connection issue) and is not handled with onErrorReturn or similar, the raw exception (including database connection strings or internal paths in stack traces) might be logged or even returned in an error response to the user. This information leak can be exploited by attackers. Furthermore, if the stream terminates due to the unhandled error, authentication service becomes unavailable (DoS).
• Impact: Information Disclosure (sensitive data leakage), Denial of Service (service unavailability), potential for further exploitation based on leaked information.
• Risk Severity: High (can escalate to Critical if highly sensitive data is leaked or core service is impacted by DoS)
• Mitigation Strategies:
  • Secure and Centralized Error Handling in Streams: Implement mandatory, secure error handling for all RxJava streams, using operators like onErrorReturn, onErrorResumeNext, onErrorComplete, and doOnError. Centralize error handling logic for consistency and security.
  • Strict Error Logging Sanitization: Enforce strict sanitization of error logs to prevent exposure of sensitive data. Mask or redact potentially sensitive information (database credentials, internal paths, user-specific data) before logging.
  • Generic Error Responses to Clients: Never expose detailed error messages or stack traces to external clients. Return generic, user-friendly error messages while logging detailed errors securely for internal debugging.
  • Stream Resiliency and Recovery: Design reactive streams to be resilient to errors. Use onErrorResumeNext to recover from errors and continue stream processing gracefully instead of terminating streams abruptly, preventing DoS.

Attack Surface: Concurrency Race Conditions Exploitable for Data Corruption or Logic Bypass

• Description: Exploitable race conditions arising from improper concurrency management within RxJava streams, leading to data corruption, inconsistent application state, or bypass of security logic.
• RxJava Contribution: RxJava's concurrency model, while powerful, introduces the risk of race conditions if shared mutable state is accessed concurrently by multiple reactive streams or operators without proper synchronization. Incorrect scheduler usage can exacerbate these issues.
• Example: An RxJava stream managing user session state. Multiple concurrent requests from the same user might trigger concurrent updates to the session object. If session state updates are not properly synchronized (e.g., using thread-safe data structures or explicit locking), race conditions can occur, leading to session data corruption. An attacker might exploit this to manipulate session data, bypass authentication checks, or gain unauthorized access.
• Impact: Data Corruption, Inconsistent Application State, Security Logic Bypass, Potential for Privilege Escalation or Unauthorized Access.
• Risk Severity: High (can be Critical if data corruption leads to significant financial loss or security breach)
• Mitigation Strategies:
  • Immutable Data and Functional Principles: Prioritize immutable data structures and functional programming principles within RxJava streams to minimize shared mutable state and reduce the risk of race conditions.
  • Strictly Control Shared Mutable State: Minimize the use of shared mutable state. When unavoidable, encapsulate and carefully control access to shared state using thread-safe data structures (e.g., ConcurrentHashMap, Atomic types) or explicit synchronization mechanisms.
  • Scheduler Best Practices and Audits: Implement and enforce best practices for RxJava scheduler usage. Regularly audit scheduler configurations to ensure they are appropriate for the application's concurrency requirements and do not introduce unintended race conditions.
  • Concurrency Focused Testing and Static Analysis: Conduct rigorous concurrency testing, including stress testing and race condition detection. Utilize static analysis tools to identify potential concurrency vulnerabilities in RxJava code.

Attack Surface: Operator Misuse Leading to Critical Logic Flaws or Code Execution

• Description: Critical security vulnerabilities introduced by incorrect or insecure usage of RxJava operators, potentially leading to logic flaws, data manipulation, or even code execution.
• RxJava Contribution: RxJava's extensive operator library, while powerful, can be misused. Certain operators, if used improperly with untrusted input or in insecure contexts, can create pathways for exploitation. Custom operators, if not implemented with security in mind, can introduce severe vulnerabilities.
• Example: Using Observable.unsafeCreate() with a poorly validated or attacker-controlled function within a reactive stream. If the function passed to unsafeCreate() is vulnerable to code injection or performs insecure operations based on external input, it can lead to Remote Code Execution (RCE). Similarly, misuse of operators like flatMap with functions that execute system commands based on user input can create critical vulnerabilities.
• Impact: Remote Code Execution (RCE), Arbitrary Code Execution, Critical Logic Flaws, Data Manipulation, Complete System Compromise.
• Risk Severity: Critical
• Mitigation Strategies:
  • Secure Operator Usage Training and Guidelines: Provide comprehensive training to developers on secure RxJava operator usage, highlighting potential security pitfalls and best practices. Establish clear coding guidelines for operator usage, especially when dealing with external input or sensitive operations.
  • Ban or Restrict Dangerous Operators: Identify and restrict the use of potentially dangerous operators like unsafeCreate() or operators that can easily lead to code injection if misused. Provide secure alternatives or require mandatory security reviews for their usage.
  • Mandatory Security Review for Custom Operators: Implement a mandatory security review process for all custom RxJava operators before deployment. Ensure custom operators are thoroughly vetted for potential vulnerabilities (injection, buffer overflows, logic flaws).
  • Input Validation and Sanitization within Operators: Enforce strict input validation and sanitization within RxJava operators, especially when processing external or untrusted data. Prevent injection attacks by validating and sanitizing input before using it in operator logic.