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Attack Tree Analysis for badoo/reaktive

Objective: Compromise application using Reaktive by exploiting weaknesses or vulnerabilities within Reaktive or its usage.

Attack Tree Visualization

Compromise Application via Reaktive Exploitation ├── OR │ ├── Exploit Reaktive Library Vulnerabilities [CRITICAL NODE - Branch] │ │ └── OR │ │ └── Logic Flaws in Reaktive's Reactive Streams Implementation: [CRITICAL NODE - Branch] │ │ └── OR │ │ └── Resource Exhaustion due to Unbounded Streams or Backpressure Issues: [CRITICAL NODE] [HIGH RISK PATH] │ │ └── AND │ │ └── Identify Reaktive flows where backpressure is not properly implemented or where streams can become unbounded. │ │ └── Flood the application with data to exhaust resources (memory, CPU, threads) leading to DoS. │ ├── Abuse Reaktive API Misuse by Developers [CRITICAL NODE - Branch] │ │ └── OR │ │ ├── Incorrect Backpressure Implementation: [CRITICAL NODE] [HIGH RISK PATH] │ │ │ └── AND │ │ │ └── Developers fail to implement proper backpressure handling in their Reaktive flows. │ │ │ └── Attacker floods the application with data, overwhelming the system due to lack of backpressure, leading to DoS. │ │ ├── Improper Error Handling in Application Reactive Flows: [HIGH RISK PATH] │ │ │ └── AND │ │ │ └── Developers implement inadequate error handling in their Reaktive flows, leading to unhandled exceptions or application crashes. │ │ │ └── Attacker triggers specific errors to cause application instability or DoS. │ ├── Exploit Dependencies of Reaktive [CRITICAL NODE] [HIGH RISK PATH] │ │ └── AND │ │ ├── Identify dependencies used by Reaktive library (check build.gradle.kts or similar). │ │ ├── Discover known vulnerabilities in these dependencies (using vulnerability databases, dependency scanning tools). │ │ └── Exploit these dependency vulnerabilities to compromise the application (e.g., transitive dependency vulnerabilities).

Attack Tree Path: Resource Exhaustion due to Unbounded Streams or Backpressure Issues [CRITICAL NODE] [HIGH RISK PATH]

Attack Vector: Denial of Service (DoS) through resource exhaustion.

  • Why High-Risk:

    • Likelihood: Medium to High - Improper backpressure handling is a common mistake in reactive programming, especially for developers new to the paradigm. Unbounded streams can also arise from incorrect operator usage or logic.
    • Impact: High - Can lead to application crashes, hangs, and unavailability, significantly impacting users and business operations.
    • Effort: Low - Attackers can easily flood the application with data if backpressure is not implemented. Simple tools or scripts can be used to generate high volumes of requests.
    • Skill Level: Low - Requires minimal technical skill to execute a flood attack.
    • Detection Difficulty: Easy - Resource exhaustion is typically easy to detect through standard monitoring tools (CPU usage, memory consumption, thread counts, response times).

  • Attack Steps:

    • Identify Vulnerable Flows: Attackers identify reactive flows in the application where backpressure is not correctly implemented or where streams can become unbounded (e.g., infinite streams without proper limits).
    • Flood with Data: Attackers send a large volume of data or requests to these vulnerable flows, exceeding the application's capacity to process them efficiently.
    • Resource Exhaustion: The application's resources (CPU, memory, threads, network bandwidth) are exhausted, leading to performance degradation, crashes, or complete service unavailability.

Attack Tree Path: Incorrect Backpressure Implementation [CRITICAL NODE] [HIGH RISK PATH]

  • Attack Vector: Denial of Service (DoS) through overwhelming the application.

  • Why High-Risk:

    • Likelihood: High - Backpressure is a core concept in reactive programming, but it can be complex to implement correctly. Developers may overlook it or implement it improperly, especially in complex reactive flows.
    • Impact: High - Similar to resource exhaustion, incorrect backpressure leads to application overload and DoS.
    • Effort: Low - Exploiting missing backpressure is straightforward. Attackers simply need to send data faster than the application can process it.
    • Skill Level: Low - Requires minimal skill to generate and send data.
    • Detection Difficulty: Easy - Easily detectable through performance monitoring, increased latency, and resource usage spikes.

  • Attack Steps:

    • Identify Backpressure Weakness: Attackers identify reactive flows where backpressure is either missing or inadequately implemented. This could be at the application level or within custom reactive operators.
    • Data Flood: Attackers send data to the application at a rate exceeding its processing capacity, bypassing any intended backpressure mechanisms.
    • System Overload: The application becomes overwhelmed, leading to slow responses, timeouts, and eventually, service disruption or crashes.

Attack Tree Path: Improper Error Handling in Application Reactive Flows [HIGH RISK PATH]

  • Attack Vector: Application Instability and Potential Denial of Service.

  • Why High-Risk:

    • Likelihood: Medium to High - Error handling in asynchronous reactive flows can be more complex than in traditional synchronous code. Developers might miss error handling scenarios or implement it incorrectly, leading to unhandled exceptions.
    • Impact: Medium - Can cause application instability, unexpected behavior, and in severe cases, lead to crashes or DoS. May also expose sensitive information in error messages if not handled carefully.
    • Effort: Low - Attackers can often trigger errors by sending malformed input, unexpected requests, or exploiting edge cases in application logic.
    • Skill Level: Low - Requires basic understanding of application inputs and potential error conditions.
    • Detection Difficulty: Easy - Unhandled exceptions and application crashes are usually logged and easily detectable through application monitoring and error reporting systems.

  • Attack Steps:

    • Identify Error Trigger Points: Attackers identify inputs, requests, or actions that can trigger errors within the application's reactive flows. This could involve invalid data, boundary conditions, or specific sequences of events.
    • Trigger Errors: Attackers send crafted requests or inputs designed to trigger these errors repeatedly.
    • Application Instability/DoS: Repeated errors can lead to unhandled exceptions, application crashes, or resource exhaustion due to error handling loops, causing instability or DoS.

Attack Tree Path: Exploit Dependencies of Reaktive [CRITICAL NODE] [HIGH RISK PATH]

  • Attack Vector: Various, depending on the dependency vulnerability (Remote Code Execution, Denial of Service, Information Disclosure, etc.).

  • Why High-Risk:

    • Likelihood: Medium - Dependency vulnerabilities are a common and persistent threat in software development. Reaktive, like most libraries, relies on external dependencies, which can have known vulnerabilities.
    • Impact: High to Critical - The impact depends on the nature of the vulnerability in the dependency. It can range from information disclosure to remote code execution, potentially leading to full application compromise.
    • Effort: Low to Medium - Identifying dependency vulnerabilities is relatively easy using automated scanning tools. Exploiting them may require more effort depending on the vulnerability and available exploits, but many known vulnerabilities have publicly available exploits.
    • Skill Level: Low to Medium - Exploiting known dependency vulnerabilities often requires moderate skill, especially if exploits are readily available.
    • Detection Difficulty: Medium - Vulnerability scanning can detect known dependency vulnerabilities. Intrusion detection systems might detect exploitation attempts depending on the nature of the exploit.

  • Attack Steps:

    • Dependency Analysis: Attackers identify the dependencies used by Reaktive (direct and transitive) by examining project files or using dependency analysis tools.
    • Vulnerability Scanning: Attackers use vulnerability databases and scanning tools to identify known vulnerabilities in these dependencies.
    • Exploit Vulnerability: If vulnerable dependencies are found, attackers attempt to exploit these vulnerabilities. This might involve using publicly available exploits or developing custom exploits.
    • Application Compromise: Successful exploitation of dependency vulnerabilities can lead to various forms of compromise, including remote code execution, data breaches, or denial of service, depending on the vulnerability.