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Attack Tree Analysis for serde-rs/serde

Objective: Compromise Application Using Serde

Attack Tree Visualization

[CRITICAL NODE] Compromise Application Using Serde ├── [HIGH-RISK PATH] Exploit Deserialization Process │ ├── [HIGH-RISK PATH] Format-Specific Vulnerabilities (Parser Exploits) │ │ ├── [CRITICAL NODE] JSON Parser Vulnerabilities (serde_json) │ │ ├── [CRITICAL NODE] YAML Parser Vulnerabilities (serde_yaml) │ ├── [CRITICAL NODE] [HIGH-RISK PATH] Deserialization of Untrusted Data without Validation │ │ ├── [CRITICAL NODE] Application directly deserializes user-provided input without sanitization or validation │ └── [HIGH-RISK PATH] Denial of Service via Large/Complex Data └── [HIGH-RISK PATH] Information Leakage via Serialization

Attack Tree Path: [CRITICAL NODE] Compromise Application Using Serde

  • Attack Vector: This is the root goal. Any successful exploitation of the sub-nodes will lead to compromising the application using Serde.
  • Breakdown: Attackers aim to leverage weaknesses in Serde's deserialization, serialization, or its integration within the application to achieve various levels of compromise, ranging from Denial of Service to Remote Code Execution and Data Breaches.

Attack Tree Path: [HIGH-RISK PATH] Exploit Deserialization Process

  • Attack Vector: Focuses on vulnerabilities arising during the deserialization of data using Serde. Deserialization is often a more complex and error-prone process than serialization, especially when handling untrusted input.
  • Breakdown: Attackers target the process of converting serialized data back into application objects. This path encompasses vulnerabilities like type confusion, parser exploits, and logic flaws triggered by unexpected deserialization behavior.

Attack Tree Path: [HIGH-RISK PATH] Format-Specific Vulnerabilities (Parser Exploits)

  • Attack Vector: Exploits vulnerabilities within the parsers used by Serde format crates (like serde_json and serde_yaml) to handle specific data formats.
  • Breakdown:
    • Parser Bugs: Attackers attempt to trigger bugs in the parsing logic of format crates. These bugs can include:
      • Integer Overflows: Sending data that causes integer overflows in parser calculations, potentially leading to memory corruption or unexpected behavior.
      • Stack Overflows: Crafting deeply nested or recursive data structures that exhaust the parser's stack space, causing crashes or potentially allowing for code execution.
      • Denial of Service: Sending malformed or excessively complex data that causes the parser to consume excessive CPU or memory, leading to a Denial of Service.
    • Format-Specific Attacks (YAML): For formats like YAML, specific features can be abused:
      • YAML Anchors and Aliases: Exploiting YAML's anchor and alias mechanism to create deeply nested or recursive structures that cause resource exhaustion or unexpected parsing behavior.

Attack Tree Path: [CRITICAL NODE] JSON Parser Vulnerabilities (serde_json)

  • Attack Vector: Specifically targets vulnerabilities within the serde_json crate, which is commonly used for JSON deserialization with Serde.
  • Breakdown: Attackers focus on finding and exploiting bugs or weaknesses in the serde_json parser implementation. This could involve:
    • Fuzzing the serde_json parser with various malformed or edge-case JSON inputs to discover crashes or unexpected behavior.
    • Analyzing known vulnerabilities in JSON parsers in general and attempting to apply similar techniques to serde_json.

Attack Tree Path: [CRITICAL NODE] YAML Parser Vulnerabilities (serde_yaml)

  • Attack Vector: Specifically targets vulnerabilities within the serde_yaml crate, used for YAML deserialization with Serde. YAML parsers are often more complex than JSON parsers, potentially leading to a wider range of vulnerabilities.
  • Breakdown: Similar to JSON parser vulnerabilities, but with a focus on serde_yaml and YAML-specific features:
    • Exploiting parser bugs through fuzzing and vulnerability analysis.
    • Abusing YAML-specific features like anchors, aliases, and directives to cause resource exhaustion, logic errors, or potentially more severe vulnerabilities.

Attack Tree Path: [CRITICAL NODE] [HIGH-RISK PATH] Deserialization of Untrusted Data without Validation

  • Attack Vector: This is a critical vulnerability pattern where the application directly deserializes data received from untrusted sources (e.g., user input, network requests) without any prior sanitization or validation.
  • Breakdown:
    • Direct Deserialization of User Input: The application takes user-provided data (e.g., from web forms, API requests) and directly feeds it into a Serde deserialization function without any checks.
    • Bypass of Security Measures: This completely bypasses any potential input validation or sanitization that should be performed before deserialization.
    • Exploitation of Downstream Vulnerabilities: This directly exposes the application to all the deserialization vulnerabilities mentioned previously (Type Confusion, Parser Exploits, Logic Bugs). An attacker can craft malicious serialized data to trigger these vulnerabilities.

Attack Tree Path: [CRITICAL NODE] Application directly deserializes user-provided input without sanitization or validation

  • Attack Vector: This node represents the specific coding practice that leads to the "Deserialization of Untrusted Data without Validation" vulnerability.
  • Breakdown: This is a coding flaw where developers mistakenly assume that deserialization itself is a form of input validation or that untrusted data can be safely deserialized without prior checks. This is fundamentally insecure and should be avoided.

Attack Tree Path: [HIGH-RISK PATH] Denial of Service via Large/Complex Data

  • Attack Vector: Attackers aim to cause a Denial of Service by sending excessively large or complex serialized data to the application, overwhelming its resources during deserialization.
  • Breakdown:
    • Large Data Payloads: Sending extremely large serialized data (e.g., very long JSON strings, massive YAML documents) that consume excessive memory during parsing and deserialization, leading to memory exhaustion and application crashes.
    • Complex Data Structures: Sending deeply nested or highly complex data structures (e.g., deeply nested JSON objects/arrays, YAML documents with many anchors and aliases) that exhaust CPU resources during parsing and deserialization, causing CPU starvation and slow response times.

Attack Tree Path: [HIGH-RISK PATH] Information Leakage via Serialization

  • Attack Vector: Exploits unintentional or insecure serialization practices that lead to the exposure of sensitive information.
  • Breakdown:
    • Accidental Serialization of Sensitive Data: Developers may inadvertently include sensitive data (e.g., passwords, API keys, personal information) in data structures that are serialized for logging, debugging, or communication with other systems.
    • Exposure through Logs, Network Traffic, etc.: The serialized data containing sensitive information is then exposed through:
      • Application logs (if serialized data is logged).
      • Network traffic (if serialized data is transmitted over the network without proper encryption or access control).
      • Error messages or debugging outputs.