attack-surface.md

Attack Surface Analysis for serde-rs/serde

Attack Surface: 1. Untrusted Deserialization

• Description: Deserializing data from an untrusted source (e.g., network input, user-supplied files) without proper validation. This is the most common and dangerous attack vector.
• How Serde Contributes: serde is the direct target here. It's the engine that processes the potentially malicious input and performs the deserialization. The vulnerability exists because serde must process the input to deserialize it, and without prior validation, that input can be malicious.
• Example: An attacker sends a crafted JSON payload that, when deserialized by serde, triggers unexpected behavior in a custom Deserialize implementation (even if the implementation itself is seemingly safe, type confusion or unexpected values can lead to issues), leading to remote code execution (RCE) or a denial-of-service (DoS). Another example is integer overflow caused by attacker providing large number.
• Impact: Remote Code Execution (RCE), Denial of Service (DoS), Data Corruption, Information Disclosure.
• Risk Severity: Critical
• Mitigation Strategies:
  • Input Validation (Pre-Deserialization): Always validate the input before passing it to serde. Use format-specific validation (e.g., JSON Schema, XML Schema, custom validation logic for binary formats). Validate data types, sizes, ranges, and structure. This is the primary defense.
  • Data Firewall: Treat all external input as hostile. Implement a "data firewall" that strictly enforces allowed data formats and structures.
  • Avoid deserialize_any: Minimize the use of deserialize_any as it bypasses type checking during deserialization.
  • Strict Type Definitions: Use precise and unambiguous type definitions in your Rust code.
  • Robust Error Handling: Handle all serde errors meticulously. Treat any deserialization error as a potential attack. Don't ignore errors.

Attack Surface: 2. Denial of Service (DoS) via Resource Exhaustion

• Description: An attacker crafts input designed to consume excessive resources (memory, CPU, stack) during serde's deserialization process, leading to application crashes or unavailability.
• How Serde Contributes: serde's deserialization process is directly manipulated by the attacker's input. The vulnerability exists because serde needs to allocate memory and perform operations based on the structure and content of the input. If the input specifies excessively large structures or deep nesting, serde will attempt to process it, leading to resource exhaustion.
• Example:
  • Deep Nesting: An attacker sends JSON with deeply nested objects, causing a stack overflow during serde's recursive deserialization.
  • Large Collections: An attacker specifies a huge size for a vector or string in the serialized data, leading to a massive memory allocation by serde.
• Impact: Denial of Service (DoS), Application Crash.
• Risk Severity: High
• Mitigation Strategies:
  • Input Size Limits: Enforce strict limits on the total size of the input data before it reaches serde.
  • Depth Limits: Limit the maximum nesting depth of data structures during deserialization. Many serde format implementations offer configuration options for this (this is a direct mitigation against serde's recursive behavior).
  • Allocation Limits: Implement custom deserializers or wrappers that track and limit memory allocation during serde's operation. This is a more advanced technique but provides fine-grained control over serde's resource usage.
  • Resource Monitoring: Monitor resource usage (CPU, memory) during deserialization and terminate the process if limits are exceeded. This is a last line of defense.
  • Fuzz Testing: Use fuzz testing (e.g., cargo fuzz) to specifically target serde's deserialization process with malformed inputs to identify DoS vulnerabilities.