Attack Surface: Deserialization of Large Payloads (DoS)
An attacker sends extremely large data payloads to be deserialized, overwhelming the application's resources (CPU, memory) and causing a Denial of Service.
Serde's core function is to deserialize data. Without explicit limits, Serde will attempt to process arbitrarily large inputs, making the application vulnerable to DoS attacks via oversized payloads.
An attacker sends a multi-gigabyte JSON file to an endpoint that uses serde_json::from_reader. Serde attempts to parse and deserialize this massive file, leading to memory exhaustion and application crash.
Application unavailability, service disruption, potential server crash.
High
- Limit Input Size: Implement strict limits on the size of incoming requests or data streams before they are passed to Serde for deserialization. Enforce these limits at the web server level or within the application's input handling logic.
- Streaming Deserialization: Utilize streaming deserialization techniques (if supported by the chosen format and Serde implementation) to process data in chunks, preventing the need to load the entire payload into memory at once.
- Resource Monitoring and Throttling: Monitor application resource usage (CPU, memory) and implement request throttling or rate limiting to mitigate the impact of large payload attacks.
Attack Surface: Deserialization of Deeply Nested Structures (DoS)
An attacker crafts input data with excessively deep nesting (e.g., nested JSON objects or XML elements) that consumes excessive stack space or heap memory during deserialization, leading to stack overflow or heap exhaustion and DoS.
Serde's recursive deserialization process can be exploited by deeply nested inputs. The depth of recursion during parsing and deserialization can grow proportionally to the nesting level in the malicious input, potentially exceeding resource limits.
An attacker sends a JSON payload with hundreds of levels of nested objects to an endpoint using serde_json::from_str. Serde's deserialization process recursively traverses this structure, potentially exceeding stack limits and causing a stack overflow, crashing the application.
Application crash, service disruption, potential server instability.
High
- Limit Nesting Depth: Implement checks to restrict the maximum allowed nesting depth during deserialization. This might require custom deserialization logic or format-specific parser configurations if available.
- Iterative Deserialization (where applicable): Explore alternative deserialization approaches that are less prone to stack overflow issues, potentially using iterative or non-recursive techniques if feasible for the chosen format and use case.
- Resource Limits (Stack Size): While less ideal, increasing stack size limits might temporarily mitigate stack overflow in some cases, but addressing the root cause by limiting nesting depth is the more robust solution.
Attack Surface: Billion Laughs Attack (XML/YAML formats)
When using Serde with XML or YAML formats, an attacker can exploit entity expansion features in these formats to create exponentially expanding payloads (e.g., "Billion Laughs" in XML). This leads to excessive memory consumption and CPU usage during parsing by Serde, causing DoS.
If Serde is used with format implementations that support entity expansion (like serde_xml_rs or serde_yaml), the application becomes vulnerable to entity expansion attacks if the underlying parser (used by Serde) doesn't have adequate protection. Serde's choice of format and reliance on the parser contribute to this attack surface.
An attacker sends an XML payload containing nested entity definitions that, when expanded by the XML parser during Serde deserialization, result in a gigabyte-sized string from a small initial payload. Parsing this expanded data consumes excessive resources and leads to DoS.
Denial of Service, application crash, service disruption.
High
- Disable Entity Expansion (if possible): Configure the XML or YAML parser used by Serde to completely disable entity expansion features if they are not essential for the application's functionality. Consult the documentation of the specific Serde format implementation for options to disable entity expansion.
- Limit Entity Expansion Depth/Size: If entity expansion cannot be disabled, configure the parser to impose strict limits on the maximum depth and size of entity expansions to prevent exponential growth and resource exhaustion.
- Use Secure Parsers: Ensure that the underlying XML or YAML parsing library used by Serde is up-to-date and known to be resistant to entity expansion attacks. Modern Rust YAML/XML libraries often include built-in protections.
- Prefer Less Vulnerable Formats: If feasible, consider using serialization formats that are inherently less susceptible to entity expansion attacks, such as JSON or binary formats, to minimize this risk.
Attack Surface: Vulnerabilities in Custom Deserialize Implementations
If developers implement custom Deserialize logic for specific types, these implementations themselves can introduce vulnerabilities due to programming errors, logic flaws, or insufficient security considerations within the custom code that is executed during Serde's deserialization process.
Serde provides the mechanism and entry point for custom deserialization through the Deserialize trait. While Serde's core library is generally secure, the overall security of deserialization becomes dependent on the correctness and security of these developer-provided custom implementations that are invoked by Serde.
A custom Deserialize implementation for a sensitive data type might contain a buffer overflow vulnerability when handling certain input lengths, or it might fail to properly sanitize input strings, leading to injection vulnerabilities when the deserialized data is later used.
Varies significantly depending on the nature of the vulnerability introduced in the custom deserialization logic. Could range from data corruption and logic errors to security bypasses, arbitrary code execution (in extreme cases, though less likely in safe Rust), and memory safety issues.
High (can be critical depending on the vulnerability and data handled)
- Rigorous Code Review and Security Audits: Subject all custom
Deserializeimplementations to thorough code reviews and security audits by experienced developers or security experts. - Comprehensive Testing: Implement comprehensive unit and integration tests for custom deserialization logic, including fuzzing and negative testing with malformed or unexpected inputs.
- Follow Secure Coding Practices: Adhere to secure coding principles when writing custom deserialization logic. Avoid common vulnerabilities such as buffer overflows, format string bugs, injection flaws, and improper input validation.
- Prefer Built-in Deserialization and Validation: Whenever possible, leverage Serde's built-in deserialization capabilities and established validation libraries instead of writing complex custom deserialization and validation code from scratch.
- Principle of Least Privilege: If custom deserialization logic handles sensitive data or performs privileged operations, ensure that the application operates with the principle of least privilege to limit the potential impact of vulnerabilities in the custom deserialization code.