attack-surface.md

Attack Surface Analysis for open-source-parsers/jsoncpp

Attack Surface: Denial of Service (DoS) via Large JSON Input

• Description: An attacker sends excessively large or complex JSON documents to the application, overwhelming server resources (CPU, memory) and causing service disruption or unavailability.
• How jsoncpp contributes: jsoncpp must parse and process the entire JSON document in memory. If the document is excessively large or deeply nested, the parsing process can consume significant resources, potentially exceeding available limits within jsoncpp's processing.
• Example: An attacker sends a JSON payload containing a multi-megabyte string or an array with millions of elements to an endpoint that uses jsoncpp to parse it. This could cause the application to become unresponsive or crash due to memory exhaustion or CPU overload within the parsing process of jsoncpp.
• Impact: Service disruption, application unavailability, potential financial loss due to downtime.
• Risk Severity: High
• Mitigation Strategies:
  • Input Size Limits (Application Level): Implement limits on the maximum size of incoming JSON payloads before they are passed to jsoncpp for parsing. Reject requests exceeding a reasonable threshold.
  • Nesting Depth Limits (Application Level): Limit the maximum nesting depth allowed in JSON documents before parsing. Reject requests exceeding this limit.
  • Resource Monitoring and Throttling (Application Level): Monitor application resource usage (CPU, memory) and implement throttling mechanisms to limit the rate of incoming requests, especially from suspicious sources, to protect against resource exhaustion during jsoncpp parsing.

Attack Surface: Denial of Service (DoS) via Deeply Nested JSON Structures

• Description: An attacker sends JSON documents with extremely deep nesting levels. This can lead to stack overflow or excessive recursion during parsing within jsoncpp, causing the application to crash or become unresponsive.
• How jsoncpp contributes: Recursive parsing algorithms within jsoncpp might be vulnerable to stack overflow if the nesting depth exceeds the stack size limits during its internal parsing process.
• Example: An attacker sends a JSON payload like {"a": {"a": {"a": ... {"value": 1} ...}}} with thousands of nested "a" keys. Parsing this deeply nested structure could exhaust the stack space and crash the application due to jsoncpp's parsing logic.
• Impact: Service disruption, application crash, potential for exploitation if the crash leads to further vulnerabilities.
• Risk Severity: High
• Mitigation Strategies:
  • Nesting Depth Limits (Application Level - as mentioned above): Enforce strict limits on the maximum allowed nesting depth in JSON documents before parsing with jsoncpp.
  • Iterative Parsing (Library Level Consideration): Ideally, jsoncpp itself should be designed to use iterative parsing techniques instead of purely recursive ones to mitigate stack overflow risks. Application developers have limited control over this, but using the latest version of jsoncpp is recommended as it may contain improvements in parsing algorithms.

Attack Surface: Buffer Overflow during String Parsing

• Description: jsoncpp might incorrectly handle string lengths during parsing, especially when dealing with escape sequences, Unicode characters, or very long strings, leading to writing beyond allocated buffers within jsoncpp's memory management.
• How jsoncpp contributes: If string length calculations are inaccurate or buffer allocation is insufficient within jsoncpp's code, parsing strings, especially those with escape sequences or multi-byte characters, could cause jsoncpp to write past the end of allocated memory buffers.
• Example: An attacker sends a JSON string containing a large number of escape sequences (e.g., \uXXXX repeated many times) or a very long string without proper length encoding. If jsoncpp miscalculates the final string length after decoding escape sequences, it could write beyond the buffer allocated for the string during its internal string processing.
• Impact: Memory corruption, potential for arbitrary code execution, DoS.
• Risk Severity: High
• Mitigation Strategies:
  • Input Validation (String Length Limits - Application Level): Limit the maximum length of strings allowed in JSON input before parsing with jsoncpp.
  • Robust String Parsing Logic (Library Level): Ensure that jsoncpp uses robust string parsing logic that correctly handles escape sequences, Unicode characters, and string length calculations. Using the latest version of jsoncpp is crucial as it may contain fixes for such vulnerabilities.
  • Memory Safety Practices (Library Level): jsoncpp should employ memory-safe programming practices to prevent buffer overflows. Application developers rely on the library's implementation for this and should choose a well-maintained and audited version of jsoncpp.

Attack Surface: Deserialization Gadgets (Indirect - Application Usage, but Critical if Exploitable)

• Description: If the application uses parsed JSON data to instantiate objects or perform actions without proper validation, attackers might be able to craft JSON payloads that trigger unintended object creation or function calls, leading to exploitation. While not a direct vulnerability in jsoncpp itself, jsoncpp facilitates the parsing that makes this attack possible. If the application's design relies heavily on dynamic instantiation based on parsed JSON, this becomes a critical attack surface enabled by using jsoncpp to process external data.
• How jsoncpp contributes: jsoncpp parses the JSON and provides the data to the application. If the application directly and unsafely uses this data to create objects or execute code, jsoncpp becomes a necessary component in this attack vector by providing the attacker-controlled data in a parsed format.
• Example: An application receives JSON that specifies a class name and constructor arguments. If the application directly uses this JSON (parsed by jsoncpp) to instantiate an object of the specified class without proper validation, an attacker could provide a malicious class name and arguments to execute arbitrary code upon object instantiation.
• Impact: Arbitrary code execution, data breaches, complete system compromise.
• Risk Severity: Critical (if exploitable in the application's design)
• Mitigation Strategies:
  • Input Validation and Sanitization (Crucial - Application Level): Thoroughly validate and sanitize all data extracted from parsed JSON (by jsoncpp) before using it to instantiate objects, perform actions, or construct commands. This is paramount to prevent deserialization gadget attacks.
  • Principle of Least Privilege (Application Level): Avoid directly using user-controlled JSON data (parsed by jsoncpp) to determine object types or function calls. If object creation is necessary based on JSON, use a whitelist of allowed classes and strictly validate constructor arguments.
  • Secure Deserialization Practices (Application Level): Follow secure deserialization best practices. In many cases, it's better to avoid dynamic object creation based on external input altogether. Design the application to minimize or eliminate reliance on deserialization of untrusted data into executable code or objects.