threat-modeling.md

Threat Model Analysis for protocolbuffers/protobuf

Threat: Buffer Overflow

Description: An attacker sends a specially crafted protobuf message with excessively long fields (strings, repeated fields) that exceeds the allocated buffer size during deserialization. This can overwrite adjacent memory regions. Impact: Denial of Service (DoS) due to application crash, potentially Remote Code Execution (RCE) if the overflow can be controlled to overwrite execution flow. Affected Protobuf Component: Protobuf parsing libraries (C++, Java, Python, etc.), generated deserialization code. Risk Severity: Critical (RCE potential) to High (DoS). Mitigation Strategies:

• Use up-to-date protobuf libraries with patched vulnerabilities.
• Implement and enforce message size limits before deserialization.
• Utilize memory-safe programming practices in protobuf library implementations.
• Employ input validation on message size and field lengths.

Threat: Denial of Service (DoS) through Resource Exhaustion

Description: An attacker sends protobuf messages designed to consume excessive resources (CPU, memory, network) during deserialization. This can be achieved through deeply nested messages, very large fields, or a huge number of repeated fields. The attacker aims to overload the server and make it unresponsive. Impact: Application unavailability, performance degradation, service disruption. Affected Protobuf Component: Protobuf parsing libraries, deserialization process. Risk Severity: High. Mitigation Strategies:

• Implement and enforce message size limits.
• Set limits on message nesting depth during deserialization.
• Limit the number of elements in repeated fields during deserialization.
• Implement timeouts for deserialization operations to prevent indefinite resource consumption.
• Resource monitoring and rate limiting of incoming protobuf messages.

Threat: Logic Bugs due to Malformed Messages

Description: An attacker sends technically valid protobuf messages that contain unexpected or malicious data values or combinations. This can exploit vulnerabilities in the application's logic that processes the deserialized data, leading to incorrect behavior or security breaches. For example, sending negative values where only positive values are expected. Impact: Data corruption, incorrect application state, bypassed security checks, potential security breaches depending on application logic. Affected Protobuf Component: Application code that processes deserialized protobuf data. Risk Severity: High (in scenarios leading to security breaches). Mitigation Strategies:

• Implement robust input validation on deserialized protobuf data within the application logic.
• Define clear and strict protobuf schema definitions to minimize ambiguity.
• Thoroughly test application logic with various valid and invalid protobuf messages, including edge cases and boundary conditions.
• Use data type validation and range checks after deserialization.

Threat: Type Confusion Vulnerabilities

Description: An attacker exploits potential weaknesses in protobuf implementations related to handling different data types during deserialization. By crafting messages that cause the deserializer to misinterpret data types, the attacker can trigger unexpected behavior, memory corruption, or other vulnerabilities. Impact: Potential for memory corruption, unexpected application behavior, or security breaches. Affected Protobuf Component: Protobuf parsing libraries, deserialization process, potentially generated code. Risk Severity: High (in scenarios leading to memory corruption or security breaches). Mitigation Strategies:

• Use well-vetted and actively maintained protobuf libraries.
• Stay updated with security advisories and patches for protobuf libraries.
• Adhere to best practices in protobuf schema design and data type usage.
• Consider using static analysis tools to detect potential type-related issues in generated code or protobuf library usage.

Threat: Vulnerabilities in Protobuf Libraries

Description: An attacker exploits known or zero-day vulnerabilities in the protobuf parsing and serialization libraries themselves (e.g., in C++, Java, Python implementations). These vulnerabilities could be triggered by sending specially crafted protobuf messages. Impact: Denial of Service (DoS), Remote Code Execution (RCE), Information Disclosure, depending on the specific vulnerability. Affected Protobuf Component: Protobuf parsing and serialization libraries (e.g., libprotobuf, protobuf-java, protobuf Python library). Risk Severity: Critical (RCE potential) to High (DoS, Information Disclosure). Mitigation Strategies:

• Use actively maintained and supported protobuf libraries from trusted sources (e.g., the official protobuf repository).
• Regularly update protobuf libraries to the latest versions to patch known vulnerabilities.
• Monitor security advisories and vulnerability databases for protobuf libraries (e.g., CVE databases, project security mailing lists).

Threat: Vulnerabilities in Generated Code

Description: An attacker exploits bugs or vulnerabilities in the code generated by the protoc compiler for specific programming languages. While less common than library vulnerabilities, issues in code generation could introduce security weaknesses in the application. Impact: Similar to library vulnerabilities, potential for DoS, RCE, Information Disclosure. Affected Protobuf Component: Code generated by the protoc compiler (language-specific generated code). Risk Severity: High (in scenarios leading to RCE or significant DoS). Mitigation Strategies:

• Use stable and well-tested versions of the protoc compiler.
• Review generated code for potential security issues, especially if using custom or less common protobuf features.
• Report any suspected vulnerabilities in the protoc compiler to the protobuf project maintainers.
• Consider using static analysis tools to scan generated code for potential vulnerabilities.