threat-modeling.md

Threat Model Analysis for google/flatbuffers

Threat: Integer Overflow/Underflow in Offset Calculations (Within FlatBuffers Library)

• Description: An attacker crafts a FlatBuffer with carefully chosen integer values that, when used in offset calculations within the FlatBuffers library itself (e.g., during vtable lookup, object traversal), cause integer overflows or underflows. This is distinct from application-level misuse of values after deserialization. The attacker aims to trigger out-of-bounds reads or writes within the FlatBuffers buffer.
• Impact: Potential for out-of-bounds memory access within the FlatBuffers buffer, leading to crashes or potentially exploitable vulnerabilities (though less direct than traditional buffer overflows). This could lead to information disclosure or, in rare cases, potentially controlled memory corruption within the context of the FlatBuffer itself.
• Affected Component: Internal FlatBuffers library functions that perform offset calculations, particularly those involved in vtable lookups and object/array traversal (e.g., GetVOffset(), and related internal functions).
• Risk Severity: High (Potentially Critical if it leads to exploitable memory corruption within the FlatBuffers library's context).
• Mitigation Strategies:
  • Primary Mitigation: Rely on the FlatBuffers library developers to address these issues through rigorous internal testing, fuzzing, and the use of safe integer arithmetic. This is a library-level concern.
  • Secondary Mitigations (Application Level):
    • Use the FlatBuffers Verifier. While not a complete solution, the Verifier does perform some checks for invalid offsets and buffer boundaries, which can help catch some instances of this threat.
    • Keep the FlatBuffers library up-to-date to benefit from any security patches.
    • Fuzz test the application's integration with FlatBuffers, providing malformed FlatBuffers as input. This can help identify if the application is triggering any latent vulnerabilities within the library.

Threat: Schema Evolution Mismatch Leading to Type Confusion (Within FlatBuffers)

• Description: A significant schema evolution mismatch between the client and server, combined with the use of force_defaults or similar features, could potentially lead to a form of type confusion within the FlatBuffers library. For example, if a field changes type (e.g., from int to string) and the older schema is used to read data written with the newer schema, the library might misinterpret the underlying bytes. This is a subtle but potentially serious issue. The attacker would need to control the schema version used by one side of the communication.
• Impact: Potentially exploitable memory corruption within the FlatBuffers library's context, leading to crashes or, in rare cases, potentially controlled memory corruption. This is more likely if the type change involves a significant difference in size or interpretation (e.g., integer to pointer).
• Affected Component: FlatBuffers deserialization logic, particularly when handling schema evolution and default values. The interaction between force_defaults, field re-typing, and versioning is the key area.
• Risk Severity: High (Potentially Critical if it leads to exploitable memory corruption).
• Mitigation Strategies:
  • Primary Mitigation: Avoid drastic schema changes that involve re-typing fields, especially between fundamentally different types (e.g., scalar to table, integer to string).
  • Strict Versioning: Implement very strict schema versioning and never allow communication between components with incompatible schema versions. Reject data with unknown or unsupported schema versions.
  • Avoid force_defaults with Significant Schema Changes: Be extremely cautious when using force_defaults (or similar features that provide default values for missing fields) in scenarios where the schema has undergone significant changes, especially type changes. It's generally safer to explicitly handle missing fields in the application logic based on the schema version.
  • Thorough Testing: Extensively test all schema evolution scenarios, including cases where the client and server are using different versions. Fuzz test with data generated using different schema versions.

Threat: Untrusted Data Source (Leading to Library Exploitation)

• Description: The application receives FlatBuffers data from an untrusted source. While all the above threats are exacerbated by untrusted data, this entry emphasizes that any vulnerability within the FlatBuffers library itself becomes significantly more dangerous when the input data is attacker-controlled. The attacker can craft malicious FlatBuffers specifically designed to trigger edge cases or vulnerabilities within the library.
• Impact: Amplifies the impact of any other FlatBuffers library vulnerability (e.g., integer overflows, type confusion). The attacker can directly target the library's internal mechanisms.
• Affected Component: The entire FlatBuffers deserialization and processing pipeline within the library.
• Risk Severity: Critical.
• Mitigation Strategies:
  • Primary Mitigation: Assume all FlatBuffers data from external sources is potentially malicious.
  • Defense in Depth: Implement all applicable mitigations from the other threats, even those that seem primarily application-level. This creates multiple layers of defense.
  • Sandboxing (High Value): Consider sandboxing the FlatBuffers deserialization and processing code to limit the impact of any library-level vulnerabilities. This could involve running the code in a separate process with restricted privileges. This is a crucial mitigation for high-security environments.
  • Library Updates: Keep the FlatBuffers library meticulously up-to-date to benefit from security patches.
  • Fuzzing (Targeted): Fuzz test the application's integration with FlatBuffers, specifically focusing on the library's handling of malformed input.

Threat: Denial of Service via Deeply Nested Objects (Stack Overflow within Library)

• Description: An attacker provides a FlatBuffer with excessively deep nesting of objects, potentially leading to a stack overflow during recursive deserialization within the FlatBuffers library itself. This is distinct from application-level recursion; it targets the library's internal handling of nested structures.
• Impact: Denial of Service (DoS) due to stack exhaustion, causing the application to crash.
• Affected Component: Deserialization logic within the FlatBuffers library, specifically the code that handles nested tables and structs.
• Risk Severity: High
• Mitigation Strategies: * Library-Level Mitigation: Rely on the FlatBuffers library to handle this internally, potentially through iterative deserialization or stack size limits. * Application-Level Mitigation:
  • Use the FlatBuffers Verifier. It might have some checks for excessive nesting, although this is primarily a library responsibility.
  • Keep the FlatBuffers library up-to-date.
  • Fuzz test the application with deeply nested FlatBuffers.
  • If possible, design your schema to avoid excessively deep nesting.