Attack Surface: Out-of-Bounds Read via Malicious Buffer
- Description: Crafting a FlatBuffer payload with manipulated offsets that cause the FlatBuffers library to read data outside the allocated buffer.
- FlatBuffers Contribution: FlatBuffers' zero-copy deserialization relies heavily on offsets within the buffer. Maliciously crafted offsets can trick the library into accessing memory outside the intended buffer boundaries.
- Example: An attacker sends a FlatBuffer payload where an offset to a vector is intentionally set to point beyond the end of the buffer. When the application tries to access elements of this vector, the FlatBuffers library attempts to read memory outside the buffer, potentially disclosing sensitive data from adjacent memory regions or causing a crash.
- Impact: Information Disclosure, Denial of Service (crash).
- Risk Severity: High.
- Mitigation Strategies:
- Input Validation (Buffer Size): Enforce limits on the maximum size of incoming FlatBuffer payloads.
- Robust Error Handling: Implement proper error handling in the application to catch potential out-of-bounds read errors during deserialization.
- Fuzzing: Use fuzzing techniques to test the application's FlatBuffers deserialization logic with malformed payloads.
- Memory Safety Tools: Utilize memory safety tools during development and testing (e.g., AddressSanitizer, MemorySanitizer).
Attack Surface: Integer Overflow in Offset Calculation
- Description: Exploiting integer overflows during offset calculations within the FlatBuffers library, leading to incorrect memory access.
- FlatBuffers Contribution: Offset calculations are fundamental to FlatBuffers' deserialization process. Integer overflows in these calculations can lead to unexpected and potentially dangerous memory accesses.
- Example: An attacker crafts a FlatBuffer payload with very large offsets that, when added together during deserialization, result in an integer overflow. This overflow can cause the calculated memory address to wrap around, potentially leading to reads or writes to unintended memory locations, including heap corruption.
- Impact: Incorrect Memory Access, Heap Corruption, potentially Arbitrary Code Execution.
- Risk Severity: High to Critical (Critical if arbitrary code execution is possible).
- Mitigation Strategies:
- Input Validation (Offset Ranges): Consider sanity checks on the overall structure and size of the payload.
- Safe Integer Arithmetic: Ensure the FlatBuffers library and the application code use safe integer arithmetic practices.
- Memory Safety Tools: Memory safety tools (AddressSanitizer, MemorySanitizer) can help detect memory corruption issues.
- Regular Audits: Conduct security audits to review the application's FlatBuffers usage.
Attack Surface: Insufficient Application-Level Validation
- Description: Relying solely on FlatBuffers schema validation and neglecting to perform application-level validation on deserialized data.
- FlatBuffers Contribution: FlatBuffers provides schema validation, but this validation is primarily focused on data structure and type correctness, not on semantic or business logic constraints.
- Example: A FlatBuffer schema defines a field for "user_id" as an integer. Schema validation ensures the data type is correct. However, it doesn't validate if the "user_id" is a valid user ID within the application's context. An attacker could send a valid FlatBuffer payload with a malicious "user_id" that passes schema validation but should be rejected by application logic.
- Impact: Security Bypass, Data Manipulation, Unauthorized Access.
- Risk Severity: High.
- Mitigation Strategies:
- Application-Level Validation: Always perform thorough application-level validation on deserialized data to enforce business logic rules and security constraints.
- Principle of Least Privilege: Validate and sanitize data based on the principle of least privilege.
- Secure Coding Practices: Follow secure coding practices to ensure application logic correctly handles and validates deserialized data.