Skip to content

Latest commit

 

History

History
49 lines (42 loc) · 5.48 KB

attack-surface.md

File metadata and controls

49 lines (42 loc) · 5.48 KB

Attack Surface Analysis for dzenbot/dznemptydataset

Attack Surface: Untrusted Schema Input

  • Description: The application accepts schema definitions for dznemptydataset from untrusted sources (e.g., user input, external APIs) without proper validation.
  • How dznemptydataset Contributes: The library's core functionality is creating datasets based on schemas. Unvalidated schema input directly impacts dznemptydataset's behavior.
  • Example: A web form allows users to define the columns and data types of an EmptyDataset. An attacker submits a schema with an extremely large number of columns, a deeply nested structure, or malicious data type definitions.
  • Impact:
    • Denial of Service (DoS) due to excessive resource consumption (memory, CPU).
    • Potential for type confusion attacks if the attacker can manipulate data types.
    • Unexpected behavior in downstream data processing that relies on the dznemptydataset schema.
  • Risk Severity: High
  • Mitigation Strategies:
    • Strict Input Validation: Implement rigorous validation of all schema input, using a whitelist approach for allowed data types, column names, and schema structures. Reject any input that does not conform.
    • Schema Definition Control: Define schemas programmatically within the application's trusted code, not allowing direct user input to define the dznemptydataset schema.
    • Schema Validation Library: Use a dedicated schema validation library (e.g., jsonschema for JSON-based schemas) to enforce constraints on the dznemptydataset schema.
    • Resource Limits: Implement limits on the size and complexity of schemas accepted by dznemptydataset (e.g., maximum number of columns, maximum nesting depth).

Attack Surface: Type Confusion Vulnerabilities (within dznemptydataset or its interaction)

  • Description: The dznemptydataset library itself, or its interaction with other components, misinterprets data types, leading to unexpected behavior or security vulnerabilities. This focuses on internal flaws or interactions directly related to how dznemptydataset handles types.
  • How dznemptydataset Contributes: The library is responsible for managing data types within the empty dataset. Any internal flaws in its type handling logic are directly attributable to the library.
  • Example: dznemptydataset internally misinterprets a string field as an integer due to a bug in its type handling code. This misinterpretation, when interacting with another component, leads to a buffer overflow.
  • Impact:
    • Unexpected Behavior: The application may crash or produce incorrect results due to dznemptydataset's incorrect type handling.
    • Code Execution (in severe cases): If the type confusion within dznemptydataset can be exploited to overwrite memory.
    • Data Corruption.
  • Risk Severity: High
  • Mitigation Strategies:
    • Code Review (of dznemptydataset): Thoroughly review the dznemptydataset library's source code for potential type handling issues. This is crucial.
    • Unit and Integration Tests (focused on dznemptydataset): Create comprehensive tests specifically for dznemptydataset that cover type handling scenarios, including edge cases and boundary conditions.
    • Fuzzing (of dznemptydataset): Consider fuzzing the dznemptydataset library to identify potential type-related vulnerabilities.

Attack Surface: Deserialization of Untrusted dznemptydataset Data

  • Description: The application deserializes dznemptydataset objects (or data intended to create dznemptydataset objects) from untrusted sources without proper validation. This is specifically about deserializing data into a dznemptydataset.
  • How dznemptydataset Contributes: If the library provides functionality to create datasets from serialized data (e.g., a serialized schema), and this functionality is used with untrusted input, it's a direct attack vector.
  • Example: An application receives a JSON payload containing a serialized dznemptydataset schema. An attacker injects malicious code into the serialized schema, which is executed upon deserialization when creating the dznemptydataset.
  • Impact:
    • Arbitrary Code Execution: Deserialization vulnerabilities can often lead to the execution of arbitrary code.
    • Data Corruption.
    • Denial of Service.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Avoid Unsafe Deserialization: Do not use inherently unsafe serialization formats like Python's pickle to serialize or deserialize dznemptydataset objects or their schemas.
    • Secure Deserialization Libraries: Use well-vetted and secure serialization/deserialization libraries (e.g., json with proper validation) when working with dznemptydataset data.
    • Schema-Aware Deserialization (for dznemptydataset): If possible, use a deserialization process that is aware of the expected dznemptydataset schema and validates the incoming data against it during deserialization.
    • Input Validation After Deserialization: Treat the deserialized data used to create or populate the dznemptydataset as untrusted and perform thorough validation after deserialization.