threat-modeling.md

Threat Model Analysis for dzenbot/dznemptydataset

Threat: Deserialization of Untrusted Data

• Threat: Deserialization of Untrusted Data

  • Description: An attacker provides malicious serialized data (e.g., a crafted Pickle object or a manipulated JSON payload) to an application endpoint that uses dznemptydataset for deserialization. If dznemptydataset or its dependencies have vulnerabilities in their deserialization process, the attacker could achieve arbitrary code execution. This threat is contingent on the application actually using dznemptydataset for deserialization, which isn't explicitly shown in the provided code but is a common pattern. This is a direct threat because the vulnerability would reside within dznemptydataset's handling of deserialization (or its direct use of a vulnerable deserialization library).
  • Impact: Complete system compromise, remote code execution, data exfiltration.
  • Affected Component: Potentially any part of dznemptydataset involved in serialization/deserialization (if such functionality exists). This might involve custom serialization methods or reliance on external libraries like pickle.
  • Risk Severity: Critical (if deserialization of untrusted data is used).
  • Mitigation Strategies:
    • Avoid Untrusted Deserialization: The best mitigation is to never deserialize data from untrusted sources using potentially vulnerable libraries.
    • Secure Deserialization: If deserialization is absolutely necessary, use a secure serialization format (like JSON) and a well-vetted, secure deserialization library. Never use pickle with untrusted data.
    • Input Validation (Pre-Deserialization): Before deserializing any data, rigorously validate its structure and content against a strict schema. Reject any input that doesn't conform.
    • Sandboxing: Perform deserialization within a restricted environment (e.g., a container or a separate process with limited privileges) to contain the impact of a potential vulnerability.