Skip to content

Latest commit

 

History

History
135 lines (104 loc) · 8.71 KB

mitigations.md

File metadata and controls

135 lines (104 loc) · 8.71 KB

Mitigation Strategies Analysis for tokio-rs/axum

Mitigation Strategy: Middleware Ordering and Logic Review

Description:

  1. Document the Pipeline: Create documentation outlining the order of Axum middleware execution using .layer(). Describe each middleware's purpose, inputs, outputs, and dependencies.
  2. Code Comments: Add comments before each .layer() call in your router definition, explaining the middleware's role.
  3. Unit Tests (Order-Specific): Create unit tests that verify the order of middleware execution. Use a test framework and potentially test-specific middleware to inspect the request/response flow.
  4. Integration Tests (End-to-End): Write integration tests simulating user requests to verify the entire middleware chain's behavior, including security checks.
  5. Code Review: During code reviews, explicitly check the middleware order and logic for potential bypasses.

Threats Mitigated:

  • Bypassing Authentication/Authorization: (Severity: Critical) - Incorrect order can allow unauthorized access.
  • Data Leakage: (Severity: High) - Middleware logging sensitive data before authentication could expose it.
  • Logic Errors in Custom Middleware: (Severity: Variable, up to Critical) - Flaws in custom Axum middleware.

Impact:

  • Bypassing Authentication/Authorization: Risk reduced significantly (Critical to Low).
  • Data Leakage: Risk reduced significantly (High to Low).
  • Logic Errors: Risk reduced moderately (severity depends on the error).

Currently Implemented:

  • Basic middleware order is correct (authentication before authorization).
  • Some unit tests for individual middleware, but not order-specific.
  • Inconsistent code comments.

Missing Implementation:

  • Dedicated documentation of the middleware pipeline.
  • Order-specific unit tests.
  • Comprehensive integration tests.
  • Formal code review checklist item.

Mitigation Strategy: Strict Extractor Usage and Validation

Description:

  1. Define Precise Types: Use specific types (e.g., u32, custom enums) in structs representing expected input, rather than generic types like String, for use with Axum extractors.
  2. Use a Validation Library: Integrate a library like validator or garde. Add validation attributes (e.g., #[validate(length(min = 1))]) to struct fields used with Axum extractors like Json, Query, Path.
  3. Handle Validation Errors: After using an extractor (e.g., Json(payload)), call the validation method (e.g., payload.validate()?). Return a structured error response (e.g., JSON with error details) if validation fails. Log the error.
  4. Custom Extractors (if needed): If creating custom Axum extractors, implement FromRequest or FromRequestParts carefully, including thorough input validation.
  5. Unit Tests (Extractor-Specific): Write unit tests targeting Axum extractors, providing valid and invalid input to test extraction and error handling.

Threats Mitigated:

  • Injection Attacks (various types): (Severity: Critical) - Validation prevents malicious data injection.
  • Type Confusion: (Severity: High) - Precise types and validation prevent unexpected data types.
  • Business Logic Errors: (Severity: Variable) - Validation enforces business rules.
  • Panic-Induced Denial of Service: (Severity: Medium) - Validation prevents input that could cause panics.

Impact:

  • Injection Attacks: Risk significantly reduced (Critical to Low).
  • Type Confusion: Risk significantly reduced (High to Low).
  • Business Logic Errors: Risk reduced moderately to significantly.
  • Panic-Induced DoS: Risk significantly reduced (Medium to Low).

Currently Implemented:

  • Basic type definitions used for request payloads.
  • validator crate is a dependency.

Missing Implementation:

  • validator attributes not consistently applied.
  • Inconsistent error handling for validation failures.
  • No unit tests specifically targeting extractor validation.
  • Custom extractors (if any) lack thorough validation.

Mitigation Strategy: Secure State Management (using Axum's State)

Description:

  1. Identify Shared State: Identify all data stored in the Axum application state (accessed via State).
  2. Choose Concurrency Primitives: For each piece of shared state, use Mutex, RwLock, or tokio::sync::Mutex for appropriate concurrency control.
  3. Implement Locking: Wrap access to the shared state with the chosen lock (e.g., state.lock().await). Minimize lock duration.
  4. Minimize State: Avoid storing unnecessary data in the Axum State. Consider external storage for data not needing to be in memory.
  5. Bound State Size: If the state includes collections, implement limits (LRU caching, time-based eviction).
  6. Unit Tests (Concurrency): Simulate concurrent access to the shared state to verify locking.

Threats Mitigated:

  • Race Conditions: (Severity: High) - Concurrent access without locking can corrupt data.
  • Data Leakage: (Severity: High) - Improper access control to state.
  • Denial of Service (State Exhaustion): (Severity: Medium) - Unbounded state growth.

Impact:

  • Race Conditions: Risk significantly reduced (High to Low).
  • Data Leakage: Risk reduced based on data sensitivity and access controls.
  • Denial of Service: Risk significantly reduced (Medium to Low).

Currently Implemented:

  • Application state stores a database connection pool (sqlx::PgPool).
  • tokio::sync::Mutex protects the connection pool.

Missing Implementation:

  • No other shared state currently used.
  • No specific unit tests for concurrent access to the pool (covered implicitly by integration tests).
  • No explicit bounds on collection sizes within the state (not applicable currently).

Mitigation Strategy: Robust FromRequest Implementation (if applicable)

Description: (Only applies if you have custom FromRequest or FromRequestParts implementations in Axum.)

  1. Code Review: Thoroughly review custom FromRequest implementations, focusing on input validation, error handling, and vulnerabilities.
  2. Unit Tests (Comprehensive): Extensive unit tests covering all input scenarios: valid, invalid, missing data, edge cases, error conditions.
  3. Security Audit (if critical): If the extractor handles sensitive data or performs security-critical operations, consider a formal audit.
  4. Follow Secure Coding Practices: Avoid unsafe code unless justified. Use appropriate error handling (don't panic). Validate all input.

Threats Mitigated:

  • Injection Attacks: (Severity: Critical) - Flaws could allow malicious data injection.
  • Authentication/Authorization Bypass: (Severity: Critical) - If involved in auth, flaws could allow bypass.
  • Denial of Service: (Severity: Medium) - Poorly written extractors could be vulnerable.
  • Logic Errors: (Severity: Variable) - Any logic errors could lead to vulnerabilities.

Impact:

  • Depends on the extractor's function. For security-critical extractors, risk reduction is significant (Critical to Low).

Currently Implemented:

  • No custom FromRequest implementations are used.

Missing Implementation:

  • N/A (no custom extractors).

Mitigation Strategy: Denial of Service (DoS) Mitigation (Axum-Specific Aspects)

Description:

  1. Request Body Size Limits: Use Axum's ContentLengthLimit extractor or custom middleware to limit request body sizes. Return a 413 Payload Too Large error if exceeded. This is directly integrated with Axum's request handling.
  2. Rate Limiting (using Axum compatible middleware): Implement rate limiting using middleware like tower-governor within your Axum application. Configure limits based on IP, user ID, etc. Return 429 Too Many Requests.

Threats Mitigated:

  • Large Request Body Attacks: (Severity: Medium) - Limits prevent excessively large requests.
  • Brute-Force Attacks: (Severity: Medium) - Rate limiting mitigates brute-force (e.g., on login).
  • General DoS: (Severity: Medium to High) - Contributes to overall DoS resilience.

Impact:

  • Significantly reduces DoS attack risk. Impact depends on attack type and configuration.

Currently Implemented:

  • ContentLengthLimit is used on endpoints with file uploads.

Missing Implementation:

  • No rate limiting is implemented.