Skip to content

Latest commit

 

History

History
48 lines (39 loc) · 5.55 KB

attack-surface.md

File metadata and controls

48 lines (39 loc) · 5.55 KB

Attack Surface Analysis for apache/arrow

Attack Surface: 1. Deserialization of Untrusted Arrow Data

  • 1. Deserialization of Untrusted Arrow Data

    • Description: Processing Arrow data (IPC, Flight, or file formats) from an untrusted source without proper validation. This is the most significant direct risk.
    • How Arrow Contributes: Arrow's serialization format is complex and performance-optimized. This complexity, combined with potential implementation bugs, makes it susceptible to crafted malicious inputs that can exploit vulnerabilities during deserialization.
    • Example: An attacker sends a crafted Arrow IPC message with a malicious schema designed to cause excessive memory allocation (DoS). A more severe example is a crafted message with invalid offsets or lengths that triggers a buffer overflow or out-of-bounds read/write during deserialization, potentially leading to RCE.
    • Impact: Denial of Service (DoS), Remote Code Execution (RCE), Information Disclosure.
    • Risk Severity: Critical (if RCE is possible) or High (for DoS).
    • Mitigation Strategies:
      • Strict Schema Whitelisting: Only accept data conforming to a predefined, rigorously vetted set of schemas. Reject any unknown or overly complex schemas. This is the most important mitigation.
      • Comprehensive Input Validation: Perform thorough validation of both the schema and the data against that schema. Verify lengths, offsets, data types, and nested structure depths. Don't assume any input is valid.
      • Hardened Resource Limits: Enforce strict, non-negotiable limits on memory allocation, batch sizes, and the complexity of nested data structures.
      • Extensive Fuzz Testing: Continuously fuzz test Arrow's deserialization routines with a wide range of malformed and edge-case inputs.
      • Memory-Safe Language Usage: Prioritize the use of memory-safe languages (e.g., Rust) for the core deserialization logic to prevent memory corruption vulnerabilities.

Attack Surface: 2. Unsafe Extension Type Handling

  • 2. Unsafe Extension Type Handling

    • Description: Vulnerabilities arising from insecurely implemented or improperly used Arrow extension types.
    • How Arrow Contributes: Arrow's extensibility mechanism allows users to define custom data types and associated logic (serialization, deserialization, computation). This flexibility directly introduces a risk if extensions are not developed and used with extreme care.
    • Example: An attacker provides data that uses a custom extension type. This extension type has a vulnerability in its deserialization logic, allowing the attacker to execute arbitrary code (RCE). Another example is an extension that leaks sensitive information during its processing.
    • Impact: Denial of Service (DoS), Remote Code Execution (RCE), Information Disclosure.
    • Risk Severity: Critical (if RCE is possible) or High.
    • Mitigation Strategies:
      • Strict Extension Whitelisting: Only allow a predefined, thoroughly vetted set of extension types to be loaded and used. This is crucial.
      • Mandatory Secure Coding Practices: Enforce rigorous secure coding practices when developing extension types, with a particular focus on secure deserialization and preventing any form of untrusted code execution.
      • Mandatory Sandboxing: If an extension type must execute user-provided code (highly discouraged), run it in a strictly sandboxed environment with severely restricted privileges.
      • Mandatory Code Reviews: Require thorough, independent code reviews of all extension type implementations before deployment or use.

Attack Surface: 3. Algorithmic Complexity Attacks on Compute Kernels

  • 3. Algorithmic Complexity Attacks on Compute Kernels
    • Description: Exploiting worst-case performance scenarios of Arrow's compute kernels (sorting, filtering, aggregation, etc.) with crafted input.
    • How Arrow Contributes: While Arrow kernels are optimized, some may have algorithmic weaknesses that can be triggered by specific input patterns. This is a direct consequence of Arrow's computational capabilities.
    • Example: An attacker provides input to a sorting kernel that forces it into its worst-case O(n^2) behavior, consuming excessive CPU and causing a DoS. Another example is crafting input to trigger excessive hash collisions in a hash-based aggregation.
    • Impact: Denial of Service (DoS).
    • Risk Severity: High.
    • Mitigation Strategies:
      • Input Data Profiling and Limits: Analyze expected input data characteristics. Set limits on data size and complexity that could trigger worst-case scenarios.
      • Resource Monitoring and Throttling: Continuously monitor CPU and memory usage of kernels. Terminate or throttle operations exceeding predefined limits.
      • Kernel Auditing: Regularly audit kernel implementations for potential algorithmic complexity vulnerabilities, especially for operations with known worst-case scenarios.
      • Input Sanitization (where feasible): If possible, pre-process or sanitize input to mitigate known worst-case triggers (e.g., limiting unique values).
      • Rate Limiting: Limit the rate at which data can be processed by specific kernels.