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attack-surface.md

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Attack Surface Analysis for facebook/folly

Attack Surface: Memory Corruption Vulnerabilities (Heap Overflow, Use-After-Free, Double-Free)

  • Description: Vulnerabilities arising from incorrect memory management within Folly's custom memory allocation and data structures, leading to memory corruption.
  • Folly Contribution: Folly provides custom allocators (e.g., fbstring, small_vector) and data structures. Bugs in the implementation of these Folly components can directly lead to memory corruption.
  • Example: A heap buffer overflow vulnerability within Folly's fbstring implementation when handling excessively long strings, allowing an attacker to overwrite memory beyond the allocated buffer.
  • Impact: Code execution, denial of service, information disclosure, privilege escalation.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Regularly update Folly: Apply security patches and bug fixes by updating to the latest stable Folly version.
    • Utilize memory safety tools during development: Employ static analysis (e.g., clang-tidy) and dynamic analysis (e.g., AddressSanitizer) to detect memory errors in code using Folly's memory management features.
    • Focus code reviews on Folly memory usage: Specifically scrutinize code sections that interact with Folly's custom allocators and data structures during code reviews.
    • Fuzz testing Folly integrations: Use fuzzing techniques to test application components that heavily rely on Folly's memory management, especially when processing external or untrusted input.

Attack Surface: Concurrency and Race Conditions in Asynchronous Operations

  • Description: Vulnerabilities stemming from improper synchronization or flawed design in Folly's asynchronous programming primitives (Futures, Promises, Executors), leading to race conditions and unpredictable behavior.
  • Folly Contribution: Folly's core asynchronous programming model relies on Futures, Promises, and Executors. Incorrect implementation or misuse of these Folly components can introduce race conditions.
  • Example: A race condition in code using Folly Futures where multiple asynchronous tasks concurrently access and modify shared data without proper synchronization mechanisms provided by Folly or correctly applied by the developer, leading to data corruption.
  • Impact: Data corruption, inconsistent application state, denial of service, potential for further exploitation depending on the nature of the race condition.
  • Risk Severity: High
  • Mitigation Strategies:
    • Rigorous concurrency design and review: Carefully design and thoroughly review concurrent code paths utilizing Folly's asynchronous primitives, ensuring correct synchronization and data protection.
    • Leverage Folly's thread-safety features: Utilize Folly's provided thread-safety mechanisms and annotations to aid in identifying and preventing concurrency issues.
    • Concurrency stress testing: Perform rigorous testing under high concurrency to expose potential race conditions. Employ tools like ThreadSanitizer to detect data races in Folly-based concurrent code.
    • Adhere to Folly concurrency best practices: Follow recommended patterns and best practices for utilizing Folly's Futures, Promises, and Executors to minimize concurrency risks.

Attack Surface: Network Protocol Parsing Vulnerabilities in Folly Networking Utilities

  • Description: Vulnerabilities within Folly's networking utilities used for parsing network protocols, potentially leading to buffer overflows, denial of service, or other exploits when processing maliciously crafted network data.
  • Folly Contribution: Folly provides networking utilities that might include components for parsing network protocols. Vulnerabilities in these specific Folly-provided parsing utilities can be exploited.
  • Example: A buffer overflow vulnerability in a hypothetical HTTP parsing utility provided by Folly, triggered by an overly long header in a malicious HTTP request processed using Folly's networking components.
  • Impact: Denial of service, potential remote code execution if the parsing vulnerability is severe, information disclosure.
  • Risk Severity: High to Critical (depending on the specific vulnerability and protocol)
  • Mitigation Strategies:
    • Prefer well-established parsing libraries: When possible, utilize robust and actively maintained parsing libraries for standard network protocols instead of relying on potentially less mature or less scrutinized parsing utilities within Folly itself.
    • Strict network input validation: Implement rigorous input validation and sanitization for all network data before processing it with Folly's networking utilities.
    • Fuzz testing network protocol handling: Employ fuzzing techniques specifically targeting the network protocol parsing code paths that utilize Folly's networking utilities, using malformed and edge-case network inputs.
    • Keep Folly updated for network security patches: Ensure timely updates of Folly to benefit from any security patches related to its networking components and protocol parsing utilities.
    • Consider defense-in-depth: Implement network security measures (firewalls, intrusion detection) to mitigate the impact of potential network parsing vulnerabilities.