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attack-surface.md

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Attack Surface Analysis for facebook/rocksdb

Attack Surface: 1. Memory Management Vulnerabilities (Heap Overflow, Use-After-Free)

  • Description: Bugs in RocksDB's C++ codebase related to memory allocation and deallocation can lead to heap overflows (writing beyond allocated memory) or use-after-free (accessing memory after it has been freed).
  • RocksDB Contribution: RocksDB is implemented in C++ and performs complex memory management for caching, data structures, and internal operations. Bugs in this memory management logic are inherent to the software.
  • Example: A specially crafted input or operation triggers a buffer overflow in a RocksDB internal function during compaction, allowing an attacker to overwrite adjacent memory regions.
  • Impact: Code execution, denial of service (crash), data corruption.
  • Risk Severity: Critical to High
  • Mitigation Strategies:
    • Keep RocksDB Up-to-Date: Regularly update RocksDB to the latest stable version. Security patches often address memory management vulnerabilities.
    • Static and Dynamic Analysis: Utilize static analysis tools and dynamic analysis/fuzzing to identify potential memory management bugs in RocksDB during development and testing (contribute to upstream RocksDB project).
    • Memory Sanitizers: Run RocksDB in testing and development environments with memory sanitizers (like AddressSanitizer, MemorySanitizer) to detect memory errors early (contribute to upstream RocksDB project).

Attack Surface: 2. Configuration Parsing Vulnerabilities

  • Description: RocksDB has a vast number of configuration options. Bugs in the parsing logic for these options, especially when read from configuration files or strings, could lead to unexpected behavior or vulnerabilities.
  • RocksDB Contribution: RocksDB's extensive configuration system requires parsing complex option strings and file formats. Errors in this parsing process within RocksDB itself are the source of this attack surface.
  • Example: A vulnerability in parsing a specific RocksDB option string allows an attacker to inject unexpected values or commands, leading to a crash or misconfiguration that weakens security.
  • Impact: Denial of service (crash), misconfiguration leading to security weaknesses, potentially information disclosure or other unexpected behavior.
  • Risk Severity: Medium to High (depending on the specific vulnerability and its impact - prioritizing High severity cases here).
  • Mitigation Strategies:
    • Keep RocksDB Up-to-Date: Updated versions often include fixes for parsing vulnerabilities.
    • Careful Configuration: Thoroughly review and understand all RocksDB configuration options. Avoid using overly complex or obscure configurations unless absolutely necessary.
    • Configuration Validation: If possible, validate RocksDB configurations programmatically before applying them to detect invalid or potentially dangerous settings (contribute to upstream RocksDB project by reporting parsing issues).

Attack Surface: 3. Denial of Service via Resource Exhaustion (Internal RocksDB Issues)

  • Description: Attackers can craft inputs or operations that exploit inefficiencies or bugs within RocksDB itself to cause excessive resource consumption (CPU, memory, disk I/O), leading to denial of service. This is distinct from application-level DoS, focusing on RocksDB's internal vulnerabilities.
  • RocksDB Contribution: RocksDB's performance can be affected by various factors, and internal inefficiencies or bugs in algorithms (e.g., compaction, indexing) can be exploited for DoS. Resource leaks within RocksDB are also a direct contribution.
  • Example: An attacker sends a series of specially crafted read requests that trigger an inefficient internal algorithm in RocksDB, leading to excessive CPU usage and service unavailability. Another example could be triggering unbounded memory growth due to a memory leak within RocksDB.
  • Impact: Denial of service, performance degradation, application unavailability.
  • Risk Severity: Medium to High (depending on the ease of exploitation and impact on application availability - prioritizing High severity cases here).
  • Mitigation Strategies:
    • Resource Limits: Configure RocksDB with appropriate resource limits (e.g., memory limits, write rate limits) to mitigate some resource exhaustion scenarios.
    • Query Monitoring and Throttling (Application Level): While mitigation is application level, understanding RocksDB performance is key. Monitor RocksDB query performance to identify potential DoS patterns.
    • Keep RocksDB Up-to-Date: Performance improvements and bug fixes in newer versions can mitigate some DoS vulnerabilities.
    • Report Performance Issues Upstream: If you identify specific input patterns or operations that cause significant performance degradation in RocksDB, report them to the RocksDB community.

Attack Surface: 4. Data Corruption due to Internal Bugs

  • Description: Bugs within RocksDB's internal logic (e.g., in compaction, write path, or recovery mechanisms) could lead to data corruption, where data stored in RocksDB becomes inconsistent or unusable.
  • RocksDB Contribution: RocksDB is a complex storage engine with intricate internal processes. Bugs in these processes are directly within RocksDB and can lead to data integrity issues.
  • Example: A race condition during compaction within RocksDB corrupts SST files, leading to data loss or inconsistencies when the application attempts to read data. Another example could be a bug in the WAL replay mechanism within RocksDB causing data corruption during recovery after a crash.
  • Impact: Data corruption, data loss, application malfunction, data integrity violations.
  • Risk Severity: Medium to High (depending on the severity of data corruption and its impact on the application - prioritizing High severity cases here).
  • Mitigation Strategies:
    • Checksums and Data Verification: Enable checksum verification in RocksDB to detect data corruption during reads.
    • Regular Backups and Recovery Testing: Implement regular backups of RocksDB data and test the restore process to ensure data can be recovered in case of corruption (application level mitigation for RocksDB internal issues).
    • Monitoring and Alerting: Monitor RocksDB logs and metrics for signs of data corruption or errors. Set up alerts to detect potential issues early (application level mitigation for RocksDB internal issues).
    • Keep RocksDB Up-to-Date: Bug fixes in newer versions often address data corruption issues.
    • Thorough Testing (Contribute Upstream): Conduct rigorous testing, including fault injection and stress testing, to identify potential data corruption vulnerabilities and report them to the RocksDB community.