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attack-surface.md

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Attack Surface Analysis for facebookresearch/faiss

Attack Surface: 1. Malicious Input Vectors (Index Building & Querying)

  • Description: Attackers craft specific input vectors to exploit Faiss's internal algorithms, causing various negative effects.
  • How Faiss Contributes: This is fundamental to Faiss. Faiss's core purpose is processing vectors, and its algorithms are the direct target.
  • Example: An attacker submits a query vector designed to trigger a division-by-zero or infinite loop within Faiss's distance calculations. Or, during index building, vectors are crafted to create unbalanced clusters, degrading performance.
  • Impact:
    • Denial of Service (DoS): Crashing Faiss, exhausting memory, or causing excessive CPU usage.
    • Information Leakage (Indirect): Revealing index structure or data distribution.
    • Index Corruption (Rare): Potentially corrupting the index.
  • Risk Severity: High (DoS is very likely).
  • Mitigation Strategies:
    • Strict Input Validation: Validate vector dimensionality, data type, and value ranges (min/max, norms). Reject invalid vectors.
    • Fuzz Testing: Use fuzzing tools to generate a wide variety of vectors to test Faiss's robustness. This is crucial.
    • Resource Monitoring: Monitor Faiss's CPU and memory usage. Set limits and alerts.
    • Rate Limiting: Limit the rate of index building and querying.
    • Input Sanitization: Sanitize inputs.

Attack Surface: 2. Index Deserialization Vulnerabilities

  • Description: Attackers provide a malicious Faiss index file that, when loaded, exploits vulnerabilities in the deserialization process.
  • How Faiss Contributes: Faiss's own serialization/deserialization mechanism is the direct attack vector.
  • Example: An attacker uploads a crafted index file that, when loaded by Faiss, triggers a buffer overflow or executes arbitrary code.
  • Impact:
    • Remote Code Execution (RCE): The attacker gains full control of the server.
    • Denial of Service (DoS): Crashing Faiss during loading.
  • Risk Severity: Critical (RCE is a major threat).
  • Mitigation Strategies:
    • Avoid Untrusted Sources: Never load Faiss indexes from untrusted sources. This is the primary defense.
    • Secure Deserialization (If Necessary): If unavoidable, investigate secure deserialization methods or build a secure wrapper. This is complex.
    • Sandboxing: Load the index in a sandboxed environment.
    • Input Validation: Validate loaded index.

Attack Surface: 3. Data Poisoning (Index Modification)

  • Description: If index modification is allowed, attackers with sufficient privileges can add/modify vectors to manipulate search results.
  • How Faiss Contributes: Faiss's APIs for adding, removing, and updating vectors are the direct means of attack.
  • Example: An attacker adds many vectors similar to a specific item to artificially boost its ranking in a recommendation system.
  • Impact: Biased/Incorrect Results: Search results are skewed.
  • Risk Severity: High (impact depends on the application; can be critical).
  • Mitigation Strategies:
    • Strict Access Control: Implement robust authentication and authorization. Follow the principle of least privilege.
    • Auditing: Log all index modification operations.
    • Input Validation: Validate all vectors added to the index.

Attack Surface: 4. Denial of Service (DoS) via Resource Exhaustion (Faiss-Specific Aspects)

  • Description: Attackers overwhelm Faiss, causing resource exhaustion. This focuses on aspects directly related to Faiss's functionality.
  • How Faiss Contributes: Faiss's processing of queries and its index management consume resources.
  • Example:
    • Query Flooding (Targeting Faiss): Sending a massive number of valid but computationally expensive Faiss queries. This is distinct from a generic network flood.
    • Index Bloating (If Modification Allowed): Adding a huge number of vectors to exhaust memory/disk used by Faiss.
  • Impact: The Faiss-based service becomes unavailable.
  • Risk Severity: High.
  • Mitigation Strategies:
    • Rate Limiting (Faiss-Specific): Limit the rate of Faiss queries, potentially with different limits based on query complexity.
    • Resource Monitoring (Faiss-Specific): Monitor Faiss's internal resource usage (memory allocated to the index, etc.).
    • Limit Index Size (If Modification Allowed): Set limits on the total index size or the number of vectors.
    • Data Validation (If Modification Allowed): Validate incoming vectors before adding them.