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attack-surface.md

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Attack Surface Analysis for milvus-io/milvus

Attack Surface: gRPC Endpoint Exposure

  • Description: Milvus's core functionality relies on exposed gRPC endpoints for internal component communication and external client access. These endpoints are fundamental to Milvus's operation.
  • Milvus Contribution: Milvus creates and exposes these gRPC endpoints as part of its architecture. The design of Milvus necessitates this exposure.
  • Example: An attacker directly connects to a Milvus query node's gRPC port (bypassing any application-level security) and attempts to execute unauthorized queries or inject malicious data, exploiting a lack of authentication or a vulnerability in Milvus's gRPC handling.
  • Impact: Unauthorized data access, data modification, denial of service, potential remote code execution (if vulnerabilities exist in Milvus's gRPC implementation).
  • Risk Severity: High to Critical (depending on authentication/authorization configuration and the presence of unpatched vulnerabilities).
  • Mitigation Strategies:
    • Network Segmentation: Use firewalls and network policies (e.g., Kubernetes NetworkPolicies) to strictly limit access to Milvus gRPC endpoints. Only allow connections from authorized clients and other Milvus components within the cluster.
    • Authentication & Authorization: Enforce strong authentication (e.g., mutual TLS, JWT) and fine-grained authorization (RBAC) on all Milvus gRPC endpoints. This is a Milvus configuration task.
    • TLS Encryption: Use TLS to encrypt all gRPC communication, preventing eavesdropping and man-in-the-middle attacks. This is configured within Milvus.
    • Regular Updates: Keep Milvus itself (and its embedded gRPC library) updated to the latest versions to patch any security vulnerabilities. This is crucial for mitigating vulnerabilities within Milvus's code.
    • Service Mesh (Optional but Recommended): Consider using a service mesh like Istio or Linkerd for advanced traffic management, security (including mTLS and authorization policies), and observability of gRPC communication specifically for Milvus.

Attack Surface: Metadata Store Compromise

  • Description: Milvus depends entirely on the integrity and availability of its metadata store (etcd, MySQL, or PostgreSQL) for managing all information about collections, partitions, segments, and access control.
  • Milvus Contribution: Milvus writes to and reads from this metadata store constantly. The correctness of Milvus's operation is directly tied to the data in this store.
  • Example: An attacker compromises the etcd cluster used by Milvus and modifies metadata to point to malicious data files, disable authentication, or grant themselves administrator privileges within Milvus.
  • Impact: Data corruption, denial of service, unauthorized data access, complete system compromise of Milvus.
  • Risk Severity: Critical.
  • Mitigation Strategies:
    • Secure the Metadata Store: Apply standard database security best practices to the chosen metadata store. This is essential for Milvus's security.
    • Network Segmentation: Isolate the metadata store on a separate network segment with extremely restricted access, allowing connections only from Milvus components.
    • Authentication & Authorization: Enforce strong authentication and authorization for access to the metadata store, specifically limiting Milvus's access to the minimum required.
    • Encryption: Use encryption at rest and in transit for the metadata store. This protects the data used by Milvus.
    • Regular Backups: Implement regular, secure backups of the metadata store to enable recovery from compromise or data loss. This is crucial for Milvus data recovery.
    • Monitoring: Monitor the metadata store for suspicious activity and unauthorized access attempts, specifically looking for changes that could affect Milvus.

Attack Surface: Insecure Deserialization

  • Description: Milvus uses serialization and deserialization for internal data transfer and when interacting with data stored in object storage. This is intrinsic to Milvus's data handling.
  • Milvus Contribution: Milvus's own code performs the serialization and deserialization. Vulnerabilities here are within Milvus itself.
  • Example: An attacker crafts a malicious serialized object that, when deserialized by Milvus, executes arbitrary code on a Milvus server component.
  • Impact: Remote code execution, complete system compromise of Milvus nodes.
  • Risk Severity: Critical.
  • Mitigation Strategies:
    • Secure Serialization Library: This is primarily the responsibility of the Milvus developers, but users should ensure they are using a Milvus version with a secure serialization library.
    • Input Validation: Milvus (and any application interacting with it) should validate all data before deserialization. This is a shared responsibility.
    • Type Whitelisting: If possible (and supported by Milvus and the serialization library), use type whitelisting to restrict the types of objects that can be deserialized by Milvus.
    • Regular Updates: Keep Milvus itself updated to the latest versions to patch any known deserialization vulnerabilities within Milvus's code. This is the most important mitigation for users.

Attack Surface: Resource Exhaustion (DoS) - Milvus Specific Aspects

  • Description: Milvus's architecture, with its distributed components and query processing logic, is susceptible to resource exhaustion attacks.
  • Milvus Contribution: The design and implementation of Milvus determine its resource consumption patterns and vulnerability to DoS.
  • Example: An attacker sends a flood of complex search queries specifically crafted to exploit inefficiencies in Milvus's query engine, overwhelming query nodes and causing a denial of service. Or, an attacker creates a very large number of collections/partitions, exhausting Milvus's internal metadata management resources.
  • Impact: Denial of service (availability loss) of Milvus.
  • Risk Severity: High.
  • Mitigation Strategies:
    • Rate Limiting: Implement rate limiting on API requests to Milvus. This can be done at the network level or within a proxy in front of Milvus.
    • Resource Quotas: Set resource limits (CPU, memory, connections) on Milvus components (using Kubernetes resource limits, for example). This is a Milvus deployment configuration.
    • Query Complexity Limits: Limit the complexity of search queries accepted by Milvus (e.g., maximum number of results, maximum search radius, limits on filtering expressions). This is a Milvus configuration and application-level concern.
    • Monitoring & Alerting: Monitor Milvus's resource usage (CPU, memory, disk I/O, network bandwidth, connection counts) and configure alerts for high resource consumption specific to Milvus components.
    • Scalability: Design the Milvus deployment to be scalable, allowing it to handle increased load by adding more resources (e.g., more query nodes, more worker nodes). This is a deployment and architecture consideration.
    • Load Testing: Regularly perform load testing specifically against Milvus to identify performance bottlenecks and resource limits within Milvus.