LLMs are increasingly being used to analyze and reason about Active Directory attack graphs, helping identify attack paths, privilege escalation opportunities, chained ACL abuses, ADCS ESC misconfigurations, delegation weaknesses, and other security findings. However, sharing a BloodHound export with a third-party AI also exposes sensitive information such as domain names, usernames, hostnames, group names, GPOs, SIDs, and free-text fields that may contain cleartext credentials. For organizations handling confidential environments, this creates a significant privacy concern.

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HoundMasker addresses this challenge by masking organization-specific identifiers in BloodHound Community Edition JSON exports while preserving the underlying graph structure. ACL relationships, group memberships, delegation rights, sessions, and SID references remain consistent across all files, allowing the masked dataset to retain the same attack paths and relationships as the original graph. This enables AI-assisted analysis without exposing real organizational identities or sensitive metadata.

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The tool also generates a local reverse mapping that allows AI findings to be translated back to the original object names without sharing the mapping outside the environment. Implemented in pure Python using only the standard library, HoundMasker includes structural validation checks and ensures the masked data remains compatible with BloodHound Community Edition imports.​​