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This package wraps the dAWShund project in two different servers so you can enumerate AWS IAM data, evaluate effective permissions and obtain a high‑level summary without having to remember a handful of separate scripts. The upstream project describes itself as a way to “put a leash on naughty permissions” and provides three main components:
- sAWSage – enumerates IAM principals and resource policies and
consolidates them into a single JSON document. Supported resources include IAM groups, roles and users and a subset of resource based policies such as AWS Backup, EFS, KMS, Lambda, S3, SNS and SQS. The consolidated file (sawsage.json) is produced under policies/ after running the scrip.
- Gerakina – feeds the consolidated policies into AWS’s
simulate-principal-policy API and produces a new JSON file, effective_permissions.json, listing which actions are allowed, explicitly denied or implicitly denied for each principal. This step may take some time because it calls the AWS API for every unique combination of principal, action and resource.
- dAWShund – imports the effective permissions into a Neo4j
database and builds a graph of principals and resources. It generates permissions4j.json and dawshund.json suitable for browsing in Neo4j and BloodHound respectively. Using the Neo4j functionality is optional; the HTTP server included here provides a lightweight summary instead.
dAWShund depends on Python, the AWS CLI an
Works with Claude (desktop and mobile), Cursor, Windsurf, VS Code, and any MCP-compatible AI app.
Category: Developer Tools
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This plugs directly into your AI and gives it new abilities it didn't have before. samvas-codes/dawshund_mcp. Once connected, just ask your AI to use it. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
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What's New
Imported from awesome:punkpeye/awesome-mcp-servers
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