hoardd-client's Introduction

hoardd-client

This is a golang implementation of an elasticsearch client that queries the Hoardd OSINT platform for emails and passwords.

There is currently no public version of Hoardd, as it is fully owned by Optiv. However, a closed beta is starting soon.

The full Hoardd tool will be released publicly eventually (hopefully in 2021)

No actual data will be provided with the public release, but infrastructure and processing code will be present that will allow anyone to set up their own private instance of Hoardd. They could then use this client and the other code contained in the Hoardd repo to process data breaches and other OSINT data to suit their needs. :)

Basic Usage

Download release for your OS from Releases
chmod +x
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Profit

Help Output

Usage of ./hoardd-client:
  -c, --config string      Path to YAML config file
      --csv-file string    CSV output filename. Only email, password, and breach_name are written to the CSV file
      --debug              Enable debug output
  -d, --domain strings     domains to search
  -e, --email strings      Emails to search
  -i, --index string       Elasticsearch index name i.e. leak_linkedin (default "leak_*")
      --json-file string   JSON output filename. The entire JSON document will be written in JSON Lines format.
      --limit int          Maximum number of results to return (default 1,000,000) - set to 0 for no limit
      --pass strings       Passwords to search
  -p, --password string    Elasticsearch password
  -q, --query string       Raw elasticsearch query
      --url string         ElasticsSearch url
  -u, --username string    Elasticsearch username
  -v, --verbose            Enable verbose output

Notes

file size estimate: 50MB/1 million results
query time estimate: 3-5 min/1 million results
the speed of this client is bound by the scroll API in elasticsearch, which is not built to export huge amounts of data. if you need to output huge amounts of data, I recommend you use spark to query elasticsearch directly, then write the files to disk from spark.

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