Convenience tool for hashcat.

Run hashcathelper -h for help. The program is structured in subcommands. See hashcathelper <subcommand> -h for more information.

First, it bruteforces all LM hashes and uses the results to crack the corresponding NT hashes. Then, a large wordlist (recommendation: Crackstation) is used together with a large ruleset (recommendation: OneRule) to crack all remaining NT hashes. The list of account names is prepended to the wordlist, as hashcat does not automatically check if the account name is the password.

The pwdump format is the one which is used by secretsdump or Meterpreter's hashdump function.

Example:

$ hashcathelper ntlm dc01.ntds

Output interesting statistics about the cracked passwords. It is meant to be used together with the output of the ntlm subcommand, but passwords which were obtained elsewhere can be analyzed as well.

It takes the following files as an input:

• Password hashes in the pwdump format
• Cracked passwords with accounts (output of the ntlm subcommand)
• Plain passwords

At least one of those is required. Ideally, you pass the hashes and the output of the ntlm subcommand.

By default, computer accounts and accounts which are marked as disabled in the pwdump file (like secretsdump -user-status does) will be disregarded.

Additionally, you can pass the path to a file containing account names to be used as a filter. Only the accounts whose names are listed in this file will be considered. This is useful if you are only interested in statistics regarding active accounts and did not use secretsdump.py -user-status, for example. Or you want the statistics regarding all accounts with admin in their name. Or statistics regarding kerberoastable users.

In addition to a filter file, you can also pass a Cypher query (some are predefined) and the appropriate credentials to use information from a BloodHound database.

Example:

$ hashcathelper analytics \
    -H dc01.ntds \
    -A dc01.ntds.out \
    -F kerberoastable_accounts.txt \
    -f text -o report.txt

The report comes as text, HTML, XLSX, or in JSON. The different sections contain different degrees of detail:

• 1: Only show statistics
• 2 (default): Show some password information such as top 10 lists
• 3: Include full credentials of certain accounts, such as which accounts have blank passwords or clusters of accounts with the same passwords
• 4: Include a full list of all credentials

Use this subcommand to interact with the database. Results from the analytics subcommand can be submitted and collected in a database. This enables us to view statistics for each entry, for example how they compare to other customers. We can now make statements like this: 57% of all passwords could be cracked, which puts you in the bottom 20th percentile.

Use hashcathelper db submit <filename> to submit a result and hashcathelper db stats <id> to view statistics for one entry.

This subcommand lets you insert new relationships into an existing BloodHound database. It takes a BloodHound URI, a report in JSON format (with degree of detail equal to three or higher) and the domain name and creates edges between user objects that share the same password. This enables you to create graphs like this, which immediately shows you offenders of password reuse among the administrator team:

This picture is the result of a query like this:

MATCH p=((a:User)-[r:SamePassword*1..2]-(b:User))
WHERE ALL(x in r WHERE STARTNODE(x).objectid > ENDNODE(x).objectid)
AND ANY(c in [a,b] WHERE c.admincount OR c.name =~ '(?i)adm_.*')
RETURN p

It might need some manual modification depending on the particular naming scheme for admin accounts. See customqueries.json for more queries. You can add these to ~/.config/bloodhound/customqueries.json.

Note that you can create reports with the analytics subcommand without having to actually crack anything; a JSON report can be created from just the hashes, which already enables us to see password reuse.

Also, not all edges of a cluster are inserted, because the numbers of actual edges grows very quickly. Instead, one member of a cluster is chosen as the "center" and all other members have edges to this one member. So the property may not look transitive in BloodHound even though it is. Keep this in mind.

To be done; stay tuned.

The recommended way to install this package is to use pipx to pull it from PyPI:

$ pipx install hashcathelper

As with any other proper Python package, pip and virtual environments can be used as usual.

The typical workflow starts with using secretsdump on a domain controller:

$ secretsdump.py <domain>/<admin>:<password>@<dc> -user-status -just-dc-ntlm -outputfile hashes.txt

This is passed to hashcathelper for cracking:

$ hashcathelper ntlm hashes.txt

Note that several files can be passed and cracked in parallel without it taking longer.

Then, reports can be generated:

$ hashcathelper analytics -H hashes.txt -A hashes.txt.out -f json -o hashes.json

If secretsdump was run with -user-status, deactivated accounts are automatically disregarded. Computer accounts (those that end on $) are also disregarded. You can restrict analysis to a group of accounts by passing another file with -F. That file needs to contain one account name per line, without the UPN suffix (see below for more information).

In the last step, you can submit the report to the database:

$ hashcathelper db submit hashes.json

If you have enough data, you can retrieve statistics about the data set:

$ hashcathelper db stats
INFO - Connection to database: sqlite:////home/cracker/.local/share/hashcathelper/hch_db.sqlite
The database holds information about 94037 accounts in 16 entries.
Key                                                Value    Mean    Std. Dev.    Perc.
-----------------------------------------------  -------  ------  -----------  -------
Accounts where password was cracked (%)            66.66   56.91        13.41       25
Accounts with nonunique password (%)               46.11   23.09        11.78        0
Accounts where username equals the password (%)     1.36    4.02        11.1        25
Accounts with a non-empty LM hash (%)               3.19    8.42        16.76       50
Accounts with an empty password (%)                 0       1.17         2.87       50
Largest baseword cluster (%)                       45.2    10.22        10           0
Average length of cracked passwords                 8.39    9.58         0.84        6

The last column shows the percentile. It should be read as "this result is better than X% of all other results", so higher is better. These values can be visualized as radar chart using third-party tools:

The output from secretsdump contains lines that start with the account name. The format looks like <domain>\<account name>, however, that is not the domain. It is the UPN suffix and can be entirely independent of the domain name -- it just coincides with the domain name by default. Especially after migrating an account from domain A to domain B, the UPN suffix will not change, but the domain name obviously will.

Hashcathelper ignores the UPN suffix pretty much everywhere. All accounts in one file are assumed to belong to the same domain. And that is actually the case if the file has been created by using secretsdump on a domain controller -- unless you used the -use-vss flag, then there is a chance you might encounter duplicate entries.

The config file (located at ${XDG_CONFIG_HOME:-$HOME/.config}/hashcathelper/hashcathelper.conf or the CWD) should look like this:

[DEFAULT]

# Path to hashcat binary
hashcat_bin = /home/cracker/hashcat/hashcat-latest

# Path to hashcat rule set (OneRule is recommended)
rule = /home/cracker/hashcat/rules/OneRule.rule

# Path to hashcat wordlist (Crackstation is recommended)
wordlist = /home/cracker/wordlists/crackstation.txt

# URI to database
db_uri = sqlite:////home/cracker/.local/share/hashcathelper/stats.sqlite

# Optional: Path to HIBP database
# Must be a sorted list of NT hashes in upper case
# Download here: https://haveibeenpwned.com/Passwords
hibp_db = /home/cracker/wordlists/pwned-passwords-ntlm-ordered-by-hash-v8.txt