Please read the initial JARM blog post for more information.

JARM is an active Transport Layer Security (TLS) server fingerprinting tool.

JARM fingerprints can be used to:

• Quickly verify that all servers in a group have the same TLS configuration.
• Group disparate servers on the internet by configuration, identifying that a server may belong to Google vs. Salesforce vs. Apple, for example.
• Identify default applications or infrastructure.
• Identify malware command and control infrastructure and other malicious servers on the Internet.

JARM support has been or is being added to:
SecurityTrails
Shodan
BinaryEdge
RiskIQ
Palo Alto Networks
Censys
360

Run JARM

python3 jarm.py [-h] [-i INPUT] [-p PORT] [-v] [-V] [-o OUTPUT] [-j] [-P PROXY] [domain/IP]
Example:
% python3 jarm.py www.salesforce.com
Domain: www.salesforce.com
Resolved IP: 23.50.225.123
JARM: 2ad2ad0002ad2ad00042d42d00000069d641f34fe76acdc05c40262f8815e5

To use it with Python 2 you'll need the ipaddress module:

pip install -r requirements.txt

Batch run JARM on a large list at speed

./jarm.sh <list> <output_file>
Example:
% ./jarm.sh alexa500.txt jarm_alexa_500.csv

Example Output

Domain	JARM
salesforce.com	2ad2ad0002ad2ad00042d42d00000069d641f34fe76acdc05c40262f8815e5
force.com	2ad2ad0002ad2ad00042d42d00000069d641f34fe76acdc05c40262f8815e5
google.com	27d40d40d29d40d1dc42d43d00041d4689ee210389f4f6b4b5b1b93f92252d
youtube.com	27d40d40d29d40d1dc42d43d00041d4689ee210389f4f6b4b5b1b93f92252d
gmail.com	27d40d40d29d40d1dc42d43d00041d4689ee210389f4f6b4b5b1b93f92252d
facebook.com	27d27d27d29d27d1dc41d43d00041d741011a7be03d7498e0df05581db08a9
instagram.com	27d27d27d29d27d1dc41d43d00041d741011a7be03d7498e0df05581db08a9
oculus.com	29d29d20d29d29d21c41d43d00041d741011a7be03d7498e0df05581db08a9

How JARM Works

Before learning how JARM works, it’s important to understand how TLS works. TLS and its predecessor, SSL, are used to encrypt communication for both common applications like Internet browsers, to keep your data secure, and malware, so it can hide in the noise. To initiate a TLS session, a client will send a TLS Client Hello message following the TCP 3-way handshake. This packet and the way in which it is generated is dependent on packages and methods used when building the client application. The server, if accepting TLS connections, will respond with a TLS Server Hello packet.

TLS servers formulate their Server Hello packet based on the details received in the TLS Client Hello packet. The manner in which the Server Hello is formulated for any given Client Hello can vary based on how the application or server was built, including:

• Operating system
• Operating system version
• Libraries used
• Versions of those libraries used
• The order in which the libraries were called
• Custom configuration

All of these factors lead to each TLS Server responding in a unique way. The combinations of factors make it unlikely that servers deployed by different organizations will have the same response.

JARM works by actively sending 10 TLS Client Hello packets to a target TLS server and capturing specific attributes of the TLS Server Hello responses. The aggregated TLS server responses are then hashed in a specific way to produce the JARM fingerprint.

This is not the first time we’ve worked with TLS fingerprinting. In 2017 we developed JA3/S, a passive TLS client/server fingerprinting method now found on most network security tools. But where JA3/S is passive, fingerprinting clients and servers by listening to network traffic, JARM is an active server fingerprinting scanner. You can find out more about TLS negotiation and JA3/S passive fingerprinting here.

The 10 TLS Client Hello packets in JARM have been specially crafted to pull out unique responses in TLS servers. JARM sends different TLS versions, ciphers, and extensions in varying orders to gather unique responses. Does the server support TLS 1.3? Will it negotiate TLS 1.3 with 1.2 ciphers? If we order ciphers from weakest to strongest, which cipher will it pick? These are the types of unusual questions JARM is essentially asking the server to draw out the most unique responses. The 10 responses are then hashed to produce the JARM fingerprint.

The JARM fingerprint hash is a hybrid fuzzy hash, it uses the combination of a reversible and non-reversible hash algorithm to produce a 62 character fingerprint. The first 30 characters are made up of the cipher and TLS version chosen by the server for each of the 10 client hello's sent. A "000" denotes that the server refused to negotiate with that client hello. The remaining 32 characters are a truncated SHA256 hash of the cumulative extensions sent by the server, ignoring x509 certificate data. When comparing JARM fingerprints, if the first 30 characters are the same but the last 32 are different, this would mean that the servers have very similar configurations, accepting the same versions and ciphers, though not exactly the same given the extensions are different.

After receiving each TLS server hello message, JARM closes the connection gracefully with a FIN as to not leave the sockets open.

It is important to note that JARM is a high-performance fingerprint function and should not be considered, or confused with, a secure crypto function. We designed the JARM fingerprint to be human consumable as much as machine consumable. This means it is small enough to eyeball, share, and tweet with enough room for contextual details.

How JARM Can Be Used to Identify Malicious Servers

Malware command and control (C2) and malicious servers are configured by their creators like any other server and then deployed across their fleet. These therefore tend to produce unique JARM fingerprints. Below are examples of common malware and offensive tools and the JARM overlap with the Alexa Top 1M websites (as of Oct. 2020):

Malicious Server C2	JARM Fingerprint	Overlap with Alexa Top 1M
Trickbot	22b22b09b22b22b22b22b22b22b22b352842cd5d6b0278445702035e06875c	0
AsyncRAT	1dd40d40d00040d1dc1dd40d1dd40d3df2d6a0c2caaa0dc59908f0d3602943	0
Metasploit	07d14d16d21d21d00042d43d000000aa99ce74e2c6d013c745aa52b5cc042d	0
Cobalt Strike	07d14d16d21d21d07c42d41d00041d24a458a375eef0c576d23a7bab9a9fb1	0
Merlin C2	29d21b20d29d29d21c41d21b21b41d494e0df9532e75299f15ba73156cee38	303

With little to no overlap of the Alexa Top 1M Websites, it should be unlikely for a host within an organization to connect to a server with these JARM fingerprints.

JARM Team

John Althouse - Original idea, concept and project lead
Andrew Smart - Concept and testing
RJ Nunnally - Programing and testing
Mike Brady - Programing and testing

Rewritten in Python for operational use by Caleb Yu