With little to no overlap of the Alexa Top 1M Websites, it should be extremely unlikely for a host within an organizatio

When taking a closer look at Cobalt Strike, a common offensive security tool used by red teams and threat actors alike, we found obvious indicators that most of the results were indeed Cobalt Strike, with server names including things like “redteam.server” “cobaltstrike” “totslegit,” as well as some of them having the default Cobalt Strike management port of 50050 open with the same JARM fingerprint. We believe that this scan found most, if not all, Cobalt Strike C2’s listening on the Internet on port 443 at the time of scan.

One could utilize JARM for detection and response by automatically scanning all destination hosts observed on their network for event enrichment, utilizing a summary table so as to not scan the same hosts multiple times in a given timeframe. They could then run queries of known-bad against the JARM list or utilize the list for correlation in response scenarios.

One time during her visits, my friend mentioned this new yoga master who was coming into town. And since I wasn’t very educated or tolerant back then, I felt that it was my duty to warn her on the spiritual “dangers” of doing yoga and following those philosophies. I was brash and truly believed I was doing her a favor when tears started streaming down my friend’s face.

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but it’s made no difference. We begin intensive in-home therapy as well as medication this week. We desperately hope they work, but I’m worried they won’t. He doesn’t like us very much unless we are giving him exactly what he wants when he wants it.

JARM Internet scanning, coupled with other metadata and historical analysis, allows for the possibility of proactive IOC identification for new campaigns using existing malware. For example, a cybersecurity researcher or company could scan the Internet with JARM, correlate known JARM results with the domain and IP history and reputation along with certificate details to build a high fidelity blocklist. This allows the cybersecurity industry to move towards the possibility of programmatically building out high fidelity blocklists before the first piece of malware is even distributed, placing threat actors on the defensive for the first time in a long time.

There is also a lot of potential for security researchers and vendors to utilize JARM with correlation and historical analysis to identify and track malicious servers. This data could then be used to build high fidelity proactive blocklists for easy consumption. But please note that extra care should be put into these blocklists to ensure minimal false positives.

To simplify the process, one could utilize a security vendor, like SecurityTrails or Shodan, and query their API for destination JARM enrichment. Security researchers and vendors are likely to be better suited to maintain historical analysis of TLS servers and can therefore provide greater levels of metadata to utilize in measuring a host’s risk score.

A fleet of application servers that are all running the same TLS configuration should have the same JARM fingerprint. One could regularly scan the fleet with JARM to confirm that they are the same. If a server in the fleet produces a different JARM fingerprint than the rest, then it is not running the same configuration. One major financial institution is already planning to use this capability to identify servers that are not running their latest TLS standard.

This shows that of the 100 Tor nodes the user maintains, 100 of them have the same JARM fingerprint. We essentially just ran a configuration drift check on this user’s Tor node deployment and found that they indeed have a well-maintained fleet. However, if one host had a different JARM than the others, then it would mean it’s not running the same configuration and may warrant investigation. To simulate this, I’ll throw a random IP into the list and run it again:

In order to use this as a potential blocklist, we need to filter out the false positives. One easy way to differentiate likely legitimate results with malicious ones is just by looking at the server’s history. Malicious C2s are generally ephemeral; they’re coming and going quite frequently, while legitimate servers tend to stay the same for long periods of time. This is where vendors with Internet historical data really come in handy. If the server matching the Cobalt Strike JARM has had its attributes unchanged for over a year, it’s more likely a legitimate false positive, while a server matching the Cobalt Strike JARM that didn’t exist 2 months ago is much more likely to be a malicious true positive. Combine that with other server attributes like name, hosting provider, certificate authority, etc. and we have ourselves a high quality Proactive Blocklist.

JARM fingerprints appear to also be unique to default configurations and patch levels for certain servers and appliances. Because of this, it may be possible to associate a JARM fingerprint with a specific version of Apache, for example. There has yet to be exhaustive research put into this, but here are some preliminary findings:

We did, however, find false positives in the list. It’s inevitable that in the sea of billions of IPs that some legitimate servers somewhere just happen to be configured in exactly the same way as Cobalt Strike. In the list we identified that the JARM also matches Burp Collaborator, another security tool used by red teams and threat actors alike, as well as miscellaneous legitimate servers, and a point of sale system. (Point of sale systems listening on the Internet is a subject for another blog post…) So while we believe JARM identified most, if not all, Cobalt Strike C2s listening on the Internet, we also had some legitimate servers caught in the net. This is like using a large magnet to pull all needles out of a haystack but getting some pieces of hay with them.

JARM fingerprints appear to also be unique to default configurations and patch levels for certain servers and appliances. Because of this, it may be possible to associate a JARM fingerprint with a specific version of Apache, for example. There has yet to be exhaustive research put into this, but here are some preliminary findings:

Tensorflow is a buzz word nowadays in this exciting world of Artificial Intelligence (AI), especially as Deep Learning continues to rapidly accelerate progress in AI. But for someone just starting with Tensorflow, the experience can be scary and daunting, as the terminologies and usage of the beautiful library can be confusing for complete beginners. When I first started learning Tensorflow, I faced similar challenges, and hope to simplify some of the intricacies through this article. This article requires a basic understanding of Python to get a clearer picture of Tensorflow.

Historically, the cybersecurity industry has been focused on reactive blocklists of atomic indicators of compromise (IOCs). That is, the industry waits until a malware campaign is observed in the wild, analyzes it, then takes the observed IOCs and publishes them for blocklists. The problem is that, by the time the IOCs are published, the malware has already been distributed and security engineers are automatically on the defensive, playing damage control.