Unsupervised Machine Learning and JA3 for Enhanced Security
Unlock the true power of Darktrace's algorithms. Learn how JA3 enhances cybersecurity defenses with unique TLS/SSL fingerprints & unsupervised machine learning.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO
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21
Jun 2018
Introducing JA3
JA3 is a methodology for fingerprinting Transport Layer Security applications. It was first posted on GitHub in June 2017 and is the work of Salesforce researchers John Althouse, Jeff Atkinson, and Josh Atkins. The JA3 TLS/SSL fingerprints created can overlap between applications but are still a great Indicator of Compromise (IoC). Fingerprinting is achieved by creating a hash of 5 decimal fields of the Client Hello message that is sent in the initial stages of an TLS/SSL session.
JA3 is an interesting approach to the increasing usage of encryption in networks. There is also a clear uptick in cyber-attacks using encrypted command and control (C2) channels – such as HTTPS – for malware communication.
The benefits of JA3 for enhancing rules-and-signatures security
These near-unique fingerprints can be used to enhance traditional cyber security approaches such as whitelisting, deny-listing, and searching for IoCs.
Let’s take the following JA3 hash for example: 3e860202fc555b939e83e7a7ab518c38. According to one of the public lists that maps JA3s to applications, this JA3 hash is associated with the ‘hola_svc’ application. This is the infamous Hola VPN solution that is non-compliant in most enterprise networks. On the other hand, the following hash is associated with the popular messenger software Slack: a5aa6e939e4770e3b8ac38ce414fd0d5. Traditional cyber security tools can use these hashes like traditional signatures to search for instances of them in data sets or trying to deny-list malicious ones.
While there is some merit to this approach, it comes with all the known limitations of rules-and-signatures defenses, such as the overlaps in signatures, the inability to detect unknown threats, as well as the added complexity of having to maintain a database of known signatures.
JA3 in Darktrace
Darktrace creates JA3 hashes for every TLS/SSL connection it encounters. This is incredibly powerful in a number of ways. First, the JA3 can add invaluable context to a threat hunt. Second, Darktrace can also be queried to see if a particular JA3 was encountered in the network, thus providing actionable intelligence during incident response if JA3 IoCs are known to the incident responders.
Things become much more interesting once we apply our unsupervised machine learning to JA3: Darktrace’s AI algorithms autonomously detect which JA3s are anomalous for the network as a whole and which JA3s are unusual for specific devices.
It basically tells a cyber security expert: This JA3 (3e860202fc555b939e83e7a7ab518c38) has never been seen in the network before and it is only used by one device. It indicates that an application, which is used by nobody else on the network, is initiating TLS/SSL connections. In our experience, this is most often the case for malware or non-compliant software. At this stage, we are observing anomalous behavior.
Darktrace’s AI combines these IoCs (Unusual Network JA3, Unusual Device JA3, …) with many other weak indicators to detect the earliest signs of an emerging threat, including previously unknown threats, without using rules or hard-coded thresholds.
Catching Red-Teams and domain fronting with JA3
The following is an example where Darktrace detected a Red-Team’s C2 communication by observing anomalous JA3 behavior.
The unsupervised machine learning algorithms identified a desktop device using a JA3 that was 100% unusual for the network connecting to an external domain using a Let’s Encrypt certificate, which, along with self-signed certificates, is often abused by malicious actors. As well as the JA3, the domain was also 100% rare for the network – nobody else visited it:
It turned out that a Red-Team had registered a domain that was very similar to the victim’s legitimate domain: www.companyname[.]com (legitimate domain) vs. www.companyname[.]online (malicious domain). This was intentionally done to avoid suspicion and human analysis. Over a 7-day period in a 2,000-device environment, this was the only time that Darktrace flagged unusual behavior of this kind.
As the C2 traffic was encrypted (therefore no intrusion detection was possible on the payload) and the domain was non-suspicious (no reputation-based deny-listing worked), this C2 had remained undetected by the rest of the security stack.
Combining unsupervised machine learning with JA3 is incredibly powerful for the detection of domain fronting. Domain fronting is a popular technique to circumvent censorship and to hide C2 traffic. While some infrastructure providers take action to prevent domain fronting on their end, it is still prevalent and actively used by attackers.
The only agreed-upon method within wide parts of the cyber-security community to detect domain fronting appears to be TLS/SSL inspection. This usually involved breaking up encrypted communication to inspect the clear-text payloads. While this works, it commonly involves additional infrastructure, network restructuring and comes with privacy issues – especially in the context of GDPR.
Unsupervised machine learning makes the detection of domain fronting without having to break up encrypted traffic possible by combining unusual JA3 detection with other anomalies such as beaconing. A good start for a domain fronting threat hunt? A device beaconing to an anomalous CDN with an unusual JA3 hash.
Conclusion
JA3 is not a silver bullet to pre-empt malware compromise. As a signature-based solution, it shares the same limitations of all other defenses that rely on pre-identified threats or deny-lists: having to play a constant game of catch-up with innovative attackers. However, as a novel means of identifying TLS/SSL applications, JA3 hashing can be leveraged as a powerful network behavioral indicator, an additional metric that can flag the use of unauthorized or risky software, or as a means of identifying emerging malware compromises in the initial stages of C2 communication. This is made possible through the power of unsupervised machine learning.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Customer Case Study: Leading Petrochemical Manufacturer
Headquartered in Saudi Arabia, this industry leading petrochemical manufacturer serves customers in more than 80 countries across diverse markets throughout Europe, Africa, Latin America, the Middle East, China, and Southeast Asia.
Cyber resiliency critical to growth strategy
This leading petrochemical manufacturer’s vision is to be one of the major global players in the production and marketing of designated petrochemicals and downstream products. The company aims to significantly increase its capacity to up to a million metric tons within the next few years.
With cyber-attacks on critical infrastructure increasing 30% globally last year, cyber resiliency is essential to supporting the company’s strategic business goals of:
Maximizing production through efficient asset utilization
Maximizing sales by conducting 90% of its business outside Saudi Arabia
Optimizing resources and processes by integrating with UN Global Compact principles for sustainability and efficiency
Growing its business portfolio by engaging in joint ventures to diversify production and add value to the economy
However, the industry leader faced several challenges in its drive to fortify its cybersecurity defenses.
Visibility gaps delay response time
The company’s existing security setup provided limited visibility to the in-house security team, hindering its ability to detect anomalous network and user activity in real time. This resulted in delayed responses to potential incidents, making proactive issue resolution difficult and any remediation in the event of a successful attack costly and time-consuming.
Manual detection drains resources
Without automated detection and response capabilities, the organization’s security team had to manually monitor for suspicious activity – a time-consuming and inefficient approach that strained resources and left the organization vulnerable. This made it difficult for the team to stay current with training or acquire new skills and certifications, which are core to the ethos of both the company’s owners and the team itself.
Cyber-attacks on critical infrastructure increasing
The petrochemical manufacturer is part of a broader ecosystem of companies, making the protection of its supply chain – both upstream and downstream – critical. With several manufacturing entities and multiple locations, the customer’s internal structure is complex and challenging to secure. As cyber-attacks on critical infrastructure escalate, it needed a more comprehensive approach to safeguard its business and the wider ecosystem.
Keeping and growing skills and focus in-house
To strengthen its cybersecurity strategy, the company considered two options:
Make a significant initial and ongoing investment in a Security Operations Center (SOC), which would involve skills development outside the company and substantial management overhead.
Use a combination of new, automated tools and an outsourced Managed Detection and Response (MDR) service to reduce the burden on internal security specialists and allow the company to invest in upskilling its staff so they can focus on more strategic tasks.
Faced with this choice between entirely outsourcing security and augmenting the security team with new capabilities, the customer chose the second option, selecting Darktrace to automate the company’s monitoring, detection, and response. Today, the petrochemical manufacturer is using:
To alleviate the burden on its lean security team, the company augmented its in-house capabilities with Darktrace’s Managed Detection & Response service. This support acts as an extension of its SOC, providing 24/7 monitoring, investigation, and escalation of high-priority threats. With Darktrace’s global SOC managing alert triage and autonomously containing threats, the organization’s internal team can focus on strategic initiatives. The result is a stronger security posture and increased capacity to proactively address evolving cyber risks – without expanding headcount or sacrificing visibility.
A unique approach to AI
In its search for a new security platform, the company’s Director of Information Technology said Darktrace’s autonomous response capability, coupled with Self-Learning AI-driven threat reduction, were two big reasons for selecting Darktrace over competing products and services.
AI was a huge factor – no one else was doing what Darktrace was doing with [AI].”
Demonstrated visibility
Before Darktrace, the customer had no visibility into the network activity to and from remote worker devices. Some employees need the ability to connect to its networks at any time and from any location, including the Director of Information Technology. The trial deployment of Darktrace / ENDPOINT was a success and gave the team peace of mind that, no matter the location or device, high-value remote workers were protected by Darktrace.
Modular architecture
Darktrace's modular architecture allowed the company to deploy security controls across its complex, multi-entity environment. The company’s different locations run on segregated networks but are still interconnected and need to be protected. Darktrace / NETWORK provides a unified view and coordinated security response across the organization’s entire network infrastructure, including endpoint devices.
Results
The petrochemical manufacturer is using Darktrace across all of its locations and has achieved total visibility across network and user activity. “Darktrace is increasing in value every day,” said the Director of Information Technology.
I don’t have a big team, and Darktrace makes our lives very, very easy, not least the automation of some of the tasks that require constant manual review.”
Time savings frees analysts to focus on proactive security
Darktrace / NETWORK provides continuous, AI-driven monitoring and analysis of the company’s network activity, user behavior, and threat patterns, establishing a baseline of what normal activity looks like, and then alerting analysts to any deviations from normal traffic, activity, and behaviors. Darktrace’s autonomous response capabilities speed up response to detected threats, meaning intervention from the security team is required for fewer incidents and alerts.
In October 2024 alone, Darktrace Cyber AI Analyst saved the team 810 investigation hours, and autonomously responded to 180 anomalous behaviors that were uncovered during the investigations. With Darktrace managing the majority of threat detection and response efforts, the security team has been able to change its day-to-day activity from manual review of traffic and alerts and belated response to activity, to proactively fortifying its detection and response posture and upskilling to meet evolving requirements.
Layered email protection reduces phishing threats
The company’s email infrastructure posed a challenge due to petrochemical industry regulations requiring on-premises email servers, with some security delivered via Microsoft Azure. By integrating Darktrace / EMAIL into the Azure stack, the organization has reduced the volume of phishing emails its users receive by 5%.
“Now we have one more layer of security related to email – every email goes through two filters. If something is not being caught or traced by Azure, it is being detected by Darktrace,” said the Director of Information Technology. “As a result, we’re now seeing only about 15% to 20% of the phishing emails we used to receive before implementing Darktrace.”
Preparing for a secure future
The time saved using Darktrace has helped the security team take proactive steps, including preparing for new cyber resilience regulations for Saudi Arabia’s Critical National Infrastructure, as mandated by the National Cybersecurity Authority (NCA).
“The team now has ample time to prepare policies and procedures that meet the new NCA regulations and, in some cases, enhance the requirements of the new law,” said the Director of Information Technology. “All of this is possible because they don’t need to keep watch; Darktrace takes on so much of that task for them.”
Tracking CVE-2025-31324: Darktrace’s detection of SAP Netweaver exploitation before and after disclosure
Introduction: Exploiting SAP platforms
Global enterprises depend extensively on SAP platforms, such as SAP NetWeaver and Visual Composer, to run critical business processes worldwide. These systems; however, are increasingly appealing targets for well-resourced adversaries:
In March 2025, CISA issued an alert confirming active exploitation of a 2017 SAP NetWeaver vulnerability (CVE‑2017‑12637), enabling attackers to perform directory traversal and exfiltrate sensitive files, including credentials, from internet-facing systems
CVE-2025-31324 affects SAP’s NetWeaver Visual Composer, a web-based software modeling tool. SAP NetWeaver is an application server and development platform that runs and connects SAP and non-SAP applications across different technologies [2]. It is commonly used by process specialists to develop application components without coding in government agencies, large enterprises, and by critical infrastructure operators [4].
CVE-2025-31324 affects SAP’s Netweaver Visual Composer Framework 7.1x (all SPS) and above [4]. The vulnerability in a Java Servlet (/irj/servlet_jsp) would enable an unauthorized actor to upload arbitrary files to the /developmentserver/metadatauploader endpoint, potentially resulting in remote code execution (RCE) and full system compromise [3]. The issue stems from an improper authentication and authorization check in the SAP NetWeaver Application Server Java systems [4].
What is the severity rating of CVE-2025-31324?
The vulnerability, first disclosed on April 24, 2025, carries the highest severity rating (CVSS v3 score: 10.0) and could allow remote attackers to upload malicious files without requiring authentication [1][5]. Although SAP released a workaround on April 8, many organizations are hesitant to take their business-critical SAP NetWeaver systems offline, leaving them exposed to potential exploitation [2].
How is CVE-2025-31324 exploited?
The vulnerability is exploitable by sending specifically crafted GET, POST, or HEAD HTTP requests to the /developmentserver/metadatauploader URL using either HTTP or HTTPS. Attackers have been seen uploading malicious files (.jsp, .java, or .class files to paths containing “\irj\servlet_jsp\irj\”), most of them being web shells, to publicly accessible SAP NetWeaver systems.
External researchers observed reconnaissance activity targeting this vulnerability in late January 2025, followed by a surge in exploitation attempts in February. The first confirmed compromise was reported in March [4].
Multiple threat actors have reportedly targeted the vulnerability, including Chinese Advanced Persistent Threats (APTs) groups Chaya_004 [7], UNC5221, UNC5174, and CL-STA-0048 [8], as well as ransomware groups like RansomEXX, also known as Storm-2460, BianLian [4] or Qilin [6] (the latter two share the same indicators of compromise (IoCs)).
Following the initial workaround published on April 8, SAP released a security update addressing CVE-2025-31324 and subsequently issued a patch on May 13 (Security Note 3604119) to resolve the root cause of the vulnerability [4].
Darktrace’s coverage of CVE-2025-31324 exploitation
Darktrace has observed activity indicative of threat actors exploiting CVE-2025-31324, including one instance detected before the vulnerability was publicly disclosed.
In April 2025, the Darktrace Threat Research team investigated activity related to the CVE-2025-31324 on SAP devices and identified two cases suggesting active exploitation of the vulnerability. One case was detected prior to the public disclosure of the vulnerability, and the other just two days after it was published.
Early detection of CVE 2025-31324 by Darktrace
Figure 1: Timeline of events for an internet-facing system, believed to be a SAP device, exhibiting activity indicative of CVE-2025-31324 exploitation.
On April 18, six days prior to the public disclosure of CVE-2025-31324, Darktrace began to detect unusual activity on a device belonging to a logistics organization in the Europe, the Middle East and Africa (EMEA) region. Multiple IoCs observed during this incident have since been linked via OSINT to the exploitation of CVE-2025-31324. Notably, however, this reporting was not available at the time of detection, highlighting Darktrace’s ability to detect threats agnostically, without relying on threat intelligence.
The device was observed making domain name resolution request for the Out-of-Band Application Security Testing (OAST) domain cvvr9gl9namk9u955tsgaxy3upyezhnm6.oast[.]online. OAST is often used by security teams to test if exploitable vulnerabilities exist in a web application but can similarly be used by threat actors for the same purpose [9].
Four days later, on April 22, Darktrace observed the same device, an internet-facing system believed to be a SAP device, downloading multiple executable (.exe) files from several Amazon Simple Storage Service (S3). Darktrace’s Threat Research team later found these files to be associated with the KrustyLoader malware [23][24][25].
KrustyLoader is known to be associated with the Chinese threat actor UNC5221, also known as UTA0178, which has been reported to aggressively target devices exposed to the internet [10] [14] [15]. It is an initial-stage malware which downloads and launches a second-stage payload – Sliver C2. Sliver is a similar tool to Cobalt Strike (an open-source post-exploitation toolkit). It is used for command-and-control (C2) connections [11][12]13]. After its successful download, KrustyLoader deletes itself to evade detection. It has been reported that multiple Chinese APT groups have deployed KrustyLoader on SAP Netweaver systems post-compromise [8].
The actors behind KrustyLoader have also been associated with the exploitation of zero-day vulnerabilities in other enterprise systems, including Ivanti devices [12]. Notably, in this case, one of the Amazon S3 domains observed (abode-dashboard-media.s3.ap-south-1.amazonaws[.]com ) had previously been investigated by Darktrace’s Threat Research team as part of their investigation into Ivanti Connect Secure (CS) and Policy Secure (PS) appliances.
In addition to the download of known malicious files, Darktrace also detected new IoCs, including several executable files that could not be attributed to any known malware families or previous attacks, and for which no corresponding OSINT reporting was available.
Post-CVE publication detection
Exploit Validation
Between April 27 and 29, Darktrace observed unusual activity from an SAP device on the network of a manufacturing customer in EMEA.
Figure 2: Darktrace / NETWORK’s detection of an SAP device performing a large volume of suspicious activity between April 27 and April 29.
The device was observed making DNS requests for OAST domains (e.g. aaaaaaaa.d06qqn7pu5a6u25tv9q08p5xhbjzw33ge.oast[.]online and aaaaaaaaaaa.d07j2htekalm3139uk2gowmxuhapkijtp.oast[.]pro), suggesting that a threat actor was testing for exploit validation [9].
Figure 3: Darktrace / NETWORK’s detection of a SAP device making suspicious domain name resolution requests for multiple OAST domains.
Privilege escalation tool download attempt
One day later, Darktrace observed the same device attempting to download an executable file from hxxp://23.95.123[.]5:666/xmrigCCall/s.exe (SHA-1 file hash: e007edd4688c5f94a714fee036590a11684d6a3a).
Darktrace / NETWORK identified the user agents Microsoft-CryptoAPI/10.0 and CertUtil URL Agent during the connections to 23.95.123[.]5. The connections were made over port 666, which is not typically used for HTTP connections.
Multiple open-source intelligence (OSINT) vendors have identified the executable file as either JuicyPotato or SweetPotato, both Windows privilege escalation tools[16][17][18][19]. The file hash and the unusual external endpoint have been associated with the Chinese APT group Gelsemium in the past, however, many threat actors are known to leverage this tool in their attacks [20] [21].
Figure 4: Darktrace’s Cyber AI Analyst’s detection of a SAP device downloading a suspicious executable file from hxxp://23.95.123[.]5:666/xmrigCCall/s.exe on April 28, 2025.
Darktrace deemed this activity highly suspicious and triggered an Enhanced Monitoring model alert, a high-priority security model designed to detect activity likely indicative of compromise. As the customer was subscribed to the Managed Threat Detection service, Darktrace’s Security Operations Centre (SOC) promptly investigated the alert and notified the customer for swift remediation. Additionally, Darktrace’s Autonomous Response capability automatically blocked connections to the suspicious IP, 23.95.123[.]5, effectively containing the compromise in its early stages.
Figure 5: Actions taken by Darktrace’s Autonomous Response to block connections to the suspicious external endpoint 23.95.123[.]5. This event log shows that the connections to 23.95.123[.]5 were made over a rare destination port for the HTTP protocol and that new user agents were used during the connections.
Conclusion
The exploitation of CVE-2025-31324 to compromise SAP NetWeaver systems highlights the persistent threat posed by vulnerabilities in public-facing assets. In this case, threat actors leveraged the flaw to gain an initial foothold, followed by attempts to deploy malware linked to groups affiliated with China [8][20].
Crucially, Darktrace demonstrated its ability to detect and respond to emerging threats even before they are publicly disclosed. Six days prior to the public disclosure of CVE-2025-31324, Darktrace detected unusual activity on a device believed to be a SAP system, which ultimately represented an early detection of the CVE. This detection was made possible through Darktrace’s behavioral analysis and anomaly detection, allowing it to recognize unexpected deviations in device behavior without relying on signatures, rules or known IoCs. Combined with its Autonomous Response capability, this allowed for immediate containment of suspicious activity, giving security teams valuable time to investigate and mitigate the threat.
Credit to Signe Zaharka (Principal Cyber Analyst), Emily Megan Lim, (Senior Cyber Analyst) and Ryan Traill (Analyst Content Lead)
Appendices
List of IoCs
23.95.123[.]5:666/xmrigCCall/s.exe - URL- JuicyPotato/SweetPotato - high confidence
29274ca90e6dcf5ae4762739fcbadf01- MD5 file hash - JuicyPotato/SweetPotato - high confidence
![Actions taken by Darktrace’s Autonomous Response to block connections to the suspicious external endpoint 23.95.123[.]5. This event log shows that the connections to 23.95.123[.]5 were made over a rare destination port for the HTTP protocol and that new user agents were used during the connections.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/685195a1592b0029efa4fd53_Screenshot%202025-06-17%20at%209.19.36%E2%80%AFAM.png)