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Erkennen und Reagieren auf Log4Shell in freier Wildbahn







In diesem Blog werfen wir einen Blick auf die Log4Shell-Schwachstelle und stellen Beispiele aus der Praxis vor, wie Darktrace Angriffe, die versuchen, Log4Shell in freier Wildbahn auszunutzen, erkennt und darauf reagiert.
Log4Shell ist jetzt der bekannte Name für CVE-2021-44228 - eine Zero-Day-Schwachstelle des Schweregrads 10, die ein bekanntes Java-Protokollierungsprogramm namens Log4j ausnutzt. Schwachstellen werden täglich entdeckt, und einige sind schwerwiegender als andere, aber die Tatsache, dass dieses Open-Source-Dienstprogramm in fast alles eingebettet ist, einschließlich der Mars Ingenuity-Drohne, macht diese Schwachstelle umso bedrohlicher. Details und weitere Updates zu Log4Shell sind zum Zeitpunkt der Veröffentlichung dieses Blogs noch in Arbeit.
Normalerweise werden Zero-Days, die in der Lage sind, so viele Systeme zu erreichen, geheim gehalten und nur von Nationalstaaten für hochrangige Ziele oder Operationen eingesetzt. Dieser Zero-Day wurde jedoch erst entdeckt, als er auf Minecraft-Spieleserver eingesetzt wurde und im Chat zwischen Spielern ausgetauscht wurde.
Es sollten zwar alle Schritte unternommen werden, um Abhilfemaßnahmen für die Log4Shell-Schwachstelle zu implementieren, aber dies kann einige Zeit dauern. Wie hier gezeigt, kann die Verhaltenserkennung verwendet werden, um nach Anzeichen für Aktivitäten nach der Ausnutzung der Schwachstelle zu suchen, z. B. Scannen, coin mining, lateral Movement und andere Aktivitäten.
Darktrace entdeckte zunächst die Log4Shell-Schwachstelle, die auf die Internet-Server eines unserer Kunden abzielt, wie Sie unten in einer aktuellen anonymisierten Bedrohungsuntersuchung im Detail sehen können. Diese wurde mithilfe des Cyber-AI-Analyst aufgedeckt, gemeldet und anschließend von unserem SOC-Team analysiert. Wichtig zu erwähnen ist, dass hierbei bereits vorhandene Algorithmen verwendet wurden, ohne dass die Klassifizierer neu trainiert oder die Reaktionsmechanismen als Reaktion auf die Log4Shell-Cyberangriffe angepasst wurden.
Wie Log4Shell funktioniert
Die Schwachstelle funktioniert, indem sie eine unsachgemäße Eingabevalidierung durch das Java Naming and Directory Interface (JNDI) ausnutzt. Ein Befehl kommt von einem HTTP-User-Agent, einer verschlüsselten HTTPS-Verbindung oder sogar einer Chatroom-Nachricht. Die JNDI sendet ihn an das Zielsystem, wo er ausgeführt wird. Die meisten Libraries und Applikationen verfügen über Prüfungen und Schutzmechanismen, um dies zu verhindern, aber wie hier zu sehen ist, werden diese manchmal übersehen.
Verschiedene Bedrohungsakteure haben damit begonnen, die Schwachstelle für Angriffe zu nutzen, die von wahllosen Krypto-Mining-Kampagnen bis hin zu gezielten, komplexen Angriffen reichen.
Reales Beispiel 1: Log4Shell wird am CVE-ID-Veröffentlichungsdatum ausgenutzt
Darktrace sah dieses erste Beispiel am 10. Dezember, dem gleichen Tag, an dem die CVE-ID veröffentlicht wurde. Wir sehen oft, dass öffentlich dokumentierte Schwachstellen innerhalb weniger Tage von Bedrohungsakteuren als Waffe eingesetzt werden. Dieser Angriff betraf ein dem Internet zugewandten Gerät in der demilitarisierten Zone (DMZ) eines Unternehmens. Darktrace hatte den Server aufgrund seines Verhaltens automatisch als dem Internet zugewandtes Gerät eingestuft.
Das Unternehmen hatte Darktrace im On-Prem-Netz als einen von vielen Abdeckungsbereichen implementiert, zu denen auch Cloud, E-Mail und SaaS gehören. Bei diesem Einsatz hatte Darktrace einen guten Überblick über den DMZ-Verkehr. Antigena war in dieser Umgebung nicht aktiv, Darktrace befand sich nur im Erkennungsmodus. Trotz dieser Tatsache war der betreffende Kunde in der Lage, diesen Vorfall innerhalb weniger Stunden nach dem ersten Alarm zu erkennen und zu beheben. Der Angriff war automatisiert und hatte zum Ziel, einen als Kinsing bekannten Krypto-Miner einzusetzen.
Bei diesem Angriff erschwerte der Angreifer die Entdeckung der Kompromittierung, indem er die anfängliche Befehlsinjektion verschlüsselte und HTTPS anstelle des in der Praxis üblichen HTTP verwendete. Obwohl mit dieser Methode herkömmliche Regeln und signaturbasierte Systeme umgangen werden konnten, war Darktrace in der Lage, mehrere ungewöhnliche Verhaltensweisen Sekunden nach der ersten Verbindung zu erkennen.
Erste Details der Kompromitierung
Durch Peer-Analysen hatte Darktrace zuvor erfahren, was dieses spezielle DMZ-Gerät und seine Peer-Gruppe normalerweise in der Umgebung tun. Während des ersten Angriffs entdeckte Darktrace verschiedene subtile Anomalien, die zusammengenommen den Angriff offensichtlich machten.
- 15:45:32 Eingehende HTTPS-Verbindung zum DMZ-Server von seltener russischer IP - 45.155.205[.]233;
- 15:45:38 Der DMZ-Server stellt eine neue ausgehende Verbindung zu derselben seltenen russischen IP-Adresse her und verwendet dabei zwei neue Benutzeragenten: Java-Benutzer-Agent und Curl über einen Port, der im Vergleich zum bisherigen Verhalten ungewöhnlich ist, um HTTP zu bedienen;
- 15:45:39 Der DMZ-Server verwendet eine HTTP-Verbindung mit einem anderen neuen curl-Benutzeragenten ('curl/7.47.0') zur gleichen russischen IP. Der URI enthält Aufklärungsinformationen vom DMZ-Server.
All diese Aktivitäten wurden nicht entdeckt, weil Darktrace sie schon einmal gesehen hatte, sondern weil sie stark vom normalen "Verhaltensmuster" für diesen und ähnliche Server in dieser speziellen Organisation abwichen.
Dieser Server hat sich mit seltenen IP-Adressen im Internet verbunden, er hat über Protokoll- und Port-Kombinationen, die er nie benutzt, nie zuvor benutzte User-Agents verwendet. Jede einzelne Anomalie mag für sich genommen ein leicht ungewöhnliches Verhalten gezeigt haben - aber zusammengenommen und im Kontext dieses speziellen Geräts und dieser Umgebung analysiert, erzählen die Entdeckungen eindeutig eine größere Geschichte eines laufenden Cyberangriffs.
Darktrace hat diese Aktivität mit verschiedenen Modellen entdeckt:
- Anomalous Connection / New User Agent to IP Without Hostname
- Anomalous Connection / Callback on Web Facing Device
Weitere Werkzeuge und Crypto-Miner Download
Weniger als 90 Minuten nach der ersten Kompromittierung begann der infizierte Server, bösartige Skripte und ausführbare Dateien von einer seltenen ukrainischen IP 80.71.158[.]12 herunterzuladen.
Die folgenden Nutzerdaten wurden anschließend der Reihe nach von der ukrainischen IP heruntergeladen:
- hXXp://80.71.158[.]12//lh.sh
- hXXp://80.71.158[.]12/Expl[REDACTED].class
- hXXp://80.71.158[.]12/kinsing
- hXXp://80.71.158[.]12//libsystem.so
- hXXp://80.71.158[.]12/Expl[REDACTED].class
Ohne Bedrohungsdaten oder Erkennungen, die auf statischen Kompromittierungsindikatoren (IoC) wie IPs, Domänennamen oder Datei-Hashes basieren, erkannte Darktrace diesen nächsten Schritt des Angriffs in Echtzeit.
Der betreffende DMZ-Server hat in der Vergangenheit nie mit dieser ukrainischen IP-Adresse über diese ungewöhnlichen Ports kommuniziert. Es ist auch höchst ungewöhnlich, dass dieses Gerät und seine Gegenspieler Skripte oder ausführbare Dateien dieser Art von einer externen Destination auf diese Weise herunterladen. Kurz nach diesen Downloads begann der DMZ-Server, Krypto-Mining zu betreiben.
Darktrace hat diese Aktivität mit verschiedenen Modellen entdeckt:
- Anomalous File / Script from Rare External Location
- Anomalous File / Internet Facing System File Download
- Device / Internet Facing System with High Priority Alert
Sofortige Aufdeckung des Log4Shell-Vorfalls
Neben Darktrace, das jeden einzelnen Schritt dieses Angriffs in Echtzeit erkennt, hat der Cyber-AI-Analyst auch den übergreifenden Sicherheitsvorfall, der eine zusammenhängende Erzählung für den Gesamtangriff enthält, als den Vorfall mit der höchsten Priorität, innerhalb einer Woche von Vorfällen und Warnungen, auf Darktrace aufgedeckt. Das bedeutet, dass dieser Vorfall das offensichtlichste und unmittelbarste Element war, auf das die Sicherheitsteams aufmerksam wurden, als er sich entwickelte. Der Cyber-AI-Analyst fand jede Phase dieses Vorfalls und stellte genau die Fragen, die Sie von Ihren menschlichen SOC-Analysten erwarten würden. Der vom Cyber-AI-Analysten erstellte Bericht in natürlicher Sprache enthält eine Zusammenfassung der einzelnen Phasen des Vorfalls, gefolgt von den wichtigen Daten, die menschliche Analysten benötigen, in einem leicht verständlichen Format. Jede Registerkarte steht für einen anderen Teil des Vorfalls und beschreibt die tatsächlichen Schritte, die während des jeweiligen Untersuchungsprozesses unternommen wurden.
Das Ergebnis ist, dass man sich nicht mehr durch Low-Level-Warnungen wühlen muss, dass man keine punktuellen Erkennungen mehr triagieren muss, dass man die Erkennungen nicht mehr in einen größeren Vorfallskontext einordnen muss und dass man keinen Bericht mehr schreiben muss. All dies wurde automatisch vom KI-Analysten erledigt und spart den menschlichen Teams wertvolle Zeit.
Der nachstehende Vorfallsbericht wurde automatisch erstellt und konnte als PDF-Datei in verschiedenen Sprachen heruntergeladen werden.

Abbildung 1: Der Cyber AI Analyst von Darktrace zeigt mehrere Stufen des Angriffs und erklärt den Untersuchungsprozess
Beispiel aus der Praxis 2: Reaktion auf einen anderen Angriff durch Log4Shell
Am 12. Dezember wurde der Internet-Server eines anderen Unternehmens zunächst über Log4Shell kompromittiert. Die Details der Kompromittierung sind zwar anders - es sind andere IoCs beteiligt - aber Darktrace hat den Angriff ähnlich wie im ersten Beispiel erkannt und analysiert.
Interessanterweise hatte diese Organisation Darktrace Antigena im autonomen Modus auf ihrem Server, was bedeutet, dass die KI eigenständig Maßnahmen ergreifen kann, um auf laufende Cyberangriffe zu reagieren. Diese Reaktionen können über eine Vielzahl von Mechanismen erfolgen, z. B. über API-Interaktionen mit Firewalls, anderen Sicherheitstools oder native Reaktionen von Darktrace.
Bei diesem Angriff wurde die seltene externe IP 164.52.212[.]196 für die Command-and-Control (C2)-Kommunikation und die Verbreitung von Malware verwendet, wobei HTTP über Port 88 genutzt wurde, was für dieses Gerät, diese Peer Group und diese Organisation höchst ungewöhnlich war.
Antigena reagierte in dieser Organisation in Echtzeit, basierend auf dem spezifischen Kontext des Angriffs, ohne dass ein Mensch involviert war. Antigena interagierte in diesem Fall mit der Firewall des Unternehmens, um alle Verbindungen zu oder von der bösartigen IP-Adresse - in diesem Fall 164.52.212[.]196 - über Port 88 für zwei Stunden zu blockieren, mit der Option, die Blockierung und die Dauer zu verlängern, wenn der Angriff anscheinend andauert. Dies ist in der nachstehenden Abbildung zu sehen:

Abbildung 2: Die Reaktion der Antigena
Jetzt kommt der Clou: Dank selbstlernender KI weiß Darktrace genau, was der Server im Internet normalerweise tut und was nicht, bis hin zu jedem einzelnen Datenpunkt. Aufgrund der verschiedenen Anomalien ist sich Darktrace sicher, dass es sich um einen schweren Cyberangriff handelt.
Antigena greift nun ein und erzwingt den regulären Ablauf des Lebens dieses Servers in der DMZ. Das bedeutet, dass der Server weiterhin tun kann, was er normalerweise tut - aber alle höchst anomalen Aktionen werden unterbrochen, wenn sie in Echtzeit stattfinden, wie z. B. die Kommunikation mit einer seltenen externen IP über Port 88, die HTTP zum Herunterladen von ausführbaren Dateien dient.
Natürlich kann der Mensch die Sperre jederzeit ändern oder aufheben. Antigena kann auch so konfiguriert werden, dass es sich im Bestätigungsmodus befindet, d. h. der Mensch ist zu bestimmten Zeiten am Tag (z. B. während der Bürozeiten) oder zu jeder Zeit eingebunden, je nach den Bedürfnissen und Anforderungen einer Organisation.
Schlussfolgerung
Dieser Blog veranschaulicht weitere Aspekte von Cyberangriffen, die die Log4Shell-Schwachstelle ausnutzen. Außerdem wird gezeigt, wie Darktrace Zero-Day-Angriffe erkennt und auf sie reagiert, wenn Darktrace Einblick in die angegriffenen Entitäten hat.
Während Log4Shell die IT- und Sicherheitsnachrichten dominiert, sind ähnliche Schwachstellen in der Vergangenheit aufgetaucht und werden auch in Zukunft auftreten. Wir haben bereits über unseren Ansatz zur Erkennung und Reaktion auf ähnliche Schwachstellen und damit verbundene Cyberangriffe gesprochen:
- die jüngste Sicherheitslücke in Gitlab;
- die ProxyShell Exchange Server-Schwachstellen, als sie noch ein Zero-Day waren;
- und die Citrix Netscaler-Schwachstelle.
Wie immer sollten Unternehmen eine "Defense-in-Depth"-Strategie anstreben, die präventive Sicherheitskontrollen mit Erkennungs- und Reaktionsmechanismen sowie einem strengen Patch-Management kombiniert.
Vielen Dank an Brianna Leddy (Darktrace’s Director of Analysis) für ihre Erkenntnisse über die oben genannte Bedrohung.
Sie mögen das und wollen mehr?
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Darktrace/Email in Action: Why AI-Driven Email Security is the Best Defense Against Sustained Phishing Campaigns
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Stopping the bad while allowing the good
Since its inception, email has been regarded as one of the most important tools for businesses, revolutionizing communication and allowing global teams to become even more connected. But besides organizations heavily relying on email for their daily operations, threat actors have also recognized that the inbox is one of the easiest ways to establish an initial foothold on the network.
Today, not only are phishing campaigns and social engineering attacks becoming more prevalent, but the level of sophistication of these attacks are also increasing with the help of generative AI tools that allow for the creation of hyper-realistic emails with minimal errors, effectively lowering the barrier to entry for threat actors. These diverse and stealthy types of attacks evade traditional email security tools based on rules and signatures, because they are less likely to contain the low-sophistication markers of a typical phishing attack.
In a situation where the sky is the limit for attackers and security teams are lean, how can teams equip themselves to tackle these threats? How can they accurately detect increasingly realistic malicious emails and neutralize these threats before it is too late? And importantly, how can email security block these threats while allowing legitimate emails to flow freely?
Instead of relying on past attack data, Darktrace’s Self-Learning AI detects the slightest deviation from a user’s pattern of life and responds autonomously to contain potential threats, stopping novel attacks in their tracks before damage is caused. It doesn’t define ‘good’ and ‘bad’ like traditional email tools, rather it understands each user and what is normal for them – and what’s not.
This blog outlines how Darktrace/Email™ used its understanding of ‘normal’ to accurately detect and respond to a sustained phishing campaign targeting a real-life company.
Responding to a sustained phishing attack
Over the course of 24 hours, Darktrace detected multiple emails containing different subjects, all from different senders to different recipients in one organization. These emails were sent from different IP addresses, but all came from the same autonomous system number (ASN).

The emails themselves had many suspicious indicators. All senders had no prior association with the recipient, and the emails generated a high general inducement score. This score is generated by structural and non-specific content analysis of the email – a high score indicates that the email is trying to induce the recipient into taking a particular action, which may lead to account compromise.
Additionally, each email contained a visually prominent link to a file storage service, hidden behind a shortened bit.ly link. The similarities across all these emails pointed to a sustained campaign targeting the organization by a single threat actor.


With all these suspicious indicators, many models were breached. This drove up the anomaly score, causing Darktrace/Email to hold all suspicious emails from the recipients’ inboxes, safeguarding the recipients from potential account compromise and disallowing the threats from taking hold in the network.
Imagining a phishing attack without Darktrace/Email
So what could have happened if Darktrace had not withheld these emails, and the recipients had clicked on the links? File storage sites have a wide variety of uses that allow attackers to be creative in their attack strategy. If the user had clicked on the shortened link, the possible consequences are numerous. The link could have led to a login page for unsuspecting victims to input their credentials, or it could have hosted malware that would automatically download if the link was clicked. With the compromised credentials, threat actors could even bypass MFA, change email rules, or gain privileged access to a network. The downloaded malware might also be a keylogger, leading to cryptojacking, or could open a back door for threat actors to return to at a later time.


The limits of traditional email security tools
Secure email gateways (SEGs) and static AI security tools may have found it challenging to detect this phishing campaign as malicious. While Darktrace was able to correlate these emails to determine that a sustained phishing campaign was taking place, the pattern among these emails is far too generic for specific rules as set in traditional security tools. If we take the characteristic of the freemail account sender as an example, setting a rule to block all emails from freemail accounts may lead to more legitimate emails being withheld, since these addresses have a variety of uses.
With these factors in mind, these emails could have easily slipped through traditional security filters and led to a devastating impact on the organization.
Schlussfolgerung
As threat actors step up their attacks in sophistication, prioritizing email security is more crucial than ever to preserving a safe digital environment. In response to these challenges, Darktrace/Email offers a set-and-forget solution that continuously learns and adapts to changes in the organization.
Through an evolving understanding of every environment in which it is deployed, its threat response becomes increasingly precise in neutralizing only the bad, while allowing the good – delivering email security that doesn’t come at the expense of business growth.
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Einblicke in das SOC-Team
Black Basta: Old Dogs with New Tricks



What is Black Basta?
Over the past year, security researchers have been tracking a new ransomware group, known as Black Basta, that has been observed targeted organizations worldwide to deploy double extortion ransomware attacks since early 2022. While the strain and group are purportedly new, evidence seen suggests they are an offshoot of the Conti ransomware group [1].
The group behind Black Basta run a Ransomware as a Service (RaaS) model. They work with initial access brokers who will typically already have a foothold in company infrastructure to begin their attacks. Once inside a network, they then pivot internally using numerous tools to further their attack.
Black Basta Ransomware
Like many other ransomware actors, Black Basta uses double extortion as part of its modus operandi, exfiltrating sensitive company data and using the publication of this as a second threat to affected companies. This is also advertised on a dark web site, setup by the group to apply further pressure for affected companies to make ransom payments and avoid reputational damage.
The group also seems to regularly take advantage of existing tools to undertake the earlier stages of their attacks. Notably, the Qakbot banking trojan, seems to be the malware often used to gain an initial foothold within compromised environments.
Analysis of the tools, procedures and infrastructure used by Black Basta belies a maturity to the actors behind the ransomware. Their models and practices suggest those involved are experienced individuals, and security researchers have drawn possible links to the Conti ransomware group.
As such, Black Basta is a particular concern for security teams as attacks will likely be more sophisticated, with attackers more patient and able to lie low on digital estates for longer, waiting for the opportune moment to strike.
Cyber security is an infinite game where defender and attacker are stuck as cat and mouse; as new attacks evolve, security vendors and teams respond to the new indicators of compromise (IoCs), and update their existing rulesets and lists. As a result, attackers are forced to change their stripes to evade detection or sometimes even readjust their targets and end goals.
Anomaly Based Detection
By using the power of Darktrace’s Self-Learning AI, security teams are able to detect deviations in behavior. Threat actors need to move through the kill chain to achieve their aims, and in doing so will cause affected devices within networks to deviate from their expected pattern of life. Darktrace’s anomaly-based approach to threat detection allows it recognize these subtle deviations that indicate the presence of an attacker, and stop them in their tracks.
Additionally, the ecosystem of cyber criminals has matured in the last few decades. It is well documented how many groups now operate akin to legitimate companies, with structure, departments and governance. As such, while new attack methods and tactics do appear in the wild, the maturity in their business models belie the experience of those behind the attack.
As attackers grow their business models and develop their arsenal of attack vectors, it becomes even more critical for security teams to remain vigilant to anomalies within networks, and remain agnostic to underlying IoCs and instead adopt anomaly detection tools able to identify tactics, techniques, and procedures (TTPs) that indicate attackers may be moving through a network, ahead of deployment of ransomware and data encryption.
Darktrace’s Coverage of Black Basta
In April 2023, the Darktrace Security Operations Center (SOC) assisted a customer in triaging and responding to an ongoing ransomware infection on their network. On a Saturday, the customer reached out directly to the Darktrace analyst team via the Ask the Expert service for support after they observed encrypted files and locked administrative accounts on their network. The analyst team were able to investigate and clarify the attack path, identifying affected devices and assisting the customer with their remediation. Darktrace DETECT™ observed varying IoCs and TTPs throughout the course of this attack’s kill chain; subsequent analysis into these indicators revealed this had likely been a case of Black Basta seen in the wild.
Erstes Eindringen
The methods used by the group to gain an initial foothold in environments varies – sometimes using phishing, sometimes gaining access through a common vulnerability exposed to the internet. Black Basta actors appear to target specific organizations, as opposed to some groups who aim to hit multiple at once in a more opportunistic fashion.
In the case of the Darktrace customer likely affected by Black Basta, it is probable that the initial intrusion was out of scope. It may be that the path was via a phishing email containing an Microsoft Excel spreadsheet that launches malicious powershell commands; a noted technique for Black Basta. [3][4] Alternatively, the group may have worked with access brokers who already had a foothold within the customer’s network.
One particular device on the network was observed acting anomalously and was possibly the first to be infected. The device attempted to connect to multiple internal devices over SMB, and connected to a server that was later found to be compromised and is described throughout the course of this blog. During this connection, it wrote a file over SMB, “syncro.exe”, which is possibly a legitimate Remote Management software but could in theory be used to spread an infection laterally. Use of this tool otherwise appears sporadic for the network, and was notably unusual for the environment.
Given these timings, it is possible this activity is related to the likely Black Basta compromise. However, there is some evidence online that use of Syncro has been seen installed as part of the execution of loaders such as Batloader, potentially indicating a separate or concurrent attack [5].
Internal Reconnaissance + Lateral Movement
However the attackers gained access in this instance, the first suspicious activity observed by Darktrace originated from an infected server. The attacker used their foothold in the device to perform internal reconnaissance, enumerating large portions of the network. Darktrace DETECT’s anomaly detection noted a distinct rise in connections to a large number of subnets, particularly to closed ports associated with native Windows services, including:
- 135 (RPC)
- 139 (NetBIOS)
- 445 (SMB)
- 3389 (RDP)
During the enumeration, SMB connections were observed during which suspiciously named executable files were written:
- delete.me
- covet.me
Data Staging and Exfiltration
Around 4 hours after the scanning activity, the attackers used their knowledge gained during enumeration about the environment to begin gathering and staging data for their double extortion attempts. Darktrace observed the same infected server connecting to a file storage server, and downloading over 300 GiB of data. Darktrace DETECT identified that the connections had been made via SMB and was able to present a list of filenames to the customer, allowing their security team to determine the data that had likely been exposed to the attackers.
The SMB paths detected by Darktrace showed a range of departments’ file areas being accessed by threat actors. This suggests they were interested in getting as much varied data as possible, presumably in an attempt to ensure a large amount of valuable information was at their disposal to make any threats of releasing them more credible, and more damaging to the company.
Shortly after the download, the device made an external connection over SSH to a rare domain, dataspt[.]com, hosted in the United States. The connection itself was made over an unusual port, 2022, and Darktrace recognized that the domain was new for the network.
During this upload, the threat actors uploaded a similar volume of data to the 300GiB that had been downloaded internally earlier. Darktrace flagged the usual elements of this external upload, making the identification and triage of this exfiltration attempt easier for the customer.
On top of this, Darktrace’s autonomous investigation tool Cyber AI Analyst™ launched an investigation into this on-going activity and was able to link the external upload events to the internal download, identifying them as one exfiltration incident rather than two isolated events. AI Analyst then provided a detailed summary of the activity detected, further speeding up the identification of affected files.
Preparing for Exploitation
All the activity documented so far had occurred on a Wednesday evening. It was at this point that the burst of activity calmed, and the ransomware lay in wait within the environment. Other devices around the network, particularly those connected to by the original infected server and a domain controller, were observed performing some elements of anomalous activity, but the attack seemed to largely take a pause.
However, on the Saturday morning, 3 days later, the compromised server began to change the way it communicated with attackers by reaching out to a new command and control (C2) endpoint. It seemed that attackers were gearing up for their attack, taking advantage of the weekend to strike while security teams often run with a reduced staffing.
Darktrace identified connections to a new endpoint within 4 minutes of it first being seen on the customer’s environment. The server had begun making repeated SSL connections to the new external endpoint, faceappinc[.]com, which has been flagged as malicious by various open-source intelligence (OSINT) sources.
The observed JA3 hash (d0ec4b50a944b182fc10ff51f883ccf7) suggests that the command-line tool BITS Admin was being used to launch these connections, another suggestion of the use of mature tooling.
In addition to this, Darktrace also detected the server using an administrative credential it had never previously been associated with. Darktrace recognized that the use of this credential represented a deviation from the device’s usual activity and thus could be indicative of compromise.
The server then proceeded to use the new credential to authenticate over Keberos before writing a malicious file (“management.exe”) to the Temp directory on a number of internal devices.
Encryption
At this point, the number of anomalous activities detected from the server increased massively as the attacker seems to connect networkwide in an attempt to cause as quick and destructive an encryption effort as possible. Darktrace observed numerous files that had been encrypted by a local process. The compromised server began to write ransom notes, named “instructions_read_me.txt” to other file servers, which presumably also had successfully deployed payloads. While Black Basta actors had initially been observed dropping ransom notes named “readme.txt”, security researchers have since observed and reported an updated variant of the ransomware that drops “instructions_read_me_.txt”, the name of the file detected by Darktrace, instead [6].
Another server was also observed making repeated SSL connections to the same rare external endpoint, faceappinc[.]com. Shortly after beginning these connections, the device made an HTTP connection to a rare IP address with no hostname, 212.118.55[.]211. During this connection, the device also downloaded a suspicious executable file, cal[.]linux. OSINT research linked the hash of this file to a Black Basta Executable and Linkable File (ELF) variant, indicating that the group was highly likely behind this ransomware attack.
Of particular interest again, is how the attacker lives off the land, utilizing pre-installed Windows services. Darktrace flagged that the server was observed using PsExec, a remote management executable, on multiple devices.
Darktrace Assistance
Darktrace DETECT was able to clearly detect and provide visibility over all stages of the ransomware attack, alerting the customer with multiple model breaches and AI Analyst investigation(s) and highlighting suspicious activity throughout the course of the attack.
For example, the exfiltration of sensitive data was flagged for a number of anomalous features of the meta-data: volume; rarity of the endpoint; port and protocol used.
In total, the portion of the attack observed by Darktrace lasted about 4 days from the first model breach until the ransomware was deployed. In particular, the encryption itself was initiated on a Saturday.
The encryption event itself was initiated on a Saturday, which is not uncommon as threat actors tend to launch their destructive attacks when they expect security teams will be at their lowest capacity. The Darktrace SOC team regularly observes and assists in customer’s in the face of ransomware actors who patiently lie in wait. Attackers often choose to strike as security teams run on reduced hours of manpower, sometimes even choosing to deploy ahead of longer breaks for national or public holidays, for example.
In this case, the customer contacted Darktrace directly through the Ask the Expert (ATE) service. ATE offers customers around the clock access to Darktrace’s team of expert analysts. Customers who subscribe to ATE are able to send queries directly to the analyst team if they are in need of assistance in the face of suspicious network activity or emerging attacks.
In this example, Darktrace’s team of expert analysts worked in tandem with Cyber AI Analyst to investigate the ongoing compromise, ensuring that the investigation and response process were completed as quickly and efficiently as possible.
Thanks to Darktrace’s Self-Learning AI, the analyst team were able to quickly produce a detailed report enumerating the timeline of events. By combining the human expertise of the analyst team and the machine learning capabilities of AI Analyst, Darktrace was able to quickly identify anomalous activity being performed and the affected devices. AI Analyst was then able to collate and present this information into a comprehensive and digestible report for the customer to consult.
Schlussfolgerung
It is likely that this ransomware attack was undertaken by the Black Basta group, or at least using tools related to their method. Although Black Basta itself is a relatively novel ransomware strain, there is a maturity and sophistication to its tactics. This indicates that this new group are actually experienced threat actors, with evidence pointing towards it being an offshoot of Conti.
The Pyramid of Pain is a well trodden model in cyber security, but it can help us understand the various features of an attack. Indicators like static C2 destinations or file hashes can easily be changed, but it’s the underlying TTPs that remain the same between attacks.
In this case, the attackers used living off the land techniques, making use of tools such as BITSAdmin, as well as using tried and tested malware such as Qakbot. While the domains and IPs involved will change, the way these malware interact and move about systems remains the same. Their fingerprint therefore causes very similar anomalies in network traffic, and this is where the strength of Darktrace lies.
Darktrace’s anomaly-based approach to threat detection means that these new attack types are quickly drawn out of the noise of everyday traffic within an environment. Once attackers have gained a foothold in a network, they will have to cause deviation from the usual pattern of a life on a network to proceed; Darktrace is uniquely placed to detect even the most subtle changes in a device’s behavior that could be indicative of an emerging threat.
Machine learning can act as a force multiplier for security teams. Working hand in hand with the Darktrace SOC, the customer was able to generate cohesive and comprehensive reporting on the attack path within days. This would be a feat for humans alone, requiring significant resources and time, but with the power of Darktrace’s Self-Learning AI, these deep and complex analyses become as easy as the click of a button.
Credit to: Matthew John, Director of Operations, SOC, Paul Jennings, Principal Analyst Consultant
Appendices
Darktrace DETECT Model Breaches
Internal Reconnaissance
Device / Multiple Lateral Movement Model Breaches
Device / Large Number of Model Breaches
Device / Network Scan
Device / Anomalous RDP Followed by Multiple Model Breaches
Device / Possible SMB/NTLM Reconnaissance
Device / SMB Lateral Movement
Anomalous Connection / SMB Enumeration
Anomalous Connection / Possible Share Enumeration Activity
Device / Suspicious SMB Scanning Activity
Device / RDP Scan
Anomalous Connection / Active Remote Desktop Tunnel
Device / Increase in New RPC Services
Device / ICMP Address Scan
Download and Upload
Unusual Activity / Enhanced Unusual External Data Transfer
Unusual Activity / Unusual External Data Transfer
Anomalous Connection / Uncommon 1 GiB Outbound
Anomalous Connection / Data Sent to Rare Domain
Anomalous Connection / Download and Upload
Compliance / SSH to Rare External Destination
Anomalous Server Activity / Rare External from Server
Anomalous Server Activity / Outgoing from Server
Anomalous Connection / Application Protocol on Uncommon Port
Anomalous Connection / Multiple Connections to New External TCP Port
Device / Anomalous SMB Followed By Multiple Model Breaches
Unusual Activity / SMB Access Failures
Lateral Movement and Encryption
User / New Admin Credentials on Server
Compliance / SMB Drive Write
Device / Anomalous RDP Followed By Multiple Model Breaches
Anomalous Connection / High Volume of New or Uncommon Service Control
Anomalous Connection / New or Uncommon Service Control
Device / New or Unusual Remote Command Execution
Anomalous Connection / SMB Enumeration
Additional Beaconing and Tooling
Device / Initial Breach Chain Compromise
Device / Multiple C2 Model Breaches
Compromise / Large Number of Suspicious Failed Connections
Compromise / Sustained SSL or HTTP Increase
Compromise / SSL or HTTP Beacon
Compromise / Suspicious Beaconing Behavior
Compromise / Large Number of Suspicious Successful Connections
Compromise / High Volume of Connections with Beacon Score
Compromise / Slow Beaconing Activity To External Rare
Compromise / SSL Beaconing to Rare Destination
Compromise / Beaconing Activity To External Rare
Compromise / Beacon to Young Endpoint
Compromise / Agent Beacon to New Endpoint
Anomalous Server Activity / Rare External from Server
Anomalous Connection / Multiple Failed Connections to Rare Endpoint
Anomalous File / EXE from Rare External Location
IoC - Type - Description + Confidence
dataspt[.]com - Hostname - Highly Likely Exfiltration Server
46.22.211[.]151:2022 - IP Address and Unusual Port - Highly Likely Exfiltration Server
faceappinc[.]com - Hostname - Likely C2 Infrastructure
Instructions_read_me.txt - Filename - Almost Certain Ransom Note
212.118.55[.]211 - IP Address - Likely C2 Infrastructure
delete[.]me - Filename - Potential lateral movement script
covet[.]me - Filename - Potential lateral movement script
d0ec4b50a944b182fc10ff51f883ccf7 - JA3 Client Fingerprint - Potential Windows BITS C2 Process
/download/cal.linux - URI - Likely BlackBasta executable file
1f4dcfa562f218fcd793c1c384c3006e460213a8 - Sha1 File Hash - Likely BlackBasta executable file

References
[1] https://blogs.blackberry.com/en/2022/05/black-basta-rebrand-of-conti-or-something-new
[2] https://www.cybereason.com/blog/threat-alert-aggressive-qakbot-campaign-and-the-black-basta-ransomware-group-targeting-u.s.-companies
[4] https://unit42.paloaltonetworks.com/atoms/blackbasta-ransomware/
[6] https://www.pcrisk.com/removal-guides/23666-black-basta-ransomware