Darktrace’s Cyber AI Analyst uncovers the intricate details of a Sodinokibi ransomware attack on a retail organization. Dive into this real-time incident.
Sodinokibi is one of the most lucrative ransomware strains of 2020, with its creators, cyber-criminal gang REvil, recently claiming over $100 million in profits this year alone. The prevalent threat is known to wipe backup files, encrypt files on local shares and exfiltrate data.
Exfiltration before encryption is a technique being increasingly adopted by profit-seeking cyber-criminals, who can threaten to leak the stolen data should a target organization not comply with their demands. Sodinobiki also makes heavy use of code obfuscation and encryption techniques to evade detection by signature-based, anti-virus solutions.
Darktrace’s AI recently detected Sodinokibi targeting a retail organization in the US. Prior to this year, the company operated primarily face-to-face in physical stores, but have conducted the majority of their business in the digital realm since the onset of the pandemic.
Cyber AI Analyst automatically launched a full investigation into this incident in real time, as the attack was unfolding. The technology provided summary reports of the entire incident which the security team could immediately action for incident response. This blog explores its findings.
Sodinokibi timeline
Darktrace automatically investigated on the full scope of the Sodinokibi attack, with Cyber AI Analyst clearly identifying and summarising every stage of the attack lifecycle, which played out over the course of three weeks as below:
Figure 1: A timeline of the attack
Darktrace produced a large number of security-relevant anomalies associated with just three credentials, and displayed these along a common timeline shown below:
Figure 2: A timeline view of anomaly detections separated by users. Note the clusters of model breaches for the compromised credentials leading up to October 14.
While a human analyst might have been able to identify these unusual patterns and investigate what caused the clusters of anomalous activity, this process would have taken precious hours during a crisis. Cyber AI Analyst automatically performed the same analysis using supervised machine learning trained on Darktrace’s world-leading analysts, generating meaningful summaries of each stage of the event in real time, as the incident unfolded.
REvil ransomware attack
The following events occurred during a free trial period, and Darktrace was not being actively monitored. Its Autonomous Response technology, Darktrace Antigena, was installed in passive mode, and in the absence of automatic interference at an early stage, this compromise was allowed to unfold without interruption. However, with Darktrace’s AI learning normal ‘patterns of life’ for every device in the background, identifying anomalies, and launching an automated investigation into the attack, we are able to go back into the Threat Visualizer and see how the incident unfolded.
The attack began when the credentials of a highly privileged member of the retail organization’s IT team were compromised. REvil is known to make use of phishing emails, exploit kits, server vulnerabilities, and compromised MSP networks for initial intrusion.
In this case, the attacker used the IT credential to compromise a domain controller and exfiltrate data directly after initial reconnaissance. Darktrace’s AI detected the attacker logging into the domain controller via SMB, writing suspicious files and then deleting batch scripts and log files in the root directory to clear their tracks.
The domain controller then made connections to several rare external endpoints, and Darktrace witnessed a 28MB upload that was likely exfiltration of initial reconnaissance data. Four days later, the attacker connected to the same endpoint (sadstat[.]com) – likely a stager download for C2, which was then initiated via connections on port 443 later that same day.
A week on from the intial C2 connection, a SQL server was detected engaging in network scanning as the attacker sought to move laterally in search of sensitive and valuable data. Over the course of two weeks, Darktrace witnessed unusual internal RDP connections using administrative credentials, before data was uploaded to multiple cloud storage endpoints as well as an SSH server. PsExec was used to deploy the ransomware, resulting in file encryption.
The evasive nature of modern ransomware
REvil started with an inherent advantage in that they were armed with the credentials of a highly privileged IT admin. Nevertheless, they still made several attempts to evade traditional, signature-based tools, such as ‘Living off the Land’ – using common tools such PsExec, WMI, RDP to blend into to legitimate activity.
They leveraged frequently-used cloud storage solutions like Dropbox and pCloud for data transfer, and they conducted SSH on port 443, blending in with SSL connections on the same port. They used a newly-registered domain for C2 communication, meaning Open Source Intelligence Tools (OSINT) were blind to the threat.
Finally, the malware itself was evasive in that it made use of code obfuscation and encryption, and had no need for a system library or API imports. This is the basis for most modern ransomware attacks, and the reality is signature-based tools cannot keep up. Darktrace’s AI not only detected the anomalous activity associated with every stage of the attack, but generated fleshed-out summaries of each stage of the attack with Cyber AI Analyst.
Cyber AI Analyst: Real-time incident reporting
Between September 21 and October 12, Cyber AI Analyst created 15 incidents, investigating dozens of point detections and creating a coherent attack narrative.
Figure 3: Cyber AI Incident log of the first compromised DC. This incident tab details the connections to sadstat[.]com
Figure 4: The DC establishes C2 to the first GHOSTnet GmbH IP
Figure 5: This incident tab highlights the file encryption of files on network shares
Figure 6: Darktrace surfaces the IT admin account takeover
Figure 7: Example of a client type device involved in extensive administrative RDP and SMB activity, as well as data uploads to Dropbox (this upload to Dropbox occurs few seconds before file encryption begins)
REvil vs AI
This Sodinokibi ransomware attack slipped under the radar of a range of traditional tools deployed by the retail organization. However, despite the threat dwelling in the retail organization’s digital environment for over a month, and REvil using local tools to blend in to regular traffic, from Darktrace’s perspective these actions were noisy in comparison to the organization’s normal ‘pattern of life’, setting off a series of alerts and investigations.
Darktrace’s Cyber AI Analyst was able to autonomously investigate nearly every attack phase of the ransomware. The technology works around the clock, without requiring training or time off, and can often reduce hours or days of incident response into just minutes, reducing time to triage by up to 92% and augmenting the capabilities of the human security team.
Thanks to Darktrace analyst Joel Lee for his insights on the above threat find.
Anomalous Server Activity / Anomalous External Activity from Critical Network Device
Compliance / SMB Drive Write
Compliance / Possible Tor Usage
Compromise / Ransomware / Ransom or Offensive Words Written to SMB
Compromise / Ransomware / Suspicious SMB Activity
Device / ICMP Address Scan
Device / Multiple Lateral Movement Model Breaches
Device / Network Scan
Device / New or Uncommon WMI Activity
Device / New or Unusual Remote Command Execution
Device / RDP Scan
Device / Suspicious Network Scan Activity
Unusual Activity / Enhanced Unusual External Data Transfer
Unusual Activity / Unusual Internal Connections
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Inside the SOC
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.
Author
Max Heinemeyer
Chief Product Officer
Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.
Darktrace is positioned as a Leader in the IDC MarketScape: Worldwide Network Detection and Response 2024 Vendor Assessment
Darktrace is pleased to announce that we have been positioned as a Leader in the IDC MarketScape: Worldwide Network Detection and Response 2024 Vendor Assessment. We believe this further highlights Darktrace’s position as a pioneer in the NDR market and follows similar recognition from KuppingerCole, who recently named Darktrace as an Overall Leader, Product Leader, Market Leader and Innovation Leader in the KuppingerCole Leadership Compass: Network Detection and Response (2024).
Network Detection and Response (NDR) solutions are uniquely positioned to provide visibility over the core hub of a business and employee activity, analyzing North-South and East-West traffic to identify threats across the modern network. NDR provides a rich and true source of anomalies and goes beyond process level data that is relied on by Endpoint Detection and Response (EDR) agents that do not provide network level visibility and can be misconfigured at any time.1
Metadata from network traffic can be used to detect a variety of different threats based on events such as anomalous port usage, unusual upload/download activity, impossible travel and many other activities.This has been accelerated by the increased usage of user behavioral analytics (UBA) in network security, which establishes statistical baselines about network entities and highlights deviations from expected activity.1
Darktrace is recognized as a Leader in the IDC MarketScape due to our leadership in the market and our pioneering leadership in AI over the past decade, alongside a variety of other unique differentiators and innovations in the NDR industry.
Darktrace / NETWORK™ delivers full visibility, real time threat detection and Autonomous Response capabilities across an organization’s on-premises, cloud, hybrid and virtual environments, including remote worker endpoints.
Unique Approach to AI
Most NDR vendors and network security tools such as IDS/IPS rely on detecting known attacks with historical data and supervised machine learning, leaving organizations blind and vulnerable to novel threats such as zero-days, variants of known attacks, supply chain attacks and insider threats.
These vendors also tend to apply AI models that are trained globally, and are not unique to each organization’s environment, which creates a high number of false positives and alerts that ultimately lack business context.
The IDC MarketScape recognizes that Darktrace takes a differentiated approach in the market with regards to delivering network detection and response capabilities, noting; “Darktrace is unique in that it does not rely on rules and signatures but rather learns what constitutes as normal for an organization and generates alerts when there is a deviation.”1
Darktrace / NETWORK achieves this through the use of Self-Learning AI and unsupervised machine learning to understand what is normal network behavior, continuously analyzing, mapping and modeling every connection to create a full picture of devices, identities, connections and potential attack paths. Darktrace Self-Learning AI autonomously optimizes itself to cut through the noise and quickly surface genuine, prioritized network security incidents – significantly reducing false positives and removing the hassle of needing to continually tuning alerts manually.
Darktrace’s unique approach to AI also extends to the investigation and triage of network alerts with Cyber AI Analyst. Unlike a chat or prompt based LLM, Cyber AI Analyst investigates all relevant alerts in an environment, including third party alerts, autonomously forming hypotheses and reaching conclusions just like a human analyst would, accelerating SOC Level 2 analyses of incidents by 10x. Cyber AI Analyst also typically providing SOC teams with up to 50,000 additional hours annually of Level 2 analysis producing high level alerts and written reporting, transforming security operations.2
Darktrace also uses its deep understanding of what is normal for a network to identify suspicious behavior, leveraging Autonomous Response capabilities to shut down both known and novel threats in real time, taking targeted actions without disrupting business operations. Darktrace / NETWORK is the only NDR solution that can autonomously enforce a pattern of life based on what is normal for a standalone device or group of peers, rapidly containing and disarming threats based on the overall context of the environment and a granular understanding of what is normal for a device or user – instead of relying on historical attack data.
Continued NDR Market Leadership
Darktrace has been recognized as a Leader in the NDR market, and the IDC MarketScape listed a variety of strengths:
Darktrace achieves roughly one-fifth of all global NDR revenue. This is important because other IT and cybersecurity solutions providers necessarily want to have integration with Darktrace.
The AI algorithms that Darktrace uses for NDR have had 10 years of deployments, tuning, and learning to draw from.
Darktrace is available as a SaaS, as an enterprise license, and as physical, hybrid, or virtual appliances. Darktrace also offers an endpoint agent and visibility into VPN and ZTNA.
Darktrace integrates with 30+ different interfaces including SIEM, SOAR, XDR platforms, IT ticketing solutions, and their own dashboards. The Darktrace Threat Visualizer highlights events and incidents from the entire deployment including cloud, apps, email, endpoint, zero trust, network, and OT.
Darktrace / NETWORK charts the progress that the SOC is making over time with key metrics such as MTTD/MTTR, alerts generated and processed, and other criteria.
Darktrace reported coverage of 14 MITRE ATT&CK categories, 158 techniques, and 184 subtechniques
Proactive Network Resilience
The IDC MarketScape notes, “Ultimately, NDR shines as a standalone detection and response technology but is especially powerful when combined with other platforms. NDR in combination with other control points such as endpoint, data, identity, and application provides the proper context when winnowing alerts and trying to uncover a single source of truth.” . Darktrace comprehensively addresses this as part of the ActiveAI Security Platform, by combining network alerts with data from / EMAIL, / IDENTITY, / ENDPOINT, / CLOUD and / OT, providing deeper contextual analysis for each network alert and automatically enriching investigations.
Darktrace also goes beyond NDR solutions with capabilities that are closely linked to our NDR offering, helping clients to achieve and maintain a state of proactive network resilience:
Darktrace / Proactive Exposure Management – look beyond just CVE risks to discover, prioritize and validate risks by business impact and how to address them early, reducing the number of real threats that security teams need to handle.
Darktrace / Incident Readiness & Recovery – lets teams respond in the best way to each incident and proactively test their familiarity and effectiveness of IR workflows with sophisticated incident simulations based on their own analysts and assets.
Together, these solutions allow Darktrace / NETWORK to go beyond the traditional approach to NDR and shift teams to a more hardened and proactive stance.
Protecting Clients with Continued Innovation
Darktrace invests heavily in Research and Development to continue providing customers with market-leading NDR capabilities and innovations, which was reflected in our position in the Leader category of the MarketScape report for both capabilities and strategy. We are led by the needs and challenges of our customers, which serve as the driving force behind our continued innovation and leadership in the NDR market. The IDC MarketScape report underlines this approach with the following feedback presented by Darktrace customers:
“A customer intimated that 99% of their detections were OOTB with little need to tune or define parameters.”
“A customer reported that it had early warnings for adversarial tactics such as suspicious SMB scanning, suspicious remote execution, remote desktop protocol (RDP) scanning, data exfiltration, C2C, LDAP query, and suspicious Kerberos activity.”
“The client could use Regex to determine if suspicious behavior was found elsewhere on the network.”
Thousands of customers around the world across all industries and sectors rely on Darktrace / NETWORK to protect against known and novel threats. From the latest vulnerabilities in network hardware to sophisticated new strains of ransomware and everything in-between, Darktrace helps clients detect and respond to all types of threats affecting their networks and avoid business disruption, even from the latest attacks.
Find out more about the unique capabilities of Darktrace / NETWORK and our application of AI in network security in the IDC MarketScape excerpt.
References
IDC MarketScape: Worldwide Network Detection and Response 2024 Vendor Assessment (Doc #US51752324, November 2024)
Darktrace’s view on Operation Lunar Peek: Exploitation of Palo Alto firewall devices (CVE 2024-2012 and 2024-9474)
Introduction: Spike in exploitation and post-exploitation activity affecting Palo Alto firewall devices
As the first line of defense for many organizations, perimeter devices such as firewalls are frequently targeted by threat actors. If compromised, these devices can serve as the initial point of entry to the network, providing access to vulnerable internal resources. This pattern of malicious behavior has become readily apparent within the Darktrace customer base. In 2024, Darktrace Threat Research analysts identified and investigated at least two major campaigns targeting internet-exposed perimeter devices. These included the exploitation of PAN-OS firewall exploitation via CVE 2024-3400 and FortiManager appliances via CVE 2024-47575.
More recently, at the end of November, Darktrace analysts observed a spike in exploitation and post-exploitation activity affecting, once again, Palo Alto firewall devices in the days following the disclosure of the CVE 2024-0012 and CVE-2024-9474 vulnerabilities.
Threat Research analysts had already been investigating potential exploitation of the firewalls’ management interface after Palo Alto published a security advisory (PAN-SA-2024-0015) on November 8. Subsequent analysis of data from Darktrace’s Security Operations Center (SOC) and external research uncovered multiple cases of Palo Alto firewalls being targeted via the likely exploitation of these vulnerabilities since November 13, through the end of the month. Although this spike in anomalous behavior may not be attributable to a single malicious actor, Darktrace Threat Research identified a clear increase in PAN-OS exploitation across the customer base by threat actors likely utilizing the recently disclosed vulnerabilities, resulting in broad patterns of post-exploitation activity.
How did exploitation occur?
CVE 2024-0012 is an authentication bypass vulnerability affecting unpatched versions of Palo Alto Networks Next-Generation Firewalls. The vulnerability resides in the management interface application on the firewalls specifically, which is written in PHP. When attempting to access highly privileged scripts, users are typically redirected to a login page. However, this can be bypassed by supplying an HTTP request where a Palo Alto related authentication header can be set to “off”. Users can supply this header value to the Nginx reverse proxy server fronting the application which will then send it without any prior processing [1].
CVE-2024-9474 is a privilege escalation vulnerability that allows a PAN-OS administrator with access to the management web interface to execute root-level commands, granting full control over the affected device [2]. When combined, these vulnerabilities enable unauthenticated adversaries to execute arbitrary commands on the firewall with root privileges.
Post-Exploitation Patterns of Activity
Darktrace Threat Research analysts examined potential indicators of PAN-OS software exploitation via CVE 2024-0012 and CVE-2024-9474 during November 2024. The investigation identified three main groupings of post-exploitation activity:
Exploit validation and initial payload retrieval
Command and control (C2) connectivity, potentially featuring further binary downloads
Potential reconnaissance and cryptomining activity
Exploit Validation
Across multiple investigated customers, Darktrace analysts identified likely vulnerable PAN-OS devices conducting external network connectivity to bin services. Specifically, several hosts performed DNS queries for, and HTTP requests to Out-of-Band Application Security Testing (OAST) domains, such as csv2im6eq58ujueonqs0iyq7dqpak311i.oast[.]pro. These endpoints are commonly used by network administrators to harden defenses, but they are increasingly used by threat actors to verify successful exploitation of targeted devices and assess their potential for further compromise. Although connectivity involving OAST domains were prevalent across investigated incidents, this activity was not necessarily the first indicator observed. In some cases, device behavior involving OAST domains also occurred shortly after an initial payload was downloaded.
Figure 1: Darktrace model alert logs detailing the HTTP request to an OAST domain immediately following PAN-OS device compromise.
Initial Payload Retrieval
Following successful exploitation, affected devices commonly performed behaviors indicative of initial payload download, likely in response to incoming remote command execution. Typically, the affected PAN-OS host would utilize the command line utilities curl and Wget, seen via use of user agents curl/7.61.1 and Wget/1.19.5 (linux-gnu), respectively.
In some cases, the use of these command line utilities by the infected devices was considered new behavior. Given the nature of the user agents, interaction with the host shell suggests remote command execution to achieve the outgoing payload requests.
While additional binaries and scripts were retrieved in later stages of the post-exploitation activity in some cases, this set of behaviors and payloads likely represent initial persistence and execution mechanisms that will enable additional functionality later in the kill chain. During the investigation, Darktrace analysts noted the prevalence of shell script payload requests. Devices analyzed would frequently make HTTP requests over the usual destination port 80 using the command line URL utility (curl), as seen in the user-agent field.
The observed URIs often featured requests for text files, such as “1.txt”, or shell scripts such as “y.sh”. Although packet capture (PCAP) samples were unavailable for review, external researchers have noted that the IP address hosting such “1.txt” files (46.8.226[.]75) serves malicious PHP payloads. When examining the contents of the “y.sh” shell script, Darktrace analysts noticed the execution of bash commands to upload a PHP-written web shell on the affected server.
Figure 2: PCAP showing the client request and server response associated with the download of the y.sh script from 45.76.141[.]166. The body content of the HTTP response highlights a shebang command to run subsequent code as bash script. The content is base64 encoded and details PHP script for what appears to be a webshell that will likely be written to the firewall device.
While not all investigated cases saw initial shell script retrieval, affected systems would commonly make an external HTTP connection, almost always via Wget, for the Executable and Linkable Format (ELF) file “/palofd” from the rare external IP 38.180.147[.]18.
Such requests were frequently made without prior hostname lookups, suggesting that the process or script initiating the requests already contained the external IP address. Analysts noticed a consistent SHA1 hash present for all identified instances of “/palofd” downloads (90f6890fa94b25fbf4d5c49f1ea354a023e06510). Multiple open-source intelligence (OSINT) vendors have associated this hash sample with Spectre RAT, a remote access trojan with capabilities including remote command execution, payload delivery, process manipulation, file transfers, and data theft [3][4].
Figure 3: Advanced Search log metrics highlighting details of the “/palofd” file download over HTTP.
Several targeted customer devices were observed initiating TLS/SSL connections to rare external IPs with self-signed TLS certificates following exploitation. Model data from across the Darktrace fleet indicated some overlap in JA3 fingerprints utilized by affected PAN-OS devices engaging in the suspicious TLS activity. Although JA3 hashes alone cannot be used for process attribution, this evidence suggests some correlation of source process across instances of PAN-OS exploitation.
These TLS/SSL sessions were typically established without the specification of a Server Name Indication (SNI) within the TLS extensions. The SNI extension prevents servers from supplying an incorrect certificate to the requesting client when multiple sites are hosted on the same IP. SSL connectivity without SNI specification suggests a potentially malicious running process as most software establishing TLS sessions typically supply this information during the handshake. Although the encrypted nature of the connection prevented further analysis of the payload packets, external sources note that JavaScript content is transmitted during these sessions, serving as initial payloads for the Sliver C2 platform using Wget [5].
C2 Communication and Additional Payloads
Following validation and preliminary post-compromise actions, examined hosts would commonly initiate varying forms of C2 connectivity. During this time, devices were frequently detected making further payload downloads, likely in response to directives set within C2 communications.
Palo Alto firewalls likely exploited via the newly disclosed CVEs would commonly utilize the Sliver C2 platform for external communication. Sliver’s functionality allows for different styles and formatting for communication. An open-source alternative to Cobalt Strike, this framework has been increasingly popular among threat actors, enabling the generation of dynamic payloads (“slivers”) for multiple platforms, including Windows, MacOS, Linux.
These payloads allow operators to establish persistence, spawn new shells, and exfiltrate data. URI patterns and PCAPs analysis yielded evidence of both English word type encoding within Sliverand Gzip formatting.
For example, multiple devices contacted the Sliver-linked IP address 77.221.158[.]154 using HTTP to retrieve Gzip files. The URIs present for these requests follow known Sliver Gzip formatted communication patterns [6]. Investigations yielded evidence of both English word encoding within Sliver, identified through PCAP analysis, and Gzip formatting.
Figure 4: Sample of URIs observed in Advanced Searchhighlighting HTTP requests to 77.221.158[.]154 for Gzip content suggest of Sliver communication.
Figure 5: PCAP showing English word encoding for Sliver communication observed during post-exploitation C2 activity.
External connectivity during this phase also featured TCP connection attempts over uncommon ports for common application protocols. For both Sliver and non-Sliver related IP addresses, devices utilized destination ports such as 8089, 3939, 8880, 8084, and 9999 for the HTTP protocol. The use of uncommon destination ports may represent attempts to avoid detection of connectivity to rare external endpoints. Moreover, some external beaconing within included URIs referencing the likely IP of the affected device. Such behavior can suggest the registration of compromised devices with command servers.
Targeted devices also proceeded to download additional payloads from rare external endpoints as beaconing/C2 activity was ongoing. For example, the newly registered domain repositorylinux[.]org (IP: 103.217.145[.]112) received numerous HTTP GET requests from investigated devices throughout the investigation period for script files including “linux.sh” and “cron.sh”. Young domains, especially those that present as similar to known code repositories, tend to host harmful content. Packet captures of the cron.sh file reveal commands within the HTTP body content involving crontab operations, likely to schedule future downloads. Some hosts that engaged in connectivity to the fake repository domain were later seen conducting crypto-mining connections, potentially highlighting the download of miner applications from the domain.
Additional payloads observed during this time largely featured variations of shell scripts, PHP content, and/or executables. Typically, shell scripts direct the device to retrieve additional content from external servers or repositories or contain potential configuration details for subsequent binaries to run on the device. For example, the “service.sh” retrieves a tar-compressed archive, a configuration JSON file as well as a file with the name “solr” from GitHub, potentially associated with the Apache Solr tool used for enterprise search. These could be used for further enumeration of the host and/or the network environment. PHP scripts observed may involve similar web shell functionality and were retrieved from both rare external IPs identified as well by external researchers [7]. Darktrace also detected the download of octet-stream data occurring mid-compromise from an Amazon Web Services (AWS) S3 bucket. Although no outside research confirmed the functionality, additional executable downloads for files such as “/initd”(IP: 178.215.224[.]246) and “/x6” (IP: 223.165.4[.]175) may relate to tool ingress, further Trojan/backdoor functionality, or cryptocurrency mining.
Figure 7: PCAP specifying the HTTP response headers and body content for the service.sh file request. The body content shown includes variable declarations for URLs that will eventually be called by the device shell via bash command.
Reconnaissance and Cryptomining
Darktrace analysts also noticed additional elements of kill chain operations from affected devices after periods of initial exploit activity. Several devices initiated TCP connections to endpoints affiliated with cryptomining pools such as us[.]zephyr[.]herominers[.]com and xmrig[.]com. Connectivity to these domains indicates likely successful installation of mining software during earlier stages of post-compromise activity. In a small number of instances, Darktrace observed reconnaissance and lateral movement within the time range of PAN-OS exploitation. Firewalls conducted large numbers of internal connectivity attempts across several critical ports related to privileged protocols, including SMB and SSH. Darktrace detected anonymous NTLM login attempts and new usage of potential PAN-related credentials. These behaviors likely constitute attempts at lateral movement to adjacent devices to further extend network compromise impact.
Figure 8: Model alert connection logs detailing the uncommon failed NTLM logins using an anonymous user account following PAN-OS exploitation.
Conclusion
Darktrace Threat Research and SOC analysts increasingly detect spikes in malicious activity on internet-facing devices in the days following the publication of new vulnerabilities. The latest iteration of this trend highlighted how threat actors quickly exploited Palo Alto firewall using authentication bypass and remote command execution vulnerabilities to enable device compromise. A review of the post-exploitation activity during these events reveals consistent patterns of perimeter device exploitation, but also some distinct variations.
Prior campaigns targeting perimeter devices featured activity largely confined to the exfiltration of configuration data and some initial payload retrieval. Within the current campaign, analysts identified a broader scope post-compromise activity consisting not only of payloads downloads but also extensive C2 activity, reconnaissance, and coin mining operations. While the use of command line tools like curl featured prominently in prior investigations, devices were seen retrieving a generally wider array of payloads during the latest round of activity. The use of the Sliver C2 platform further differentiates the latest round of PAN-OS compromises, with evidence of Sliver activity in about half of the investigated cases.
Several of the endpoints contacted by the infected firewall devices did not have any OSINT associated with them at the time of the attack. However, these indicators were noted as unusual for the devices according to Darktrace based on normal network traffic patterns. This reality further highlights the need for anomaly-based detection that does not rely necessarily on known indicators of compromise (IoCs) associated with CVE exploitation for detection. Darktrace’s experience in 2024 of multiple rounds of perimeter device exploitation may foreshadow future increases in these types of comprise operations.
Credit to Adam Potter (Senior Cyber Analyst), Alexandra Sentenac (Senior Cyber Analyst), Emma Foulger (Principal Cyber Analyst) and the Darktrace Threat Research team.
![PCAP showing the client request and server response associated with the download of the y.sh script from 45.76.141[.]166. The body content of the HTTP response highlights a shebang command to run subsequent code as bash script. The content is base64 encoded and details PHP script for what appears to be a webshell that will likely be written to the firewall device.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/675700c4a98f6ccec0f1a975_6756f988e443cb72200722b4_Screenshot%25202024-12-09%2520at%252014.06.53.png)
![Sample of URIs observed in Advanced Searchhighlighting HTTP requests to 77.221.158[.]154 for Gzip content suggest of Sliver communication.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/6757623084734b0dbf6418e4_675761ed5dcf48e9b0b0befb_Screenshot%25202024-12-09%2520at%25201.31.31%25E2%2580%25AFPM.png)
