June 20, 2024

Post-Exploitation Activities on PAN-OS Devices: A Network-Based Analysis

This blog investigates the network-based activity detected by Darktrace in compromises stemming from the exploitation of a vulnerability in Palo Alto Networks firewall devices, namely CVE-2024-3400.

Written by

Adam Potter

Senior Cyber Analyst

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.

Written by

Adam Potter

Senior Cyber Analyst

Jun 2024

Update:
Following the initial publication of this blog detailing exploitation campaigns utilizing the recently disclosed vulnerability, Darktrace analysts expanded the scope of the threat research investigation to identify potential earlier, pre-CVE disclosure, exploitation of CVE 2024-3400. While the majority of PAN-OS exploitation activity seen in the Darktrace customer base occurred after the public release of the CVE, Darktrace did also see tooling activity likely related to CVE-2024-3400 exploitation prior to the vulnerability's disclosure. Unlike the post-CVE-release exploitation activity, which largely reflected indiscriminate, opportunistic targeting of unpatched systems, these pre-CVE release activities likely represented selective targeting by more calculated actors.

Between March 26 and 28, Darktrace identified two Palo Alto firewall devices within the network of a public sector customer making HTTP GET requests utilizing both cURL and wget user agents, versions of which were seen in later compromise activity in April. The devices requested multiple shell script files (.sh) from rare external IP addresses. These IPs are likely associated with an operational relay box (ORB) network^[1]. The connections also occurred without a specified hostname lookup, suggesting the IPs were hardcoded into process code or already cached through unexpected running processes. One of the destination IPs was later confirmed by Palo Alto Network’s Unit 42 as associated with exploitation of the PAN-OS vulnerability^[2]. This observed activity closely resembles post-exploitation activity seen on affected firewall devices in mid-April. However, unlike the more disruptive and noisier follow-on exploitation activity seen in post-CVE-release incidents, the pre-CVE-release case observed by Darktrace appears to have been much more discreet, likely due to the relevant threat actor's desire to remain undetected.

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Introduction

Perimeter devices such as firewalls, virtual private networks (VPNs), and intrusion prevention systems (IPS), have long been the target of adversarial actors attempting to gain access to internal networks. However, recent publications and public service announcements by leading public institutions underscore the increased emphasis threat actors are putting on leveraging such products to initiate compromises.

A blog post by the UK National Cyber Security Center (NCSC) released in early 2024 notes that as improvements are made in the detection of phishing email payloads, threat actors have again begun re-focusing efforts to exploiting network edge devices, many of which are not secure by design, as a means of breach initiation.^[i] As such, it comes as no surprise that new Common Vulnerabilities and Exposures (CVEs) are constantly discovered that exploit such internet-exposed systems.

Darktrace analysts frequently observe the impacts of such CVEs first through their investigations via Darktrace’s Security Operations Center (SOC). Beginning in April 2024, Darktrace’s SOC began handling alerts and customer requests for potential incidents involving Palo Alto Networks firewall devices. Just days prior, external researchers publicly disclosed what would later be classified as PAN-OS CVE-2024-3400, a form of remote command execution vulnerability that affects several versions of Palo Alto Networks’ firewall operating system (PAN-OS), namely PAN-OS 11.1, 11.0 and 10.2. At the time, multiple Darktrace customers were unaware of the recently announced vulnerability.

The increase in observed SOC activity for Palo Alto firewall devices, coupled with the public announcement of the new CVE prompted Darktrace researchers to look for evidence of PAN-OS exploitation on customer networks. Researchers also focused on documenting post-exploitation activity from threat actors leveraging the recently disclosed vulnerability.

As such, this blog highlights the network-based behaviors involved in the CVE-2024-3400 attack chains investigated by Darktrace’s SOC and Threat Research teams. Moreover, this investigation also provides a deeper insight into the post-compromise activities of threat actors leveraging the novel CVE. Such insights will not only prove relevant for cybersecurity teams looking to inhibit compromises in this specific instance, but also highlights general patterns of behavior by threat actors utilizing such CVEs to target internet-facing systems.

CVE-2024-3400

In mid-April 2024, the Darktrace SOC observed an uptick in activity involving recurring patterns of malicious activity from Palo Alto firewall appliances. In response to this trend, Darktrace initiated a Threat Research investigation into such activity to try and identify common factors and indicators across seemingly parallel events. Shortly before the Threat Research team opened their investigation, external researchers provided public details of CVE-2024-3400, a form of remote command execution vulnerability in the GlobalProtect feature on Palo Alto Network firewall devices running PAN-OS versions: 10.2, 11.0, and 11.1.^[ii]

In their proof of concept, security researchers at watchTowr demonstrated how an attacker can pass session ID (SESSID) values to these PAN-OS devices to request files that do not exist. In response, the system creates a zero-byte file with root privileges with the same name.^[iii] Log data is passed on devices running telemetry services to external servers through command line functionality.^[iv] Given this functionality, external actors could then request non-existent files in the SESSID containing command parameters which then be interpreted by the command line functionality.^[v] Although researchers first believed the exploit could only be used against devices running telemetry services, this was later discovered to be untrue.^[vi]

As details of CVE-2024-3400 began to surface, Darktrace’s Threat Research analysts quickly identified distinct overlaps in the observed activity on specific customer deployments and the post-exploitation behavior reported by external researchers. Given the parallels, Darktrace correlated the patterns of activity observed by the SOC team to exploitation of the newly discovered vulnerability in PAN-OS firewall appliances.

Campaign Analysis

Between the April and May 2024, Darktrace identified four main themes of post-exploitation activity involving Palo Alto Network firewall devices likely targeted via CVE-2024-3400: exploitation validation, shell command and tool retrieval, configuration data exfiltration, and ongoing command and control through encrypted channels and application protocols.

1. Exploit Validation and Further Vulnerability Enumeration

Many of the investigated attack chains began with malicious actors using out-of-band application security testing (OAST) services such as Interactsh to validate exploits against Palo Alto firewall appliances. This exploit validation activity typically resulted in devices attempting to contact unusual external endpoints (namely, subdomains of ‘oast[.]pro’, ‘oast[.]live’, ‘oast[.]site’, ‘oast[.]online’, ‘oast[.]fun’, ‘oast[.]me’, and ‘g3n[.]in’) associated with OAST services such as Interactsh. These services can be used by developers to inspect and debug internet traffic, but also have been easily abused by threat actors.

While attempted connections to OAST services do not alone indicate CVE-2024-3400 exploitation, the prevalence of such activities in observed Palo Alto firewall attack chains suggests widespread usage of these OAST services to validate initial access methods and possibly further enumerate systems for additional vulnerabilities.

Figure 1: Model alert log details showcasing a PAN-OS device making DNS queries for Interactsh domain names in what could be exploit validation, and/or further host enumeration.

2. Command and Payload Transmission

The most common feature across analyzed incidents was HTTP GET requests for shell scripts and Linux executable files (ELF) from external IPs associated with exploitation of the CVE. These HTTP requests were frequently initiated using the utilities, cURL and wget. On nearly every device likely targeted by threat actors leveraging the CVE, Darktrace analysts highlighted the retrieval of shell scripts that either featured enumeration commands, the removal of evidence of compromise activity, or commands to retrieve and start binaries on the destination device.

a) Shell Script Retrieval

Investigated devices commonly performed HTTP GET requests to retrieve shell command scripts. Despite this commonality, there was some degree of variety amongst the retrieved payloads and their affiliation with certain command tools. Several distinct types of shell commands and files were identified during the analyzed breaches. For example, some firewall devices were seen requesting .txt files associated with both Sliver C2, whose malicious use has previously been investigated by Darktrace, and Cobalt Strike. The target URIs of devices’ HTTP requests for these files included, “36shr.txt”, “2.txt”, “bin.txt”, and “data.txt”.

More interestingly, though, was the frequency with which analyzed systems requested bash scripts from rare external IP addresses, sometimes over non-standard ports for the HTTP protocol. These bash scripts would feature commands usually for the recipient system to check for certain existing files and or running processes. If the file did not exist, the system would then use cURL or wget to obtain content from external sites, change the permissions of the file, and then execute, sending output to dev/null as a means of likely defense evasion. In some scripts, the system would first make a new folder, and change directories prior to acquiring external content. Additionally, some samples highlighted multiple attempts at enumeration of the host system.

Figure 2: Packet capture (PCAP) data highlighting the incoming shell scripts providing instructions to use cURL to obtain external content, change the permissions of the file to execute, and then run the binary using the credentials and details provided.

Figure 3: PCAP data highlighting a variation of a shell script seen in an HTTP response processed by compromised devices. The script provides instructions to make a directory, retrieve and execute external content, and to hide the output.

Not every retrieved file that was not explicitly a binary featured bash scripts. Model alerts on some deployments also included file masquerading attempts by threat actors, whereby the Palo Alto firewall device would request content with a misleading extension in the URI. In one such instance, the requested URI, and HTTP response header suggests the returned content is an image/png, but the actual body response featured configuration parameters for a new daemon service to be run on the system.

Figure 4: PCAP data indicating configuration details likely for a new daemon on an investigated host. Such HTTP body content differs from the image/png extension within the request URI and declared content type in the HTTP response header.

Bash scripts analyzed across customer deployments also mirrored those identified by external security teams. External researchers previously reported on a series of identifiable shell commands in some cases of CVE-2024-3400 exploitation analyzed by their teams. Commands frequently involved a persistence mechanism they later labeled as the “UPSTYLE” backdoor.^[vii] This python-based program operates by reading commands hidden in error logs generated by 404 requests to the compromised server. The backdoor interprets the requests and writes the output to CSS files on the device. In many cases, Darktrace’s Threat Research team noted clear parallels between shell commands retrieved via HTTP GET request with those directly involving UPSTYLE. There were also matches with some URI patterns identified with the backdoor and requests observed on Darktrace deployments.

Figure 5: HTTP response data containing shell commands potentially relating to the UPSTYLE backdoor.

The presence of these UPSTYLE-related shell commands in response to Palo Alto firewall devices’ HTTP requests provides further evidence for initial exploitation of the CVE. Many bash scripts in examined cases interacted with folders and files likely related to CVE-2024-3400 exploitation. These scripts frequently sought to delete contents of certain folders, such as “/opt/panlogs/tmp/device_telemetry/minute/*” where evidence of exploitation would likely reside. Moreover, recursive removal and copy commands were frequently seen targeting CSS files within the GlobalProtect folder, already noted as the vulnerable element within PAN-OS versions. This evidence is further corroborated by host-based forensic analysis conducted by external researchers.^[viii]

Figure 6: PCAP data from investigated system indicating likely defense evasion by removing content on folders where CVE exploitation occurred.

b) Executable File Retrieval

Typically, following command processing, compromised Palo Alto firewall devices proceeded to make web requests for several unusual and potentially malicious files. Many such executables would be retrieved via processed scripts. While there a fair amount of variety in specific executables and binaries obtained, overall, these executables involved either further command tooling such as Sliver C2 or Cobalt Strike payloads, or unknown executables. Affected systems would also employ uncommon ports for HTTP connections, in a likely attempt to evade detection. Extensions featured within the URI, when visible, frequently noted ‘.elf’ (Linux executable) or ‘.exe’ payloads. While most derived hashes did not feature identifiable open-source intelligence (OSINT) details, some samples did have external information tying the sample to specific malware. For example, one such investigation featured a compromised system requesting a file with a hash identified as the Spark malware (backdoor) while another investigated case included a host requesting a known crypto-miner.

Figure 7: PCAP data highlighting compromised system retrieving ELF content from a rare external server running a simple Python HTTP server.

Figure 8: Darktrace model alert logs highlighting a device labeled “Palo Alto” making a HTTP request on an uncommon port for an executable file following likely CVE exploitation.

3. Configuration Data Exfiltration and Unusual HTTP POST Activity

During Darktrace’s investigations, there were also several instances of sensitive data exfiltration from PAN-OS firewall devices. Specifically, targeted systems were observed making HTTP POST requests via destination port 80 to rare external endpoints that OSINT sources associate with CVE-2024-3400 exploitation and activity. PCAP analysis of such HTTP requests revealed that they often contained sensitive configuration details of the targeted Palo Alto firewall devices, including the IP address, default gateway, domain, users, superusers, and password hashes, to name only a few. Threat actors frequently utilized Target URIs such as “/upload” in their HTTP POST requests of this multi-part boundary form data. Again, the User-Agent headers of these HTTP requests largely involved versions of cURL, typically 7.6.1, and wget.

Figure 9: PCAP datahighlighting Palo Alto Firewall device running vulnerable version of PAN-OSposting configuration details to rare external services via HTTP.

Figure 10: Model alert logs highlighting a Palo Alto firewall device performing HTTP POSTs to a rare external IP, without a prior hostname lookup, on an uncommon port using a URI associated with configuration data exfiltration across analyzed incidents

Figure 11: Examples of TargetURIs of HTTP POST requests involving base64 encoded IPs and potential dataegress.

4. Ongoing C2 and Miscellaneous Activity

Lastly, a smaller number of affected Palo Alto firewall devices were seen engaging in repeated beaconing and/or C2 communication via both encrypted and unencrypted protocols during and following the initial series of kill chain events. Such encrypted channels typically involved protocols such as TLS/SSL and SSH. This activity likely represented ongoing communication of targeted systems with attacker infrastructure. Model alerts typically highlighted unusual levels of repeated external connectivity to rare external IP addresses over varying lengths of time. In some investigated incidents, beaconing activity consisted of hundreds of thousands of connections over several days.

Figure 12: Advanced search details highlighting high levels of ongoing external communication to endpoints associated with C2 infrastructure exploiting CVE-2024-3400.

Some beaconing activity appears to have involved the use of the WebSocket protocol, as indicated by the appearance of “/ws” URIs and validated within packet captures. Such connections were then upgraded to an encrypted connection.

Figure 13: PCAP highlighting use of WebSocket protocol to engage in ongoing external connectivity to likely C2 infrastructure following CVE-2024-3400 compromise.

While not directly visible in all the deployments, some investigations also yielded evidence of attempts at further post-exploitation activity. For example, a handful of the analyzed binaries that were downloaded by examined devices had OSINT information suggesting a relation to crypto-mining malware strains. However, crypto-mining activity was not directly observed at this time. Furthermore, several devices also triggered model alerts relating to brute-forcing activity via several authentication protocols (namely, Keberos and RADIUS) during the time of compromise. This brute-force activity likely represented attempts to move laterally from the affected firewall system to deeper parts of the network.

Figure 14: Model alert logs noting repeated SSL connectivity to a Sliver C2-affiliated endpoint in what likely constitutes C2 connectivity.

Figure 15: Model alert logs featuring repeated RADIUS login failures from a compromised PAN-OS device using generic usernames, suggesting brute-force activity.

Conclusion

Between April and late May 2024, Darktrace’s SOC and Threat Research teams identified several instances of likely PAN-OS CVE-2024-3400 exploitation across the Darktrace customer base. The subsequent investigation yielded four major themes that categorize the observed network-based post-exploitation activity. These major themes were exploit validation activity, retrieval of binaries and shell scripts, data exfiltration via HTTP POST activity, and ongoing C2 communication with rare external endpoints. The insights shared in this article will hopefully contribute to the ongoing discussion within the cybersecurity community about how to handle the likely continued exploitation of this vulnerability. Moreover, this article may also help cybersecurity professionals better respond to future exploitation of not only Palo Alto PAN-OS firewall devices, but also of edge devices more broadly.

Threat actors will continue to discover and leverage new CVEs impacting edge infrastructure. Since it is not yet known which CVEs threat actors will exploit next, relying on rules and signatures for the detection of exploitation of such CVEs is not a viable approach. Darktrace’s anomaly-based approach to threat detection, however, is well positioned to robustly adapt to threat actors’ changing methods, since although threat actors can change the CVEs they exploit, they cannot change the fact that their exploitation of CVEs results in highly unusual patterns of activity.

Credit to Adam Potter, Cyber Analyst, Sam Lister, Senior Cyber Analyst

Appendices ‍

Pre-CVE-Release IoCs

38.54[.]104[.]14/3.sh
154.223[.]16[.]34/1.sh
154.223[.]16[.]34/co.sh
38.54[.]104[.]14/

Indicators of Compromise

Indicator – Type – Description

94.131.120[.]80 IP C2 Endpoint

94.131.120[.]80:53/?src=[REDACTED]=hour=root URL C2/Exfiltration Endpoint

134.213.29[.]14/?src=[REDACTED]min=root URL C2/Exfiltration Endpoint

134.213.29[.]14/grep[.]mips64 URL Payload

134.213.29[.]14/grep[.]x86_64 URL Payload

134.213.29[.]14/?deer URL Payload

134.213.29[.]14/?host=IDS URL Payload

134.213.29[.]14/ldr[.]sh URL Payload

91ebcea4e6d34fd6e22f99713eaf67571b51ab01 SHA1 File Hash Payload

185.243.115[.]250/snmpd2[.]elf URL Payload

23.163.0[.]111/com URL Payload

80.92.205[.]239/upload URL C2/Exfiltration Endpoint

194.36.171[.]43/upload URL C2/Exfiltration Endpoint

update.gl-protect[.]com Hostname C2 Endpoint

update.gl-protect[.]com:63869/snmpgp URL Payload

146.70.87[.]237 IP address C2 Endpoint

146.70.87[.]237:63867/snmpdd URL Payload

393c41b3ceab4beecf365285e8bdf0546f41efad SHA1 File Hash Payload

138.68.44[.]59/app/r URL Payload

138.68.44[.]59/app/clientr URL Payload

138.68.44[.]59/manage URL Payload

72.5.43[.]90/patch URL Payload

217.69.3[.]218 IP C2 Endpoint

5e8387c24b75c778c920f8aa38e4d3882cc6d306 SHA1 File Hash Payload

217.69.3[.]218/snmpd[.]elf URL Payload

958f13da6ccf98fcaa270a6e24f83b1a4832938a SHA1 File Hash Payload

6708dc41b15b892279af2947f143af95fb9efe6e SHA1 File Hash Payload

dc50c0de7f24baf03d4f4c6fdf6c366d2fcfbe6c SHA1 File Hash Payload

109.120.178[.]253:10000/data[.]txt URL Payload

109.120.178[.]253:10000/bin[.]txt URL Payload

bc9dc2e42654e2179210d98f77822723740a5ba6 SHA1 File Hash Payload

109.120.178[.]253:10000/123 URL Payload

65283921da4e8b5eabb926e60ca9ad3d087e67fa SHA1 File Hash Payload

img.dxyjg[.]com/6hiryXjZN0Mx[.]sh URL Payload

149.56.18[.]189/IC4nzNvf7w/2[.]txt URL Payload

228d05fd92ec4d19659d71693198564ae6f6b117 SHA1 File Hash Payload

54b892b8fdab7c07e1e123340d800e7ed0386600 SHA1 File Hash Payload

165.232.121[.]217/rules URL Payload

165.232.121[.]217/app/request URL Payload

938faec77ebdac758587bba999e470785253edaf SHA1 File Hash Payload

165.232.121[.]217/app/request63 URL Payload

165.232.121[.]217:4443/termite/165.232.121[.]217 URL Payload

92.118.112[.]60/snmpd2[.]elf URL Payload

2a90d481a7134d66e8b7886cdfe98d9c1264a386 SHA1 File Hash Payload

92.118.112[.]60/36shr[.]txt URL Payload

d6a33673cedb12811dde03a705e1302464d8227f SHA1 File Hash Payload

c712712a563fe09fa525dfc01ce13564e3d98d67 SHA1 File Hash Payload

091b3b33e0d1b55852167c3069afcdb0af5e5e79 SHA1 File Hash Payload

5eebf7518325e6d3a0fd7da2c53e7d229d7b74b6 SHA1 File Hash Payload

183be7a0c958f5ed4816c781a2d7d5aa8a0bca9f SHA1 File Hash Payload

e7d2f1224546b17d805617d02ade91a9a20e783e SHA1 File Hash Payload

e6137a15df66054e4c97e1f4b8181798985b480d SHA1 File Hash Payload

95.164.7[.]33:53/sea[.]png URL Payload

95.164.7[.]33/rules URL Payload

95.164.7[.]33:53/lb64 URL Payload

c2bc9a7657bea17792048902ccf2d77a2f50d2d7 SHA1 File Hash Payload

923369bbb86b9a9ccf42ba6f0d022b1cd4f33e9d SHA1 File Hash Payload

52972a971a05b842c6b90c581b5c697f740cb5b9 SHA1 File Hash Payload

95d45b455cf62186c272c03d6253fef65227f63a SHA1 File Hash Payload

322ec0942cef33b4c55e5e939407cd02e295973e SHA1 File Hash Payload

6335e08873b4ca3d0eac1ea265f89a9ef29023f2 SHA1 File Hash Payload

134.213.29[.]14 IP C2 Endpoint

185.243.115[.]250 IP C2 Endpoint

80.92.205[.]239 IP C2 Endpoint

194.36.171[.]43 IP C2 Endpoint

92.118.112[.]60 IP C2 Endpoint

109.120.178[.]253 IP C2 Endpoint

23.163.0[.]111 IP C2 Endpoint

72.5.43[.]90 IP C2 Endpoint

165.232.121[.]217 IP C2 Endpoint

8.210.242[.]112 IP C2 Endpoint

149.56.18[.]189 IP C2 Endpoint

95.164.7[.]33 IP C2 Endpoint

138.68.44[.]59 IP C2 Endpoint

Img[.]dxyjg[.]com Hostname C2 Endpoint

Darktrace Model Alert Coverage

· Anomalous Connection / New User Agent to IP Without Hostname

· Device / New User Agent (triggered by pre-CVE-release activity)

· Anomalous File / Script from Rare External Location (triggered by pre-CVE-release activity)

· Anomalous File / Masqueraded File Transfer

· Anomalous File / EXE from Rare External Location

· Anomalous File / Multiple EXE from Rare External Locations

· Anomalous File / Script and EXE from Rare External

· Anomalous File / Suspicious Octet Stream Download

· Anomalous File / Numeric File Download

· Anomalous Connection / Application Protocol on Uncommon Port

· Anomalous Connection / Posting HTTP to IP Without Hostname

· Anomalous Connection / Multiple Failed Connections to Rare Endpoint

· Anomalous Connection / Suspicious Self-Signed SSL

· Anomalous Connection / Anomalous SSL without SNI to New External

· Anomalous Connection / Multiple Connections to New External TCP Port

· Anomalous Connection / Rare External SSL Self-Signed

· Anomalous Server Activity / Outgoing from Server

· Anomalous Server Activity / Rare External from Server

· Compromise / SSH Beacon

· Compromise / Beacon for 4 Days

· Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

· Compromise / High Priority Tunnelling to Bin Services

· Compromise / Sustained SSL or HTTP Increase

· Compromise / Connection to Suspicious SSL Server

· Compromise / Suspicious File and C2

· Compromise / Large Number of Suspicious Successful Connections

· Compromise / Slow Beaconing Activity To External Rare

· Compromise / HTTP Beaconing to New Endpoint

· Compromise / SSL or HTTP Beacon

· Compromise / Suspicious HTTP and Anomalous Activity

· Compromise / Beacon to Young Endpoint

· Compromise / High Volume of Connections with Beacon Score

· Compromise / Suspicious Beaconing Behaviour

· Compliance / SSH to Rare External Destination

· Compromise / HTTP Beaconing to Rare Destination

· Compromise / Beaconing Activity To External Rare

· Device / Initial Breach Chain Compromise

· Device / Multiple C2 Model Breaches

MITRE ATTACK Mapping

Tactic – Technique

Initial Access T1190 – Exploiting Public-Facing Application

Execution T1059.004 – Command and Scripting Interpreter: Unix Shell

Persistence T1543.002 – Create or Modify System Processes: Systemd Service

Defense Evasion T1070.004 – Indicator Removal: File Deletion

Credential Access T1110.001 – Brute Force: Password Guessing

Discovery T1083 – File and System Discovery

T1057 – Process Discovery

Collection T1005 – Data From Local System

Command and Control

T1071.001 – Application Layer Protocol: Web Protocols

T1573.002 – Encrypted Channel: Asymmetric Cryptography

T1571 – Non-Standard Port

T1105 – Ingress Tool Transfer

Exfiltration

T1041 – Exfiltration over C2 Protocol

T1048.002 - Exfiltration Over Alternative Protocol: Exfiltration Over Asymmetric Encrypted Non-C2 Protocol

References

[1] https://cloud.google.com/blog/topics/threat-intelligence/china-nexus-espionage-orb-networks

[2] https://unit42.paloaltonetworks.com/cve-2024-3400/

^[i] https://www.ncsc.gov.uk/blog-post/products-on-your-perimeter

^[ii] https://security.paloaltonetworks.com/CVE-2024-3400

^[iii] https://labs.watchtowr.com/palo-alto-putting-the-protecc-in-globalprotect-cve-2024-3400/

^[iv] https://labs.watchtowr.com/palo-alto-putting-the-protecc-in-globalprotect-cve-2024-3400/

^[v] https://labs.watchtowr.com/palo-alto-putting-the-protecc-in-globalprotect-cve-2024-3400/

^[vi] https://security.paloaltonetworks.com/CVE-2024-3400

^[vii] https://www.volexity.com/blog/2024/04/12/zero-day-exploitation-of-unauthenticated-remote-code-execution-vulnerability-in-globalprotect-cve-2024-3400/

^[viii] https://www.volexity.com/blog/2024/05/15/detecting-compromise-of-cve-2024-3400-on-palo-alto-networks-globalprotect-devices/

‍

Written by

Adam Potter

Senior Cyber Analyst

Inside the SOC

Written by

Adam Potter

Senior Cyber Analyst

•

October 21, 2026

Tara Gould

Malware Research Lead

•

October 22, 2026

Nathaniel Jones

SVP, Global Threat Intelligence

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AI

October 23, 2026

PHP Vulnerabilities, ARTEX and the Growing Role of AI in Cyber Attacks

The disclosure of CVE-2026-109618 and CVE-2026-109619 placed immediate pressure on organizations running PHP. Both vulnerabilities can allow unauthenticated remote code execution, including in products and appliances where PHP is embedded, and updates depend on third-party vendors [1].

Early guidance focused on patching internet-facing systems and monitoring for behavior associated with successful exploitation, including unexpected command execution, Webroot changes and outbound connections from PHP servers.

Darktrace observed widespread scanning across monitored environments involving hundreds of distinct source IP addresses that public reporting has associated with activity targeting the PHP vulnerabilities. The volume of scanning was significantly greater than the evidence available for successful exploitation. For analysts, that distinction is important. Scanning demonstrates interest in exposed infrastructure; establishing compromise requires evidence of subsequent behavior on the targeted system, including command execution, file modification or unexpected outbound communication.

PHP is not an isolated example. Imperva subsequently demonstrated remote code execution against a vulnerable Node.js build through malicious Brotli data, reproducing execution through compressed HTTP responses and configured request parsers. The same inputs were rejected by the patched runtime. Imperva linked its Node2Shell and PHP2Shell research to the increasing speed of vulnerability discovery and exploit development. [2]

While Darktrace has not observed direct evidence of CVE-2026-109618 or CVE-2026-109619 exploitation, on October 21^st we observed a six-month high in detections consistent with zero-day exploitation activity, representing a 64% increase compared to historic data. This increase has not been confirmed as related to the PHP vulnerabilities. Darktrace’s behavioral approach is designed to detect anomalous activity without relying solely on rules written for a specific exploit. Comparable increases were observed around the Log4j disclosure and other globally significant vulnerability disclosures.

AI-assisted vulnerability research

The PHP disclosure arrives as AI systems have become more capable at vulnerability research [3]. OpenAI launched Daybreak in June 2026, combining GPT-5.5-Cyber, Codex Security and partnerships with security researchers and software maintainers. OpenAI says its models have been applied to discovering and generating patches for vulnerabilities in browsers, network infrastructure, FreeBSD and the Linux kernel. Darktrace joined the OpenAI Daybreak Cyber Partner Program in June to explore how OpenAI's cyber capabilities could be integrated within Darktrace products and services [4][10].

OpenAI reported in August that Codex Security Cloud had analyzed more than 30 million commits across more than 30,000 codebases. According to OpenAI, Cloudflare uses models accessed through Daybreak to investigate customer-authorized code and combines findings with live traffic and security signals to assess whether affected routes are active or receiving suspicious traffic, with proposed mitigations and patches remaining subject to customer review [5][6].

Faster discovery still leaves defenders with the work of locating affected software, testing fixes and completing remediation. The PHP case shows that gap clearly, particularly when vulnerable runtimes are embedded inside other products.

Enterprise agents as an attack surface

Darktrace Signal Labs has also examined how a trusted enterprise agent could be abused after an attacker has already obtained access to an environment. In a controlled simulation, researchers demonstrated that, in that post-compromise scenario, the Claude platform’s memory stores feature could be abused to establish a live C2 channel between an external operator and internal Claude Code agents. The external operator was able to dynamically assign network discovery, credential access, and data exfiltration tasks to the agents via this C2 channel. Detection of the agents’ activities relied on the correlation of process creation and network traffic activity from agent harness processes, alongside behavioral analysis to identify deviations from expected behavior. Darktrace has not claimed it observed this memory-store command-and-control technique in operational attacks [7].

AI-assisted attacks in Japan

Evidence from Japan indicates AI being used for vulnerability scanning and attacks outside controlled research. On 21 October, JPCERT/CC published findings from an investigation into recent unauthorized access incidents affecting organizations in Japan. JPCERT/CC states that it confirmed attackers had used AI to scan for known vulnerabilities and conduct attacks through APIs. According to JPCERT/CC, its examination of attack infrastructure found evidence of Android application analysis, API discovery and testing for weaknesses including server-side request forgery, and the infrastructure also targeted services intended for users outside Japan [8].

JPCERT/CC does not identify ARTEX by name in that publication. The named ARTEX attribution comes from separate investigations, including Microsoft's analysis of Storm-0940.

AI in offensive operations

Microsoft describes ARTEX as an open-source, multi-agent penetration-testing system capable of automating activity with limited human interaction. Microsoft reported that Storm-0940, which it assesses to be a China state-aligned actor active since 2021, used ARTEX against hundreds of organizations, and Microsoft attributed the ConnectWise supply-chain compromise [9].

Microsoft observed ARTEX accelerating scanning, reconnaissance, password spraying and OAuth application abuse. Several ARTEX-driven operations reached privileged cloud access less than three hours after initial targeting. Following the ConnectWise compromise, Storm-0940 attempted to authenticate to more than 2,250 tenants at rates of up to 2,900 attempts per minute [9].

Darktrace Threat Research found activity in its own telemetry involving infrastructure later associated with the campaign. On 13 October, Darktrace recorded failed authentication activity involving 144.172.64[.]128, one of the addresses Microsoft later identified as performing sign-ins and Microsoft Graph activity in ConnectWise customer environments. Darktrace analysis identified 65 failed authentication attempts in around 20 minutes.

*Figure 1: Darktrace Advanced Search showing failed authentication events involving 144.172.64[.]128 on 13 October 2026, (customer-identifying information redacted).*

The telemetry establishes repeated failed authentication from infrastructure later associated with the campaign. It does not establish ARTEX execution or successful compromise. At the time of observation, Microsoft's later attribution was unavailable. Analysts therefore had the behavior itself: repeated failed authentication from the same source over a short period, together with any related identity, application or network activity inside the environment.

Microsoft documented a wider password-spray campaign against 327 accounts across eight organizations between 10 and 14 October. It recorded more than 151,000 failed logons, including more than 111,000 on 11 October, but no successful authentication, token issuance, Graph call or directory change from that campaign. Microsoft also reported that most Storm-0940 targeting was unsuccessful [9].

Where the actor progressed beyond authentication, Microsoft observed service-principal sign-ins followed by Graph and directory operations. Activity included reconnaissance, application credential changes and attempts to modify privileges. Microsoft recommends correlating service-principal sign-ins with Graph activity, directory changes and resource access [9].

Behavior before attribution

The PHP and ARTEX cases begin with different information. PHP defenders knew which software required urgent remediation but initially had limited information about exploitation. Process execution, file modification and outbound communication could still provide evidence of compromise.

The PHP and ARTEX observations also illustrate the difference between volume and impact. Darktrace observed broad scanning around the PHP disclosure, while the ARTEX-associated infrastructure produced concentrated authentication activity. Neither observation independently demonstrates compromise and in each case examining the behavior that follows the initial interaction is the investigative clue.

The Darktrace ARTEX-related observation began with authentication behavior. Microsoft publicly attributed the source infrastructure eight days later. That attribution added context, but it did not change what had originally been recorded in the environment.

AI is now present at several points in this cycle. Daybreak applies models to vulnerability discovery and remediation. Darktrace Signal Labs demonstrated how a trusted enterprise agent could be co-opted in a controlled environment. JPCERT/CC confirmed AI-assisted vulnerability scanning and API attacks in recent incidents. Microsoft documented ARTEX being used by Storm-0940 in active operations [4][7][8][9].

Microsoft's findings also show why scale and impact must be assessed separately. One ARTEX-associated campaign produced more than 151,000 failed logons without successful access, while other operations reached privileged cloud access in less than three hours. Security teams have to distinguish high-volume attempts from activity that progresses through authentication, privilege changes and resource access.

The evidence required to assess these cases remains familiar: authentication records, process execution, application and directory changes, file activity and network communication. AI can increase the rate at which those behaviors occur. Defenders still need to establish their sequence and impact.

Credit to Emma Foulger, Sam Lister, Dylan Evans, Moishe Tajtelbaum

References

[1] NCSC-NL, php2shell vulnerabilities (CVE-2026-109618, CVE-2026-109619), 21 October 2026. https://github.com/NCSC-NL/php2shell

[2] Imperva, https://www.imperva.com/blog/node2shell-from-brotli-data-to-remote-code-execution/

[3] Cyber Security Agency of Singapore, https://www.csa.gov.sg/alerts-and-advisories/alerts/al-2026-151/

[4] OpenAI, Daybreak: Tools for securing every organization in the world, 22 June 2026. https://openai.com/index/daybreak-securing-the-world/

[5] OpenAI Developers, Scaling cyber defenders with Daybreak, 21 August 2026. https://developers.openai.com/blog/scaling-cyber-defenders-with-daybreak

[6] OpenAI, Cloudflare links vulnerability discovery to defense with OpenAI, 21 October 2026. https://openai.com/index/cloudflare-cyber/

[7] Darktrace Signal Labs, Shared Memories as a Covert C2 Channel, draft research supplied for review.

[8] JPCERT/CC, Recent AI-Assisted Cyber Attacks in Japan: Investigating Attack Infrastructure and Techniques, 21 October 2026. https://blogs.jpcert.or.jp/en/2026/10/web_api_attacks.html

[9] Microsoft Security Research and Microsoft Threat Intelligence, AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise, 21 October 2026. https://www.microsoft.com/en-us/security/blog/2026/10/21/storm-0940-uses-artex-agentic-ai-connectwise-supply-chain-compromise/

[10] Darktrace, Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations, 22 June 2026. https://www.darktrace.com/blog/advancing-the-use-of-frontier-ai-in-cybersecurity-darktrace-joins-the-openai-daybreak-cyber-partner-program-to-explore-defensive-ai-integrations

About the author

Nathaniel Jones

SVP, Global Threat Intelligence

Blog

OT

October 23, 2026

Beyond the Fridge: Exposed Industrial Control Systems

Introduction

In October 2026, hundreds of supermarkets across France were reportedly affected by a cyberattack targeting their refrigeration systems. According to public reports, the attackers gained access to internet-connected controllers and modified their settings, forcing refrigeration units into high and extremely low temperatures and causing significant disruption. Total losses have been reported to reach several million euros.

The incident drew attention to the security of industrial refrigeration systems, particularly older devices with exposed management interfaces and known vulnerabilities.

The exact details of how the attackers gained access have not been confirmed, however it is speculated that the public-facing web server could be accessed with the username “admin” and password being the current date. Researchers from Claroty’s Team82 identified a vulnerability in Danfoss AK-SM 8xxA Series in 2025, “CVE-2025-41450", allowing for administrative access using a time-based “code-of-the-day" for authentication [1].

Default credentials, outdated firmware and publicly accessible administrative interfaces have been an issue for years. And yet these systems continue to appear in internet scans and require little sophistication to gain admin access. A simple search of internet-connected devices shows a range of exposed devices from Programmable Logic Controllers (PLCs) and remote gateways, to, yes, more fridges.

Darktrace malware researchers identified hundreds of IoT and OT devices publicly exposed on the internet, many of which are configured with no authentication, weak credentials, or default credentials. As shown in Figure 1, an exposed Copeland XWEB web interface, used to manage Dixell equipment, including refrigerators, can provide the ability to remotely control connected devices.

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Figure 1: Copeland XWEB interface used to control systems including refrigerators and air conditioning.

However, it’s not only fridges; web management interfaces for industrial controls are also publicly accessible, often protected only by weak credentials. Figure 2 shows an example of a publicly exposed WAGO interface. The WAGO management interface is a configuration and administration panel for controllers. Through it, a logged-in user can view device details and diagnostics, manage user accounts and passwords, update firmware, back up or restore the full configuration, set the clock, and manage SD card storage. While it typically doesn’t manage switch outputs or read live process values, someone with access could still disrupt operation by changing network or Modbus settings, loading bad firmware or configurations, or by resetting passwords and enabling insecure services.

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The issue extends beyond management interfaces. During our research, we identified publicly accessible web interfaces belonging to PLCs, including Rockwell Automation Allen-Bradley devices. In each case, the risk arises from how the devices have been deployed and configured, not from a flaw in the vendors’ products.

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Figure 3: Web interface for Allen-Bradley CompactLogix controller.

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Figure 3 shows the web interface of an Allen-Bradley CompactLogix controller. Unlike the previous examples, this interface provides a view into the controller's operation, including running tasks, network connections, memory usage and diagnostic information. Rockwell Automation's CompactLogix controllers are commonly used to automate industrial processes, from manufacturing and packaging lines to water treatment and material handling systems. Depending on their configuration, these controllers can operate motors, pumps, valves and other physical equipment.

Although in this example the interface shown does not provide direct control over the PLC's logic, it exposes information about the model, firmware, network layout, and connections, which is useful reconnaissance for a bigger attack.

The concern is not necessarily that every exposed interface allows an attacker to immediately manipulate industrial processes. However, these devices are accessible from the public internet in the first place, providing information about systems that may be responsible for controlling physical equipment.

The attacks on refrigeration systems in France highlight the consequences of poorly secured industrial devices. As demonstrated throughout this research, publicly accessible management interfaces are not limited to refrigeration controllers but extend to PLCs and other systems used to manage industrial equipment.

Finding these devices requires little effort. Internet scanners make it possible to identify publicly exposed interfaces quickly, and in some cases these interfaces rely on publicly documented default credentials or have no authentication at all. As attackers increasingly use AI tools, such attacks may be identified and carried out faster and at a greater scale.

The consequences of this access depend on the device and the functionality exposed. While some interfaces provide little more than diagnostic information, others allow users to modify configurations, disrupt communications, or change settings that affect physical equipment. In industrial environments, this can result in operational downtime, damaged equipment and financial losses.

Weak passwords, default credentials and exposed administrative interfaces are not new security issues. Yet their continued presence on internet-connected industrial systems demonstrates how easily preventable weaknesses can leave physical operations vulnerable to attack.

Exposed devices often come from third-party suppliers and contractors, who frequently install refrigeration controllers, gateways and PLCs without the organization ever being aware they are connected to the internet. Asset visibility remains an important foundation, but organizations should assume that some unknown assets and services will exist. Effective security therefore also depends on continuous monitoring, anomaly detection, network segmentation and rapid response, so that risky exposure and unusual activity can be identified and addressed before it becomes disruptive.

Darktrace helps security teams gain visibility across IT and OT and respond to risks: https://www.darktrace.com/products/operational-technology.

Copeland, Dixell, WAGO, Rockwell Automation, Allen-Bradley and CompactLogix are trademarks of their respective owners. Their use here is for identification only and does not imply any affiliation with or endorsement by those owners.

References

1. https://claroty.com/team82/research/freeze-the-controller-defrost-the-food-uncovering-vulnerabilities-in-danfoss-refrigeration-controllers

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About the author

Tara Gould

Malware Research Lead

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Check out this article by Darktrace: Post-Exploitation Activities on PAN-OS Devices: A Network-Based Analysis

Post-Exploitation Activities on PAN-OS Devices: A Network-Based Analysis

Introduction

CVE-2024-3400

Campaign Analysis

1. Exploit Validation and Further Vulnerability Enumeration

2. Command and Payload Transmission

a) Shell Script Retrieval

b) Executable File Retrieval

3. Configuration Data Exfiltration and Unusual HTTP POST Activity

4. Ongoing C2 and Miscellaneous Activity

Conclusion

Appendices ‍

Pre-CVE-Release IoCs

Indicators of Compromise

Darktrace Model Alert Coverage

MITRE ATTACK Mapping

References

Beyond the Fridge: Exposed Industrial Control Systems

PHP Vulnerabilities, ARTEX and the Growing Role of AI in Cyber Attacks

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October 23, 2026

Beyond the Fridge: Exposed Industrial Control Systems

Introduction

References