Uncover the signs of insider threats with Darktrace’s expert analysis. Learn how to protect your organization from internal security breaches.
What is an Insider Threat?
Insider threat refers to cyber threats which originates from within an organization. Insider threats can come in the form of employees, vendors, contractors, or anyone with access to sensitive systems, data, or information. The cyber risk posed by insiders can be grouped into malicious insiders, such as rogue or disgruntled employees, or accidental, such as a well-meaning employee inadvertently leaking data or introducing a security flaw.
Insider threats are a reality for all organizations, across all industry verticals around the world. However, this Darktrace short read will focus on Operational Technology (OT), such as those within Critical Infrastructure, and the impact insiders can have on safety-critical systems, the environment, and human life.
Malicious v Non-Malicious Insider Threat in OT
There are generally two types of insider threats: malicious and non-malicious, or accidental. For organizations managing OT, both types originate from personnel who have legitimate privileged access to OT networks and have insider knowledge of assets, configurations, locations, security controls, or vulnerabilities. Of increasing concern to security teams, these personnel can also include external contractors, such as vendors or consultants, who require high levels of access to perform their role.
The 2001 sewage spill in Maroochy Shire, Australia, was the first high-profile example of a malicious insider manipulating control systems to impact OT. More recently, the 2021 incident at the Oldsmar Water Facility in Florida was the result of poor cyber security practices. While there is much speculation as to the exact cause of the incident, the root cause appears to have been human error which resulted in changes to intended chemical content levels in drinking water.
Insider Threats: Challenges and Solutions
The biggest concern for cyber security managers in Critical Infrastructure and OT networks is the threat posed by those on the inside. Compliance breaches, poor cyber hygiene, and disgruntled or rogue employees all pose a greater everyday threat to these systems than APTs or the latest zero day.
Insider threats are hard to catch. They rarely use attack tools or malware to achieve their goal, rendering signature-based threat detection useless. Instead, they leverage their legitimate access to make changes to native functionality. Rules-based threat detection can be used to prevent certain actions, but playbooks are limited to the imagination of the person implementing them and the time they have to create and maintain them.
Anomaly-Based Threat Detection
Anomaly-based threat detection is uniquely positioned to detect insider threats. Both accidental and malicious disruption may use legitimate privileged access to target Purdue Level 1 and 2 controllers and programmers to alter operations. The actor will alter the routine functionality of the process control environment, which can be detected and alerted by a security tool which understands normal and can spot deviations.
Darktrace/OT vs Insider Threats
Powered by scalable, Self-Learning AI, Darktrace/OT uses anomaly-based detection to detect unpredictable attacks in their earliest stages. By learning the normal ‘patterns of life’ for every device and operator in an industrial environment, Darktrace/OT detects known and unknown threats including zero-day exploits, supply chain attacks, ransomware, pre-existing infections, and insider threat.
Using raw digital data from an OT network to understand the normal pattern of life, Darktrace/OT does not need any data or threat feeds from external sources to perform anomaly-based threat detection. The approach is perfectly suited to spotting and stopping the threat posed by malicious and non-malicious insiders.
Attack Case Study: Spotting Insider Threats with Self-Learning AI
In the real-world example below, Darktrace/OT detected a subtle deviation from normal behavior when a reprogram command was sent by an engineering workstation to a PLC controlling a pump, an action an insider threat with legitimized access to OT systems would take to alter the physical process without any malware involved. In this instance, AI Analyst, Darktrace’s investigation tool that triages events to reveal the full security incident, detected the event as unusual based on multiple metrics including the source of the command, the destination device, the time of the activity, and the command itself.
As a result, AI Analyst created a complete security incident, with a natural language summary, the technical details of the activity, and an investigation process explaining how it came to its conclusion. By leveraging Explainable AI, a security team can quickly triage and escalate Darktrace incidents in real time before it becomes disruptive, and even when performed by a trusted insider.
Figure 1: AI Analyst Incident reporting an unusual reprogram command using the MODBUS protocol. The incident includes a plain English summary, relevant technical information, and the investigation process used by the AI.
Figure 2: The Darktrace Threat Visualizer allows security analysts and OT engineers to visualize and replay incidents in real time.
Credit to Daniel Simonds for his contribution to this blog.
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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
Oakley Cox
Director of Product
Oakley is a Product Manager within the Darktrace R&D team. He collaborates with global customers, including all critical infrastructure sectors and Government agencies, to ensure Darktrace/OT remains the first in class solution for OT Cyber Security. He draws on 7 years’ experience as a Cyber Security Consultant to organizations across EMEA, APAC and ANZ. His research into cyber-physical security has been published by Cyber Security journals and by CISA. Oakley has a Doctorate (PhD) from the University of Oxford.
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.
Cloud Security: Addressing Common CISO Challenges with Advanced Solutions
Cloud adoption is a cornerstone of modern business with its unmatched potential for scalability, cost efficiency, flexibility, and net-zero targets around sustainability. However, as organizations migrate more workloads, applications, and sensitive data to the cloud it introduces more complex challenges for CISO’s. Let’s dive into the most pressing issues keeping them up at night—and how Darktrace / CLOUD provides a solution for each.
1. Misconfigurations: The Silent Saboteur
Misconfigurations remain the leading cause of cloud-based data breaches. In 2023 alone over 80% of data breaches involved data stored in the cloud.1 Think open storage buckets or overly permissive permissions; seemingly minor errors that are easily missed and can snowball into major disasters. The fallout of breaches can be costly—both financially and reputationally.
How Darktrace / CLOUD Helps:
Darktrace / CLOUD continuously monitors your cloud asset configurations, learning your environment and using these insights to flag potential misconfigurations. New scans are triggered when changes take place, then grouped and prioritised intelligently, giving you an evolving and prioritised view of vulnerabilities, best practice and mitigation strategies.
2. Hybrid Environments: The Migration Maze
Many organizations are migrating to the cloud, but hybrid setups (where workloads span both on-premises and cloud environments) create unique challenges and visibility gaps which significantly increase complexity. More traditional and most cloud native security tooling struggles to provide adequate monitoring for these setups.
How Darktrace / CLOUD Helps:
Provides the ability to monitor runtime activity for both on-premises and cloud workloads within the same user interface. By leveraging the right AI solution across this diverse data set, we understand the behaviour of your on-premises workloads and how they interact with cloud systems, spotting unusual connectivity or data flow activity during and after the migration process.
This unified visibility enables proactive detection of anomalies, ensures seamless monitoring across hybrid environments, and provides actionable insights to mitigate risks during and after the migration process.
3. Securing Productivity Suites: The Last Mile
Cloud productivity suites like Microsoft 365 (M365) are essential for modern businesses and are often the first step for an organization on a journey to Infrastructure as a Service (IaaS) or Platform as a Service (PaaS) use cases. They also represent a prime target for attackers. Consider a scenario where an attacker gains access to an M365 account, and proceeds to; access sensitive emails, downloading files from SharePoint, and impersonating the user to send phishing emails to internal employees and external partners. Without a system to detect these behaviours, the attack may go unnoticed until significant damage is done.
How Darktrace helps:
Darktrace’s Active AI platform integrates with M365 and establishes an understanding of normal business activity, enabling the detection of abnormalities across its suite including Email, SharePoint and Teams. By identifying subtle deviations in behaviour, such as:
• Unusual file accesses
• Anomalous login attempts from unexpected locations or devices.
• Suspicious email forwarding rules created by compromised accounts.
Darktrace’s Autonomous Response can act precisely to block malicious actions, by disabling compromised accounts and containing threats before they escalate. Precise actions also ensure that critical business operations are maintained even when a response is triggered.
4. Agent Fatigue: The Visibility Struggle
To secure cloud environments, visibility is critical. If you don’t know what’s there, how can you secure it? Many solutions require agents to be deployed on every server, workload, and endpoint. But managing and deploying agents across sprawling hybrid environments can be both complex and time-consuming when following change controls, and especially as cloud resources scale dynamically.
How Darktrace / CLOUD Helps:
Darktrace reduces or eliminates the need for widespread agent deployment. Its agentless by default, integrating directly with cloud environments and providing instant visibility without the operational headache. Darktrace ensures coverage with minimal friction. By intelligently graphing the relationships between assets and logically grouping your deployed Cloud resources, you are equipped with real-time visibility to quickly understand and protect your environment.
So why Darktrace / CLOUD?
Darktrace’s Self-Learning AI redefines cloud security by adapting to your unique environment, detecting threats as they emerge, and responding in real-time. From spotting misconfigurations to protecting productivity suites and securing hybrid environments. Darktrace / CLOUD simplifies cloud security challenges without adding operational burdens.
From Chaos to Clarity
Cloud security doesn’t have to be a game of endless whack-a-mole. With Darktrace / CLOUD, CISOs can achieve the visibility, control, and proactive protection they need to navigate today’s complex cloud ecosystems confidently.
![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)
