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July 9, 2019

Insights on Shamoon 3 Data-Wiping Malware

Gain insights into Shamoon 3 and learn how to protect your organization from its destructive capabilities.
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
Max Heinemeyer
Global Field CISO
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09
Jul 2019

Responsible for some of the “most damaging cyber-attacks in history” since 2012, the Shamoon malware wipes compromised hard drives and overwrites key system processes, intending to render infected machines unusable. During a trial period in the network of a global company, Darktrace observed a Shamoon-powered cyber-attack on December 10, 2018 — when several Middle Eastern firms were impacted by a new variant of the malware.

While there has been detailed reporting on the malware files and wiper modules that these latest Shamoon attacks employed, the complete cyber kill chain involved remains poorly understood, while the intrusions that led to the malware’s eventual “detonation” last December has not received nearly as much coverage. As a consequence, this blog post will focus on the insights that Darktrace’s cyber AI generated regarding (a) the activity of the infected devices during the “detonation” and (b) the indicators of compromise that most likely represent lateral movement activity during the weeks prior.

A high-level overview of major events leading up to the detonation on December 10th.

In the following, we will dive into that timeline more deeply in reverse chronological order, going back in time to trace the origins of the attack. Let’s begin with zero hour.

December 10: 42 devices “detonate”

A bird's-eye perspective of how Darktrace identified the alerts in December 2018.

What immediately strikes the analyst’s eye is the fact that a large accumulation of alerts, indicated by the red rectangle above, took place on December 10, followed by complete network silence over the subsequent four days.

These highlighted alerts represent Darktrace’s detection of unusual network scans on remote port 445 that were conducted by 42 infected devices. These devices proceeded to scan more machines — none of which were among those already infected. Such behavior indicates that the compromised devices started scanning and were wiped independently from each other, instead of conducting worming-style activity during the detonation of the malware. The initial scanning device started its scan at 12:56 p.m. UTC, while the last scanning device started its scan at 2:07 p.m. UTC.

Not only was this activity readily apparent from the bird’s-eye perspective shown above, the detonating devices also created the highest-priority Darktrace alerts over a several day period: “Device / Network Scan” and “Device / Expanded Network Scan”:

Moreover, when investigating “Devices — Overall Score,” the detonating devices rank as the most critical assets for the time period December 8–11:

Darktrace AI generated all of the above alerts because they represented significant anomalies from the normal ‘pattern of life’ that the AI had learned for each user and device on the company’s network. Crucially, none of the alerts were the product of predefined ‘rules and signatures’ — the mechanism that conventional security tools rely on to detect cyber-threats. Rather, the AI revealed the activity because the scans were unusual for the devices given their precise nature and timing, demonstrating the necessity of the such a nuanced approach in catching elusive threats like Shamoon. Of further importance is that the company’s network consists of around 15,000 devices, meaning that a rules-based approach without the ability to prioritize the most serious threats would have drowned out the Shamoon alerts in noise.

Now that we’ve seen how cyber AI sounded the alarms during the detonation itself, let’s investigate the various indicators of suspicious lateral movement that precipitated the events of December 10. Most of this activity happened in brief bursts, each of which could have been spotted and remediated if Darktrace had been closely monitored.

November 19: Unusual Remote Powershell Usage (WinRM)

One such burst of unusual activity occurred on November 19, when Darktrace detected 14 devices — desktops and servers alike — that all successfully used the WinRM protocol. None of these devices had previously used WinRM, which is also unusual for the organization’s environment as a whole. Conversely, Remote PowerShell is quite often abused in intrusions during lateral movement. The devices involved did not classify as traditional administrative devices, making their use of WinRM even more suspicious.

Note the clustering of the WinRM activity as indicated by the timestamp on the left.

October 29–31: Scanning, Unusual PsExec & RDP Brute Forcing

Another burst of likely lateral movement occurred between October 29 and 31, when two servers were seen using PsExec in an unusual fashion. No PsExec activity had been observed in the network before or after these detections, prompting Darktrace to flag the behavior. One of the servers conducted an ICMP Ping sweep shortly before the lateral movement. Not only did both servers start using PsExec on the same day, they also used SMBv1 — which, again, was very unusual for the network.

Most legitimate administrative activity involving PsExec these days uses SMBv2. The graphic below shows several Darktrace alerts on one of the involved servers — take note of the chronology of detections at the bottom of the graphic. This clearly reads like an attacker’s diary: ICMP scan, SMBv1 usage, and unusual PsExec usage, followed by new remote service controls. This server was among the top five highest ranking devices during the analyzed time period and was easy to identify.

Following the PsExec use, the servers also started an anomalous amount of remote services via the srvsvc and svcctl pipes over SMB. They did so by starting services on remote devices with which they usually did not communicate — using SMBv1, of course. Some of the attempted communication failed due to access violation and access permission errors. Both are often seen during malicious lateral movement.

Additional context around the SMBv1 and remote srvsvc pipe activity. Note the access failure.

Thanks to Darktrace’s deep packet inspection, we can see exactly what happened on the application layer. Darktrace highlights any unusual or new activity in italics below the connections — we can easily see that the SMB activity is not only unusual because of SMBv1 being used, but also because this server had never used this type of SMB activity remotely to those particular destinations before. We can also observe remote access to the winreg pipe — likely indicating more lateral movement and persistence mechanisms being established.

The other server conducted some targeted address scanning on the network on October 29, employing typical lateral movement ports 135, 139 and 445:

Another device was observed to conduct RDP brute forcing on October 29 around the same time as the above address scan. The desktop made an unusual amount of RDP connections to another internal server.

A clear plateau in increased internal connections (blue) can be seen. Every colored dot on top represents an RDP brute force detection. This was again a clear-cut detection not drowned in other noise — these were the only RDP brute force detections for a several-month monitoring time window.

October 9–11: Unusual Credential Usage

Darktrace identifies the unusual use of credentials — for instance, if administrative credentials are used on client device on which they are not commonly used. This might indicate lateral movement where service accounts or local admin accounts have been compromised.

Darktrace identified another cluster of activity that is likely representing lateral movement, this time involving unusual credential usage. Between October 9 and 11, Darktrace identified 17 cases of new administrative credentials being used on client devices. While new administrative credentials were being used from time to time on devices as part of normal administrative activity, this strong clustering of unusual admin credential usage was outstanding. Additionally, Darktrace also identified the source of some of the credentials being used as unusual.

Conclusion

Having observed a live Shamoon infection within Darktrace, there are a few key takeaways. While the actual detonation on December 10 was automated, the intrusion that built up to it was most likely manual. The fact that all detonating devices started their malicious activity roughly at the same time — without scanning each other — indicates that the payload went off based on a trigger like a scheduled task. This is in line with other reporting on Shamoon 3.

In the weeks leading up to December 10, there were various significant signs of lateral movement that occurred in disparate bursts — indicating a ‘low-and-slow’ manual intrusion.

The adversaries used classic lateral movement techniques like RDP brute forcing, PsExec, WinRM usage, and the abuse of stolen administrative credentials.

While the organization in question had a robust security posture, an attacker only needs to exploit one vulnerability to bring down an entire system. During the lifecycle of the attack, the Darktrace Enterprise Immune System identified the threatening activity in real time and provided numerous suggested actions that could have prevented the Shamoon attack at various stages. However, human action was not taken, while the organization had yet to activate Antigena, Darktrace’s autonomous response solution, which could have acted in the security team’s stead.

Despite having limited scope during the trial period, the Enterprise Immune System was able to detect the lateral movement and detonation of the payload, which was indicative of the malicious Shamoon virus activity. A junior analyst could have easily identified the activity, as high-severity alerts were consistently generated, and the likely infected devices were at the top of the suspicious devices list.

Darktrace Antigena would have prevented the movement responsible for the spread of the virus, while also sending high-severity alerts to the security team to investigate the activity. Even the scanning on port 445 from the detonating devices would have been shut down, as it presented a significant deviation from the known behavior of all scanning devices, which would have further limited the virus’s spread, and ultimately, spared the company and its devices from attack.

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
Max Heinemeyer
Global Field CISO

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July 9, 2025

Defending the Cloud: Stopping Cyber Threats in Azure and AWS with Darktrace

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Real-world intrusions across Azure and AWS

As organizations pursue greater scalability and flexibility, cloud platforms like Microsoft Azure and Amazon Web Services (AWS) have become essential for enabling remote operations and digitalizing corporate environments. However, this shift introduces a new set of security risks, including expanding attack surfaces, misconfigurations, and compromised credentials frequently exploited by threat actors.

This blog dives into three instances of compromise within a Darktrace customer’s Azure and AWS environment which Darktrace.

  1. The first incident took place in early 2024 and involved an attacker compromising a legitimate user account to gain unauthorized access to a customer’s Azure environment.
  2. The other two incidents, taking place in February and March 2025, targeted AWS environments. In these cases, threat actors exfiltrated corporate data, and in one instance, was able to detonate ransomware in a customer’s environment.

Case 1 - Microsoft Azure

Simplified timeline of the attack on a customer’s Azure environment.
Figure 1: Simplified timeline of the attack on a customer’s Azure environment.

In early 2024, Darktrace identified a cloud compromise on the Azure cloud environment of a customer in the Europe, the Middle East and Africa (EMEA) region.

Initial access

In this case, a threat actor gained access to the customer’s cloud environment after stealing access tokens and creating a rogue virtual machine (VM). The malicious actor was found to have stolen access tokens belonging to a third-party external consultant’s account after downloading cracked software.

With these stolen tokens, the attacker was able to authenticate to the customer’s Azure environment and successfully modified a security rule to allow inbound SSH traffic from a specific IP range (i.e., securityRules/AllowCidrBlockSSHInbound). This was likely performed to ensure persistent access to internal cloud resources.

Detection and investigation of the threat

Darktrace / IDENTITY recognized that this activity was highly unusual, triggering the “Repeated Unusual SaaS Resource Creation” alert.

Cyber AI Analyst launched an autonomous investigation into additional suspicious cloud activities occurring around the same time from the same unusual location, correlating the individual events into a broader account hijack incident.

Cyber AI Analyst’s investigation into unusual cloud activity performed by the compromised account.
Figure 2: Cyber AI Analyst’s investigation into unusual cloud activity performed by the compromised account.
Figure 2: Surrounding resource creation events highlighted by Cyber AI Analyst.
Figure 3: Surrounding resource creation events highlighted by Cyber AI Analyst.
Figure 4: Surrounding resource creation events highlighted by Cyber AI Analyst.

“Create resource service limit” events typically indicate the creation or modification of service limits (i.e., quotas) for a specific Azure resource type within a region. Meanwhile, “Registers the Capacity Resource Provider” events refer to the registration of the Microsoft Capacity resource provider within an Azure subscription, responsible for managing capacity-related resources, particularly those related to reservations and service limits. These events suggest that the threat actor was looking to create new cloud resources within the environment.

Around ten minutes later, Darktrace detected the threat actor creating or modifying an Azure disk associated with a virtual machine (VM), suggesting an attempt to create a rogue VM within the environment.

Threat actors can leverage such rogue VMs to hijack computing resources (e.g., by running cryptomining malware), maintain persistent access, move laterally within the cloud environment, communicate with command-and-control (C2) infrastructure, and stealthily deliver and deploy malware.

Persistence

Several weeks later, the compromised account was observed sending an invitation to collaborate to an external free mail (Google Mail) address.

Darktrace deemed this activity as highly anomalous, triggering a compliance alert for the customer to review and investigate further.

The next day, the threat actor further registered new multi-factor authentication (MFA) information. These actions were likely intended to maintain access to the compromised user account. The customer later confirmed this activity by reviewing the corresponding event logs within Darktrace.

Case 2 – Amazon Web Services

Simplified timeline of the attack on a customer’s AWS environment
Figure 5: Simplified timeline of the attack on a customer’s AWS environment

In February 2025, another cloud-based compromised was observed on a UK-based customer subscribed to Darktrace’s Managed Detection and Response (MDR) service.

How the attacker gained access

The threat actor was observed leveraging likely previously compromised credential to access several AWS instances within customer’s Private Cloud environment and collecting and exfiltrating data, likely with the intention of deploying ransomware and holding the data for ransom.

Darktrace alerting to malicious activity

This observed activity triggered a number of alerts in Darktrace, including several high-priority Enhanced Monitoring alerts, which were promptly investigated by Darktrace’s Security Operations Centre (SOC) and raised to the customer’s security team.

The earliest signs of attack observed by Darktrace involved the use of two likely compromised credentials to connect to the customer’s Virtual Private Network (VPN) environment.

Internal reconnaissance

Once inside, the threat actor performed internal reconnaissance activities and staged the Rclone tool “ProgramData\rclone-v1.69.0-windows-amd64.zip”, a command-line program to sync files and directories to and from different cloud storage providers, to an AWS instance whose hostname is associated with a public key infrastructure (PKI) service.

The threat actor was further observed accessing and downloading multiple files hosted on an AWS file server instance, notably finance and investment-related files. This likely represented data gathering prior to exfiltration.

Shortly after, the PKI-related EC2 instance started making SSH connections with the Rclone SSH client “SSH-2.0-rclone/v1.69.0” to a RockHoster Virtual Private Server (VPS) endpoint (193.242.184[.]178), suggesting the threat actor was exfiltrating the gathered data using the Rclone utility they had previously installed. The PKI instance continued to make repeated SSH connections attempts to transfer data to this external destination.

Darktrace’s Autonomous Response

In response to this activity, Darktrace’s Autonomous Response capability intervened, blocking unusual external connectivity to the C2 server via SSH, effectively stopping the exfiltration of data.

This activity was further investigated by Darktrace’s SOC analysts as part of the MDR service. The team elected to extend the autonomously applied actions to ensure the compromise remained contained until the customer could fully remediate the incident.

Continued reconissance

Around the same time, the threat actor continued to conduct network scans using the Nmap tool, operating from both a separate AWS domain controller instance and a newly joined device on the network. These actions were accompanied by further internal data gathering activities, with around 5 GB of data downloaded from an AWS file server.

The two devices involved in reconnaissance activities were investigated and actioned by Darktrace SOC analysts after additional Enhanced Monitoring alerts had triggered.

Lateral movement attempts via RDP connections

Unusual internal RDP connections to a likely AWS printer instance indicated that the threat actor was looking to strengthen their foothold within the environment and/or attempting to pivot to other devices, likely in response to being hindered by Autonomous Response actions.

This triggered multiple scanning, internal data transfer and unusual RDP alerts in Darktrace, as well as additional Autonomous Response actions to block the suspicious activity.

Suspicious outbound SSH communication to known threat infrastructure

Darktrace subsequently observed the AWS printer instance initiating SSH communication with a rare external endpoint associated with the web hosting and VPS provider Host Department (67.217.57[.]252), suggesting that the threat actor was attempting to exfiltrate data to an alternative endpoint after connections to the original destination had been blocked.

Further investigation using open-source intelligence (OSINT) revealed that this IP address had previously been observed in connection with SSH-based data exfiltration activity during an Akira ransomware intrusion [1].

Once again, connections to this IP were blocked by Darktrace’s Autonomous Response and subsequently these blocks were extended by Darktrace’s SOC team.

The above behavior generated multiple Enhanced Monitoring alerts that were investigated by Darktrace SOC analysts as part of the Managed Threat Detection service.

Enhanced Monitoring alerts investigated by SOC analysts as part of the Managed Detection and Response service.
Figure 5: Enhanced Monitoring alerts investigated by SOC analysts as part of the Managed Detection and Response service.

Final containment and collaborative response

Upon investigating the unusual scanning activity, outbound SSH connections, and internal data transfers, Darktrace analysts extended the Autonomous Response actions previously triggered on the compromised devices.

As the threat actor was leveraging these systems for data exfiltration, all outgoing traffic from the affected devices was blocked for an additional 24 hours to provide the customer’s security team with time to investigate and remediate the compromise.

Additional investigative support was provided by Darktrace analysts through the Security Operations Service, after the customer's opened of a ticket related to the unfolding incident.

Simplified timeline of the attack
Figure 8: Simplified timeline of the attack

Around the same time of the compromise in Case 2, Darktrace observed a similar incident on the cloud environment of a different customer.

Initial access

On this occasion, the threat actor appeared to have gained entry into the AWS-based Virtual Private Cloud (VPC) network via a SonicWall SMA 500v EC2 instance allowing inbound traffic on any port.

The instance received HTTPS connections from three rare Vultr VPS endpoints (i.e., 45.32.205[.]52, 207.246.74[.]166, 45.32.90[.]176).

Lateral movement and exfiltration

Around the same time, the EC2 instance started scanning the environment and attempted to pivot to other internal systems via RDP, notably a DC EC2 instance, which also started scanning the network, and another EC2 instance.  

The latter then proceeded to transfer more than 230 GB of data to the rare external GTHost VPS endpoint 23.150.248[.]189, while downloading hundreds of GBs of data over SMB from another EC2 instance.

Cyber AI Analyst incident generated following the unusual scanning and RDP connections from the initial compromised device.
Figure 7: Cyber AI Analyst incident generated following the unusual scanning and RDP connections from the initial compromised device.

The same behavior was replicated across multiple EC2 instances, whereby compromised instances uploaded data over internal RDP connections to other instances, which then started transferring data to the same GTHost VPS endpoint over port 5000, which is typically used for Universal Plug and Play (UPnP).

What Darktrace detected

Darktrace observed the threat actor uploading a total of 718 GB to the external endpoint, after which they detonated ransomware within the compromised VPC networks.

This activity generated nine Enhanced Monitoring alerts in Darktrace, focusing on the scanning and external data activity, with the earliest of those alerts triggering around one hour after the initial intrusion.

Darktrace’s Autonomous Response capability was not configured to act on these devices. Therefore, the malicious activity was not autonomously blocked and escalated to the point of ransomware detonation.

Conclusion

This blog examined three real-world compromises in customer cloud environments each illustrating different stages in the attack lifecycle.

The first case showcased a notable progression from a SaaS compromise to a full cloud intrusion, emphasizing the critical role of anomaly detection when legitimate credentials are abused.

The latter two incidents demonstrated that while early detection is vital, the ability to autonomously block malicious activity at machine speed is often the most effective way to contain threats before they escalate.

Together, these incidents underscore the need for continuous visibility, behavioral analysis, and machine-speed intervention across hybrid environments. Darktrace's AI-driven detection and Autonomous Response capabilities, combined with expert oversight from its Security Operations Center, give defenders the speed and clarity they need to contain threats and reduce operational disruption, before the situation spirals.

Credit to Alexandra Sentenac (Senior Cyber Analyst) and Dylan Evans (Security Research Lead)

References

[1] https://www.virustotal.com/gui/ip-address/67.217.57.252/community

Case 1

Darktrace / IDENTITY model alerts

IaaS / Compliance / Uncommon Azure External User Invite

SaaS / Resource / Repeated Unusual SaaS Resource Creation

IaaS / Compute / Azure Compute Resource Update

Cyber AI Analyst incidents

Possible Unsecured AzureActiveDirectory Resource

Possible Hijack of Office365 Account

Case 2

Darktrace / NETWORK model alerts

Compromise / SSH Beacon

Device / Multiple Lateral Movement Model Alerts

Device / Suspicious SMB Scanning Activity

Device / SMB Lateral Movement

Compliance / SSH to Rare External Destination

Device / Anomalous SMB Followed By Multiple Model Alerts

Device / Anonymous NTLM Logins

Anomalous Connection / SMB Enumeration

Device / New or Uncommon SMB Named Pipe Device / Network Scan

Device / Suspicious Network Scan Activity

Device / New Device with Attack Tools

Device / RDP Scan Device / Attack and Recon Tools

Compliance / High Priority Compliance Model Alert

Compliance / Outgoing NTLM Request from DC

Compromise / Large Number of Suspicious Successful Connections

Device / Large Number of Model Alerts

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Unusual Activity / Internal Data Transfer

Anomalous Connection / Unusual Internal Connections

Device / Anomalous RDP Followed By Multiple Model Alerts

Unusual Activity / Unusual External Activity

Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Unusual External Data to New Endpoint

Anomalous Connection / Multiple Connections to New External TCP Port

Darktrace / Autonomous Response model alerts

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / Manual / Quarantine Device

Antigena / MDR / MDR-Quarantined Device

Antigena / MDR / Model Alert on MDR-Actioned Device

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / Insider Threat / Antigena Internal Data Transfer Block

Cyber AI Analyst incidents

Possible Application Layer Reconnaissance Activity

Scanning of Multiple Devices

Unusual Repeated Connections

Unusual External Data Transfer

Case 3

Darktrace / NETWORK model alerts

Unusual Activity / Unusual Large Internal Transfer

Compliance / Incoming Remote Desktop

Unusual Activity / High Volume Server Data Transfer

Unusual Activity / Internal Data Transfer

Anomalous Connection / Unusual Internal Remote Desktop

Anomalous Connection / Unusual Incoming Data Volume

Anomalous Server Activity / Domain Controller Initiated to Client

Device / Large Number of Model Alerts

Anomalous Connection / Possible Flow Device Brute Force

Device / RDP Scan

Device / Suspicious Network Scan Activity

Device / Network Scan

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Connection / Download and Upload

Unusual Activity / Unusual External Data Transfer

Unusual Activity / High Volume Client Data Transfer

Unusual Activity / Unusual External Activity

Anomalous Connection / Uncommon 1 GiB Outbound

Device / Increased External Connectivity

Compromise / Large Number of Suspicious Successful Connections

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Low and Slow Exfiltration to IP

Unusual Activity / Enhanced Unusual External Data Transfer

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / Multiple Connections to New External UDP Port

Anomalous Connection / Possible Data Staging and External Upload

Unusual Activity / Unusual External Data to New Endpoint

Device / Large Number of Model Alerts from Critical Network Device

Compliance / External Windows Communications

Anomalous Connection / Unusual Internal Connections

Cyber AI Analyst incidents

Scanning of Multiple Devices

Extensive Unusual RDP Connections

MITRE ATT&CK mapping

(Technique name – Tactic ID)

Case 1

Defense Evasion - Modify Cloud Compute Infrastructure: Create Cloud Instance

Persistence – Account Manipulation

Case 2

Initial Access - External Remote Services

Execution - Inter-Process Communication

Persistence - External Remote Services

Discovery - System Network Connections Discovery

Discovery - Network Service Discovery

Discovery - Network Share Discovery

Lateral Movement - Remote Desktop Protocol

Lateral Movement - Remote Services: SMB/Windows Admin Shares

Collection - Data from Network Shared Drive

Command and Control - Protocol Tunneling

Exfiltration - Exfiltration Over Asymmetric Encrypted Non-C2 Protocol

Case 3

Initial Access - Exploit Public-Facing Application

Discovery - Remote System Discovery

Discovery - Network Service Discovery

Lateral Movement - Remote Services

Lateral Movement - Remote Desktop Protocol  

Collection - Data from Network Shared Drive

Collection - Data Staged: Remote Data Staging

Exfiltration - Exfiltration Over C2 Channel

Command and Control - Non-Standard Port

Command and Control – Web Service

Impact - Data Encrypted for Impact

List of IoCs

IoC         Type      Description + Probability

193.242.184[.]178 - IP Address - Possible Exfiltration Server  

45.32.205[.]52  - IP Address  - Possible C2 Infrastructure

45.32.90[.]176 - IP Address - Possible C2 Infrastructure

207.246.74[.]166 - IP Address - Likely C2 Infrastructure

67.217.57[.]252 - IP Address - Likely C2 Infrastructure

23.150.248[.]189 - IP Address - Possible Exfiltration Server

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About the author
Alexandra Sentenac
Cyber Analyst

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July 8, 2025

Top Eight Threats to SaaS Security and How to Combat Them

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The latest on the identity security landscape

Following the mass adoption of remote and hybrid working patterns, more critical data than ever resides in cloud applications – from Salesforce and Google Workspace, to Box, Dropbox, and Microsoft 365.

On average, a single organization uses 130 different Software-as-a-Service (SaaS) applications, and 45% of organizations reported experiencing a cybersecurity incident through a SaaS application in the last year.

As SaaS applications look set to remain an integral part of the digital estate, organizations are being forced to rethink how they protect their users and data in this area.

What is SaaS security?

SaaS security is the protection of cloud applications. It includes securing the apps themselves as well as the user identities that engage with them.

Below are the top eight threats that target SaaS security and user identities.

1.  Account Takeover (ATO)

Attackers gain unauthorized access to a user’s SaaS or cloud account by stealing credentials through phishing, brute-force attacks, or credential stuffing. Once inside, they can exfiltrate data, send malicious emails, or escalate privileges to maintain persistent access.

2. Privilege escalation

Cybercriminals exploit misconfigurations, weak access controls, or vulnerabilities to increase their access privileges within a SaaS or cloud environment. Gaining admin or superuser rights allows attackers to disable security settings, create new accounts, or move laterally across the organization.

3. Lateral movement

Once inside a network or SaaS platform, attackers move between accounts, applications, and cloud workloads to expand their foot- hold. Compromised OAuth tokens, session hijacking, or exploited API connections can enable adversaries to escalate access and exfiltrate sensitive data.

4. Multi-Factor Authentication (MFA) bypass and session hijacking

Threat actors bypass MFA through SIM swapping, push bombing, or exploiting session cookies. By stealing an active authentication session, they can access SaaS environments without needing the original credentials or MFA approval.

5. OAuth token abuse

Attackers exploit OAuth authentication mechanisms by stealing or abusing tokens that grant persistent access to SaaS applications. This allows them to maintain access even if the original user resets their password, making detection and mitigation difficult.

6. Insider threats

Malicious or negligent insiders misuse their legitimate access to SaaS applications or cloud platforms to leak data, alter configurations, or assist external attackers. Over-provisioned accounts and poor access control policies make it easier for insiders to exploit SaaS environments.

7. Application Programming Interface (API)-based attacks

SaaS applications rely on APIs for integration and automation, but attackers exploit insecure endpoints, excessive permissions, and unmonitored API calls to gain unauthorized access. API abuse can lead to data exfiltration, privilege escalation, and service disruption.

8. Business Email Compromise (BEC) via SaaS

Adversaries compromise SaaS-based email platforms (e.g., Microsoft 365 and Google Workspace) to send phishing emails, conduct invoice fraud, or steal sensitive communications. BEC attacks often involve financial fraud or data theft by impersonating executives or suppliers.

BEC heavily uses social engineering techniques, tailoring messages for a specific audience and context. And with the growing use of generative AI by threat actors, BEC is becoming even harder to detect. By adding ingenuity and machine speed, generative AI tools give threat actors the ability to create more personalized, targeted, and convincing attacks at scale.

Protecting against these SaaS threats

Traditionally, security leaders relied on tools that were focused on the attack, reliant on threat intelligence, and confined to a single area of the digital estate.

However, these tools have limitations, and often prove inadequate for contemporary situations, environments, and threats. For example, they may lack advanced threat detection, have limited visibility and scope, and struggle to integrate with other tools and infrastructure, especially cloud platforms.

AI-powered SaaS security stays ahead of the threat landscape

New, more effective approaches involve AI-powered defense solutions that understand the digital business, reveal subtle deviations that indicate cyber-threats, and action autonomous, targeted responses.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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