July 26, 2024

Understanding the WarmCookie Backdoor Threat

Discover effective strategies for disarming the WarmCookie backdoor and securing your systems against this persistent threat.

Written by

Justin Torres

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

Justin Torres

Cyber Analyst

Jul 2024

What is WarmCookie malware?

WarmCookie, also known as BadSpace [2], is a two-stage backdoor tool that provides functionality for threat actors to retrieve victim information and launch additional payloads. The malware is primarily distributed via phishing campaigns according to multiple open-source intelligence (OSINT) providers.

Backdoor malware: A backdoor tool is a piece of software used by attackers to gain and maintain unauthorized access to a system. It bypasses standard authentication and security mechanisms, allowing the attacker to control the system remotely.

Two-stage backdoor malware: This means the backdoor operates in two distinct phases:

1. Initial Stage: The first stage involves the initial infection and establishment of a foothold within the victim's system. This stage is often designed to be small and stealthy to avoid detection.‍

2. Secondary Stage: Once the initial stage has successfully compromised the system, it retrieves or activates the second stage payload. This stage provides more advanced functionalities for the attacker, such as extensive data exfiltration, deeper system control, or the deployment of additional malicious payloads.

How does WarmCookie malware work?

Reported attack patterns include emails attempting to impersonate recruitment firms such as PageGroup, Michael Page, and Hays. These emails likely represented social engineering tactics, with attackers attempting to manipulate jobseekers into engaging with the emails and following malicious links embedded within [3].

This backdoor tool also adopts stealth and evasion tactics to avoid the detection of traditional security tools. Reported evasion tactics included custom string decryption algorithms, as well as dynamic API loading to prevent researchers from analyzing and identifying the core functionalities of WarmCookie [1].

Before this backdoor makes an outbound network request, it is known to capture details from the target machine, which can be used for fingerprinting and identification [1], this includes:

- Computer name

- Username

- DNS domain of the machine

- Volume serial number

WarmCookie samples investigated by external researchers were observed communicating over HTTP to a hardcoded IP address using a combination of RC4 and Base64 to protect its network traffic [1]. Ultimately, threat actors could use this backdoor to deploy further malicious payloads on targeted networks, such as ransomware.

Darktrace Coverage of WarmCookie

Between April and June 2024, Darktrace’s Threat Research team investigated suspicious activity across multiple customer networks indicating that threat actors were utilizing the WarmCookie backdoor tool. Observed cases across customer environments all included the download of unusual executable (.exe) files and suspicious outbound connectivity.

Affected devices were all observed making external HTTP requests to the German-based external IP, 185.49.69[.]41, and the URI, /data/2849d40ade47af8edfd4e08352dd2cc8.

The first investigated instance occurred between April 23 and April 24, when Darktrace detected a a series of unusual file download and outbound connectivity on a customer network, indicating successful WarmCookie exploitation. As mentioned by Elastic labs, "The PowerShell script abuses the Background Intelligent Transfer Service (BITS) to download WarmCookie and run the DLL with the Start export" [1].

Less than a minute later, the same device was observed making HTTP requests to the rare external IP address: 185.49.69[.]41, which had never previously been observed on the network, for the URI /data/b834116823f01aeceed215e592dfcba7. The device then proceeded to download masqueraded executable file from this endpoint. Darktrace recognized that these connections to an unknown endpoint, coupled with the download of a masqueraded file, likely represented malicious activity.

Following this download, the device began beaconing back to the same IP, 185.49.69[.]41, with a large number of external connections observed over port 80. This beaconing related behavior could further indicate malicious software communicating with command-and-control (C2) servers.

Darktrace’s model alert coverage included the following details:

[Model Alert: Device / Unusual BITS Activity]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:10:23 UTC

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Microsoft BITS/7.8

[Model Alert: Anomalous File / EXE from Rare External Location]

[Model Alert: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:11:18 UTC

- Destination IP: 185.49.69[.]41

- Destination port: 80

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)

- Event details: File: http[:]//185.49.69[.]41/data/b834116823f01aeceed215e592dfcba7, total seen size: 144384B, direction: Incoming

- SHA1 file hash: 4ddf0d9c750bfeaebdacc14152319e21305443ff

- MD5 file hash: b09beb0b584deee198ecd66976e96237

[Model Alert: Compromise / Beaconing Activity To External Rare]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:15:24 UTC

- Destination IP: 185.49.69[.]41

- Destination port: 80

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)

Between May 7 and June 4, Darktrace identified a wide range of suspicious external connectivity on another customer’s environment. Darktrace’s Threat Research team further investigated this activity and assessed it was likely indicative of WarmCookie exploitation on customer devices.

Similar to the initial use case, BITS activity was observed on affected devices, which is utilized to download WarmCookie [1]. This initial behavior was observed with the device after triggering the model: Device / Unusual BITS Activity on May 7.

Just moments later, the same device was observed making HTTP requests to the aforementioned German IP address, 185.49.69[.]41 using the same URI /data/2849d40ade47af8edfd4e08352dd2cc8, before downloading a suspicious executable file.

Just like the first use case, this device followed up this suspicious download with a series of beaconing connections to 185.49.69[.]41, again with a large number of connections via port 80.

Similar outgoing connections to 185.49.69[.]41 and model alerts were observed on additional devices during the same timeframe, indicating that numerous customer devices had been compromised.

Darktrace’s model alert coverage included the following details:

[Model Alert: Device / Unusual BITS Activity]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:23 UTC

- ASN: AS28753 Leaseweb Deutschland GmbH

- User agent: Microsoft BITS/7.8

[Model Alert: Anomalous File / EXE from Rare External Location]

[Model Alert: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:35 UTC

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- ASN: AS28753 Leaseweb Deutschland GmbH

- Event details: File: http[:]//185.49.69[.]41/data/2849d40ade47af8edfd4e08352dd2cc8, total seen size: 72704B, direction: Incoming

- SHA1 file hash: 5b0a35c574ee40c4bccb9b0b942f9a9084216816

- MD5 file hash: aa9a73083184e1309431b3c7a3e44427

[Model Alert: Anomalous Connection / New User Agent to IP Without Hostname]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:04:14 UTC

- Destination IP: 185.49.69[.]41

- Application protocol: HTTP

- URI: /data/2849d40ade47af8edfd4e08352dd2cc8

- User agent: Microsoft BITS/7.8

[Model Alert: Compromise / HTTP Beaconing to New Endpoint]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:08:47 UTC

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- Application protocol: HTTP

- ASN: AS28753 Leaseweb Deutschland GmbH

- URI: /

- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705) \

Figure 1: Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.

Figure 2: External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.

Fortunately, this particular customer was subscribed to Darktrace’s Proactive Threat Notification (PTN) service and the Darktrace Security Operation Center (SOC) promptly investigated the activity and alerted the customer. This allowed their security team to address the activity and begin their own remediation process.

In this instance, Darktrace’s Autonomous Response capability was configured in Human Confirmation mode, meaning any mitigative actions required manual application by the customer’s security team.

Despite this, Darktrace recommended two actions to contain the activity: blocking connections to the suspicious IP address 185.49.69[.]41 and any IP addresses ending with '69[.]41', as well as the ‘Enforce Pattern of Life’ action. By enforcing a pattern of life, Darktrace can restrict a device (or devices) to its learned behavior, allowing it to continue regular business activities uninterrupted while blocking any deviations from expected activity.

Figure 3: Actions suggested by Darktrace to contain the emerging activity, including blocking connections to the suspicious endpoint and restricting the device to its ‘pattern of life’.

Conclusion

Backdoor tools like WarmCookie enable threat actors to gather and leverage information from target systems to deploy additional malicious payloads, escalating their cyber attacks. Given that WarmCookie’s primary distribution method seems to be through phishing campaigns masquerading as trusted recruitments firms, it has the potential to affect a large number of organizations.

In the face of such threats, Darktrace’s behavioral analysis provides organizations with full visibility over anomalous activity on their digital estates, regardless of whether the threat bypasses by human security teams or email security tools. While threat actors seemingly managed to evade customers’ native email security and gain access to their networks in these cases, Darktrace identified the suspicious behavior associated with WarmCookie and swiftly notified customer security teams.

Had Darktrace’s Autonomous Response capability been fully enabled in these cases, it could have blocked any suspicious connections and subsequent activity in real-time, without the need of human intervention, effectively containing the attacks in the first instance.

Credit to Justin Torres, Cyber Security Analyst and Dylan Hinz, Senior Cyber Security Analyst

Appendices

Darktrace Model Detections

- Anomalous File / EXE from Rare External Location

- Anomalous File / Masqueraded File Transfer

- Compromise / Beacon to Young Endpoint

- Compromise / Beaconing Activity To External Rare

- Compromise / HTTP Beaconing to New Endpoint

- Compromise / HTTP Beaconing to Rare Destination

- Compromise / High Volume of Connections with Beacon Score

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Quick and Regular Windows HTTP Beaconing

- Compromise / SSL or HTTP Beacon

- Compromise / Slow Beaconing Activity To External Rare

- Compromise / Sustained SSL or HTTP Increase

- Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

- Anomalous Connection / Multiple Failed Connections to Rare Endpoint

- Anomalous Connection / New User Agent to IP Without Hostname

- Compromise / Sustained SSL or HTTP Increase

AI Analyst Incident Coverage:

- Unusual Repeated Connections

- Possible SSL Command and Control to Multiple Endpoints

- Possible HTTP Command and Control

- Suspicious File Download

Darktrace RESPOND Model Detections:

- Antigena / Network / External Threat / Antigena Suspicious File Block

- Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

List of IoCs

IoC - Type - Description + Confidence

185.49.69[.]41 – IP Address – WarmCookie C2 Endpoint

/data/2849d40ade47af8edfd4e08352dd2cc8 – URI – Likely WarmCookie URI

/data/b834116823f01aeceed215e592dfcba7 – URI – Likely WarmCookie URI

4ddf0d9c750bfeaebdacc14152319e21305443ff - SHA1 Hash – Possible Malicious File

5b0a35c574ee40c4bccb9b0b942f9a9084216816 - SHA1 Hash – Possiblem Malicious File

MITRE ATT&CK Mapping

(Technique Name) – (Tactic) – (ID) – (Sub-Technique of)

Drive-by Compromise - INITIAL ACCESS - T1189

Ingress Tool Transfer - COMMAND AND CONTROL - T1105

Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

Lateral Tool Transfer - LATERAL MOVEMENT - T1570

Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

Web Services - RESOURCE DEVELOPMENT - T1583.006 - T1583

Browser Extensions - PERSISTENCE - T1176

Application Layer Protocol - COMMAND AND CONTROL - T1071

Fallback Channels - COMMAND AND CONTROL - T1008

Multi-Stage Channels - COMMAND AND CONTROL - T1104

Non-Standard Port - COMMAND AND CONTROL - T1571

One-Way Communication - COMMAND AND CONTROL - T1102.003 - T1102

Encrypted Channel - COMMAND AND CONTROL - T1573

External Proxy - COMMAND AND CONTROL - T1090.002 - T1090

Non-Application Layer Protocol - COMMAND AND CONTROL - T1095

References

[1] https://www.elastic.co/security-labs/dipping-into-danger

[2] https://www.gdatasoftware.com/blog/2024/06/37947-badspace-backdoor

[3] https://thehackernews.com/2024/06/new-phishing-campaign-deploys.html

Written by

Justin Torres

Cyber Analyst

Inside the SOC

Written by

Justin Torres

Cyber Analyst

VP, Security & AI Strategy, Field CISO

Senior Product Marketing Manager, Email

Watch the NIS2 Webinar

Blog

Identity

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

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]

‍

About the author

Carlos Gray

Senior Product Marketing Manager, Email

Blog

Proactive Security

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

‍

Vulnerabilities are weaknesses in a system that can be exploited by malicious actors to gain unauthorized access or to disrupt normal operations. Common Vulnerabilities and Exposures (or CVEs) are a list of publicly disclosed cybersecurity vulnerabilities that can be tracked and mitigated by the security community.

When a vulnerability is discovered, the standard practice is to report it to the vendor or the responsible organization, allowing them to develop and distribute a patch or fix before the details are made public. This is known as responsible disclosure.

With a record-breaking 40,000 CVEs reported for 2024 and a predicted higher number for 2025 by the Forum for Incident Response and Security Teams (FIRST) [1], anomaly-detection is essential for identifying these potential risks. The gap between exploitation of a zero-day and disclosure of the vulnerability can sometimes be considerable, and retroactively attempting to identify successful exploitation on your network can be challenging, particularly if taking a signature-based approach.

Detecting threats without relying on CVE disclosure

Abnormal behaviors in networks or systems, such as unusual login patterns or data transfers, can indicate attempted cyber-attacks, insider threats, or compromised systems. Since Darktrace does not rely on rules or signatures, it can detect malicious activity that is anomalous even without full context of the specific device or asset in question.

‍For example, during the Fortinet exploitation late last year, the Darktrace Threat Research team were investigating a different Fortinet vulnerability, namely CVE 2024-23113, for exploitation when Mandiant released a security advisory around CVE 2024-47575, which aligned closely with Darktrace’s findings.

Retrospective analysis like this is used by Darktrace’s threat researchers to better understand detections across the threat landscape and to add additional context.

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Often, the disclosure of an exploited vulnerability can be off the back of an incident response investigation related to a compromise by an advanced threat actor using a zero-day. Once the vulnerability is registered and publicly disclosed as having been exploited, it can kick off a race between the attacker and defender: attack vs patch.

Nation-state actors, highly skilled with significant resources, are known to use a range of capabilities to achieve their target, including zero-day use. Often, pre-CVE activity is “low and slow”, last for months with high operational security. After CVE disclosure, the barriers to entry lower, allowing less skilled and less resourced attackers, like some ransomware gangs, to exploit the vulnerability and cause harm. This is why two distinct types of activity are often seen: pre and post disclosure of an exploited vulnerability.

Darktrace saw this consistent story line play out during several of the Fortinet and PAN OS threat actor campaigns highlighted above last year, where nation-state actors were seen exploiting vulnerabilities first, followed by ransomware gangs impacting organizations [2].

The same applies with the recent SAP Netweaver exploitations being tied to a China based threat actor earlier this spring with subsequent ransomware incidents being observed [3].

Autonomous Response

Anomaly-based detection offers the benefit of identifying malicious activity even before a CVE is disclosed; however, security teams still need to quickly contain and isolate the activity.

For example, during the Ivanti chaining exploitation in the early part of 2025, a customer had Darktrace’s Autonomous Response capability enabled on their network. As a result, Darktrace was able to contain the compromise and shut down any ongoing suspicious connectivity by blocking internal connections and enforcing a “pattern of life” on the affected device.

This pre-CVE detection and response by Darktrace occurred 11 days before any public disclosure, demonstrating the value of an anomaly-based approach.

In some cases, customers have even reported that Darktrace stopped malicious exploitation of devices several days before a public disclosure of a vulnerability.

For example, During the ConnectWise exploitation, a customer informed the team that Darktrace had detected malicious software being installed via remote access. Upon further investigation, four servers were found to be impacted, while Autonomous Response had blocked outbound connections and enforced patterns of life on impacted devices.

Conclusion

By continuously analyzing behavioral patterns, systems can spot unusual activities and patterns from users, systems, and networks to detect anomalies that could signify a security breach.

Through ongoing monitoring and learning from these behaviors, anomaly-based security systems can detect threats that traditional signature-based solutions might miss, while also providing detailed insights into threat tactics, techniques, and procedures (TTPs). This type of behavioral intelligence supports pre-CVE detection, allows for a more adaptive security posture, and enables systems to evolve with the ever-changing threat landscape.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO), Emma Fougler (Global Threat Research Operations Lead), Ryan Traill (Analyst Content Lead)

References and further reading:

Related Darktrace blogs:

‍

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

Your data. Our AI.

Elevate your network security with Darktrace AI

Get a demo

Check out this article by Darktrace: Understanding the WarmCookie Backdoor Threat

Understanding the WarmCookie Backdoor Threat

What is WarmCookie malware?

How does WarmCookie malware work?

Darktrace Coverage of WarmCookie

Conclusion

Appendices

List of IoCs

MITRE ATT&CK Mapping

References

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Top Eight Threats to SaaS Security and How to Combat Them

Enjoying the blog?

More in this series

Darktrace Collaborates with Microsoft: Unifying Email Security with a Shared Vision

Why attack-centric approaches to email security can’t cope with modern threats

Evaluating Email Security: How to Select the Best Solution for Your Organization

Blog

Identity

July 3, 2025

Top Eight Threats to SaaS Security and How to Combat Them

The latest on the identity security landscape

What is SaaS security?

1. Account Takeover (ATO)

2. Privilege escalation

3. Lateral movement

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

5. OAuth token abuse

6. Insider threats

7. Application Programming Interface (API)-based attacks

8. Business Email Compromise (BEC) via SaaS

Protecting against these SaaS threats

AI-powered SaaS security stays ahead of the threat landscape

Blog

Proactive Security

July 2, 2025

Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability

Detecting threats without relying on CVE disclosure

Below are ten examples from the past year where Darktrace detected malicious activity days or even weeks before a vulnerability was publicly disclosed.

Trends in pre-cve exploitation

Autonomous Response

Conclusion

References and further reading:

Related Darktrace blogs: