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June 25, 2024

Following up on our Conversation: Detecting & Containing a LinkedIn Phishing Attack with Darktrace

Darktrace/Email detected a phishing attack that had originated from LinkedIn, where the attacker impersonated a well known construction company to conduct a credential harvesting attack on the target. Darktrace’s ActiveAI Security Platform played a critical role in investigating the activity and initiating real-time responses that were outside the physical capability of human security teams.
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
Nicole Wong
Cyber Security Analyst
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25
Jun 2024

Note: Real organization, domain and user names have been modified and replaced with fictitious names to maintain anonymity.  

Social media cyber-attacks

Social media is a known breeding ground for cyber criminals to easily connect with a near limitless number of people and leverage the wealth of personal information shared on these platforms to defraud the general public.  Analysis suggests even the most tech savvy ‘digital natives’ are vulnerable to impersonation scams over social media, as criminals weaponize brands and trends, using the promise of greater returns to induce sensitive information sharing or fraudulent payments [1].

LinkedIn phishing

As the usage of a particular social media platform increases, cyber criminals will find ways to exploit the increasing user base, and this trend has been observed with the rise in LinkedIn scams in recent years [2].  LinkedIn is the dominant professional networking site, with a forecasted 84.1million users by 2027 [3].  This platform is data-driven, so users are encouraged to share information publicly, including personal life updates, to boost visibility and increase job prospects [4] [5].  While this helps legitimate recruiters to gain a good understanding of the user, an attacker could also leverage the same personal content to increase the sophistication and success of their social engineering attempts.  

Darktrace detection of LinkedIn phishing

Darktrace detected a Software-as-a-Service (SaaS) compromise affecting a construction company, where the attack vector originated from LinkedIn (outside the monitoring of corporate security tools), but then pivoted to corporate email where a credential harvesting payload was delivered, providing the attacker with credentials to access a corporate file storage platform.  

Because LinkedIn accounts are typically linked to an individual’s personal email and are most commonly accessed via the mobile application [6] on personal devices that are not monitored by security teams, it can represent an effective initial access point for attackers looking to establish an initial relationship with their target. Moreover, user behaviors to ignore unsolicited emails from new or unknown contacts are less frequently carried over to platforms like LinkedIn, where interactions with ‘weak ties’ as opposed to ‘strong ties’ are a better predictor of job mobility [7]. Had this attack been allowed to continue, the threat actor could have leveraged access to further information from the compromised business cloud account to compromise other high value accounts, exfiltrate sensitive data, or defraud the organization.

LinkedIn phishing attack details

Reconnaissance

The initial reconnaissance and social engineering occurred on LinkedIn and was thus outside the purview of corporate security tools, Darktrace included.

However, the email domain “hausconstruction[.]com” used by the attacker in subsequent communications appears to be a spoofed domain impersonating a legitimate construction company “haus[.]com”, suggesting the attacker may have also impersonated an employee of this construction company on LinkedIn.  In addition to spoofing the domain, the attacker seemingly went further to register “hausconstruction.com” on a commercial web hosting platform.  This is a technique used frequently not just to increase apparent legitimacy, but also to bypass traditional security tools since newly registered domains will have no prior threat intelligence, making them more likely to evade signature and rules-based detections [8].  In this instance, open-source intelligence (OSINT) sources report that the domain was created several months earlier, suggesting this may have been part of a targeted attack on construction companies.  

Initial Intrusion

It was likely that during the correspondence over LinkedIn, the target user was solicited into following up over email regarding a prospective construction project, using their corporate email account.  In a probable attempt to establish a precedent of bi-directional correspondence so that subsequent malicious emails would not be flagged by traditional security tools, the attacker did not initially include suspicious links, attachments or use solicitous or inducive language within their initial emails.

Example of bi-directional email correspondence between the target and the attacker impersonating a legitimate employee of the construction company haus.com.
Figure 1: Example of bi-directional email correspondence between the target and the attacker impersonating a legitimate employee of the construction company haus.com.
Cyber AI Analyst investigation into one of the initial emails the target received from the attacker.
Figure 2: Cyber AI Analyst investigation into one of the initial emails the target received from the attacker.  

To accomplish the next stage of their attack, the attacker shared a link, hidden behind the inducing text “VIEW ALL FILES”, to a malicious file using the Hightail cloud storage service. This is also a common method employed by attackers to evade detection, as this method of file sharing does not involve attachments that can be scanned by traditional security tools, and legitimate cloud storage services are less likely to be blocked.

OSINT analysis on the malicious link link shows the file hosted on Hightail was a HTML file with the associated message “Following up on our LinkedIn conversation”.  Further analysis suggests the file contained obfuscated Javascript that, once opened, would automatically redirect the user to a malicious domain impersonating a legitimate Microsoft login page for credential harvesting purposes.  

The malicious HTML file containing obfuscated Javascript, where the highlighted string references the malicious credential harvesting domain.
Figure 3: The malicious HTML file containing obfuscated Javascript, where the highlighted string references the malicious credential harvesting domain.
Screenshot of fraudulent Microsoft Sign In page hosted on the malicous credential harvesting domain.
Figure 4: Screenshot of fraudulent Microsoft Sign In page hosted on the malicious credential harvesting domain.

Although there was prior email correspondence with the attacker, this email was not automatically deemed safe by Darktrace and was further analyzed for unusual properties and unusual communications for the recipient and the recipient’s peer group.  

Darktrace determined that:

  • It was unusual for this file storage solution to be referenced in communications to the user and the wider network
  • Textual properties of the email body suggested a high level of inducement from the sender, with a high level of focus on the phishing link.
  • The full link contained suspicious properties suggesting it is high risk.
Darktrace’s analysis of the phishing email, presenting key information about the unusual characteristics of this email, information on highlighted content, and an overview of actions that were initially applied.
Figure 5: Darktrace’s analysis of the phishing email, presenting key information about the unusual characteristics of this email, information on highlighted content, and an overview of actions that were initially applied.  

Based on these anomalies, Darktrace initially moved the phishing email to the junk folder and locked the link, preventing the user from directly accessing the malicious file hosted on Hightail.  However, the customer’s security team released the email, likely upon end-user request, allowing the target user to access the file and ultimately enter their credentials into that credential harvesting domain.

Darktrace alerts triggered by the malicious phishing email and the corresponding Autonomous Response actions.
Figure 6: Darktrace alerts triggered by the malicious phishing email and the corresponding Autonomous Response actions.

Lateral Movement

Correspondence between the attacker and target continued for two days after the credential harvesting payload was delivered.  Five days later, Darktrace detected an unusual login using multi-factor authentication (MFA) from a rare external IP and ASN that coincided with Darktrace/Email logs showing access to the credential harvesting link.

This attempt to bypass MFA, known as an Office365 Shell WCSS attack, was likely achieved by inducing the target to enter their credentials and legitimate MFA token into the fake Microsoft login page. This was then relayed to Microsoft by the attacker and used to obtain a legitimate session. The attacker then reused the legitimate token to log into Exchange Online from a different IP and registered their own device for MFA.

Screenshot within Darktrace/Email of the phishing email that was released by the security team, showing the recipient clicked the link to file storage where the malicious payload was stored.
Figure 7: Screenshot within Darktrace/Email of the phishing email that was released by the security team, showing the recipient clicked the link to file storage where the malicious payload was stored.

Event Log showing a malicious login and MFA bypass at 17:57:16, shortly after the link was clicked.  Highlighted in green is activity from the legitimate user prior to the malicious login, using Edge.
Figure 8: Event Log showing a malicious login and MFA bypass at 17:57:16, shortly after the link was clicked.  Highlighted in green is activity from the legitimate user prior to the malicious login, using Edge. Highlighted in orange and red is the malicious activity using Chrome.

The IP addresses used by the attacker appear to be part of anonymization infrastructure, but are not associated with any known indicators of compromise (IoCs) that signature-based detections would identify [9] [10].

In addition to  logins being observed within half an hour of each other from multiple geographically impossible locations (San Francisco and Phoenix), the unexpected usage of Chrome browser, compared to Edge browser previously used, provided Darktrace with further evidence that this activity was unlikely to originate from the legitimate user.  Although the user was a salesperson who frequently travelled for their role, Darktrace’s Self-Learning AI understood that the multiple logins from these locations was highly unusual at the user and group level, and coupled with the subsequent unexpected account modification, was a likely indicator of account compromise.  

Accomplish mission

Although the email had been manually released by the security team, allowing the attack to propagate, additional layers of defense were triggered as Darktrace's Autonomous Response initiated “Disable User” actions upon detection of the multiple unusual logins and the unauthorized registration of security information.  

However, the customer had configured Autonomous Response to require human confirmation, therefore no actions were taken until the security team manually approved them over two hours later. In that time, access to mail items and other SharePoint files from the unusual IP address was detected, suggesting a potential loss of confidentiality to business data.

Advanced Search query showing several FilePreviewed and MailItemsAccessed events from either the IPs used by the attacker, or using the software Chrome.  Note some of the activity originated from Microsoft IPs which may be whitelisted by traditional security tools.
Figure 9: Advanced Search query showing several FilePreviewed and MailItemsAccessed events from either the IPs used by the attacker, or using the software Chrome.  Note some of the activity originated from Microsoft IPs which may be whitelisted by traditional security tools.

However, it appears that the attacker was able to maintain access to the compromised account, as login and mail access events from 199.231.85[.]153 continued to be observed until the afternoon of the next day.  

Conclusion

This incident demonstrates the necessity of AI to security teams, with Darktrace’s ActiveAI Security Platform detecting a sophisticated phishing attack where human judgement fell short and initiated a real-time response when security teams could not physically respond as fast.  

Security teams are very familiar with social engineering and impersonation attempts, but these attacks remain highly prevalent due to the widespread adoption of technologies that enable these techniques to be deployed with great sophistication and ease.  In particular, the popularity of information-rich platforms like LinkedIn that are geared towards connecting with unknown people make it an attractive initial access point for malicious attackers.

In the second half of 2023 alone, over 200 thousand fake profiles were reported by members on LinkedIn [11].  Fake profiles can be highly sophisticated, use professional images, contain compelling descriptions, reference legitimate company listings and present believable credentials.  

It is unrealistic to expect end users to defend themselves against such sophisticated impersonation attempts. Moreover, it is extremely difficult for human defenders to recognize every fraudulent interaction amidst a sea of fake profiles. Instead, defenders should leverage AI, which can conduct autonomous investigations without human biases and limitations. AI-driven security can ensure successful detection of fraudulent or malicious activity by learning what real users and devices look like and identifying deviations from their learned behaviors that may indicate an emerging threat.

Appendices

Darktrace Model Detections

DETECT/ Apps

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and Account Update

SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compliance / M365 Security Information Modified

RESPOND/ Apps

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Unusual Activity Block

DETECT & RESPOND/ Email

·      Link / High Risk Link + Low Sender Association

·      Link / New Correspondent Classified Link

·      Link / Watched Link Type

·      Antigena Anomaly

·      Association / Unknown Sender

·      History / New Sender

·      Link / Link to File Storage

·      Link / Link to File Storage + Unknown Sender

·      Link / Low Link Association

List of IoCs

·      142.252.106[.]251 - IP            - Possible malicious IP used by attacker during cloud account compromise

·      199.231.85[.]153 – IP - Probable malicious IP used by attacker during cloud account compromise

·      vukoqo.hebakyon[.]com – Endpoint - Credential harvesting endpoint

MITRE ATT&CK Mapping

·      Resource Development - T1586 - Compromise Accounts

·      Resource Development - T1598.003 – Spearphishing Link

·      Persistence - T1078.004 - Cloud Accounts

·      Persistence - T1556.006 - Modify Authentication Process: Multi-Factor Authentication

·      Reconnaissance - T1593.001 – Social Media

·      Reconnaissance - T1598 – Phishing for Information

·      Reconnaissance - T1589.001 – Credentials

·      Reconnaissance - T1591.002 – Business Relationships

·      Collection - T1111 – Multifactor Authentication Interception

·      Collection - T1539 – Steal Web Session Cookie

·      Lateral Movement - T1021.007 – Cloud Services

·      Lateral Movement - T1213.002 - Sharepoint

References

[1] Jessica Barker, Hacked: The secrets behind cyber attacks, (London: Kogan Page, 2024), p. 130-146.

[2] https://www.bitdefender.co.uk/blog/hotforsecurity/5-linkedin-scams-and-how-to-avoid-them/

[3] https://www.washingtonpost.com/technology/2023/08/31/linkedin-personal-posts/

[4] https://www.forbes.com/sites/joshbersin/2012/05/21/facebook-vs-linkedin-whats-the-difference/

[5] https://thelinkedblog.com/2022/3-reasons-why-you-should-make-your-profile-public-1248/

[6] https://www.linkedin.com/pulse/50-linkedin-statistics-every-professional-should-ti9ue

[7] https://www.nytimes.com/2022/09/24/business/linkedin-social-experiments.html

[8] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

[9] https://spur.us/context/142.252.106[.]251

[10] https://spur.us/context/199.231.85[.]153

[11]https://www.statista.com/statistics/1328849/linkedin-number-of-fake-accounts-detected-and-removed

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
Nicole Wong
Cyber Security Analyst

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July 3, 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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July 2, 2025

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

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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:

  1. https://www.first.org/blog/20250607-Vulnerability-Forecast-for-2025
  2. https://cloud.google.com/blog/topics/threat-intelligence/fortimanager-zero-day-exploitation-cve-2024-47575
  3. https://thehackernews.com/2025/05/china-linked-hackers-exploit-sap-and.html

Related Darktrace blogs:

*Self-reported by customer, confirmed afterwards.

**Updated January 2024 blog now reflects current findings

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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