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May 19, 2020

Understanding a SaaS Attack and How AI Can Investigate

The Cyber AI Platform recently detected and investigated two incidents of SaaS account takeover in real-time. Learn about the importance of cyber security here!
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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19
May 2020

Executive summary

  • Darktrace has observed a significant increase in attacks against SaaS platforms, including file storage, collaborative work, and email solutions.
  • This blog post details two example threats that are representative of the current threat landscape: an Office 365 business email compromise and a Box.com file sharing account compromise.
  • Organizations are recommended to enable multi-factor authentication to combat credential stuffing attacks and the re-use of stolen credentials from data dumps. It is further advised to actively monitor SaaS environments for in-progress cyber-attacks.
  • SaaS exacerbates the skill gap in security – identifying and investigating threats in SaaS environments is a different skill to traditional security operations skill-sets.

Introduction

The digital transformation – whether planned naturally or forced by the global pandemic – has increased the use of Software-as-a-Service (SaaS) solutions in modern organizations. The annual growth rate of the SaaS market is currently 18%, and as the workforce becomes increasingly remote throughout 2020, this is set to skyrocket.

Attackers have been targeting SaaS solutions for a long time – but almost nobody talks about how the Techniques, Tools & Procedures (TTPs) in SaaS attacks differ significantly from traditional TTPs seen in networks and endpoint attacks.

How do you create meaningful detections in SaaS environments that don’t have endpoint or network data? How can you investigate threats in a SaaS environment as an analyst? What does a ‘good’ SaaS event look like, and what does a threat look like? Finding skilled security analysts that can work in traditional IT environments is already hard – it gets even harder when trying to hire security people with SaaS domain knowledge.

SaaS consumers are left with only a few choices: either use the native SaaS security controls provided in each SaaS solution – and rely on the (non-)maturity of the SaaS provider – or go with a third party SaaS security solution, often in the form of Cloud Access Security Brokers (CASBs). Both cases are often not ideal.

This blog outlines two attacks we have recently observed in SaaS environments that are representative for the broader SaaS threat landscape: a Microsoft (Office) 365 business email compromise (BEC) and the compromise of a corporate Box.com account. The analysis serves to illuminate the sharp distinction between a traditional network attack and a SaaS compromise – demonstrating how using machine learning to detect anomalies in behavior offers crucial hope for defenders as SaaS applications define this new era of work.

Anonymized SaaS Threat 1: Office 365 Business Email Compromise

Figure 1: The timeline of attack for the Microsoft 365 Compromise

In this case of a classic BEC attack, a threat-actor infiltrated an employee’s Microsoft 365 account to access sensitive financial documents hosted in SharePoint, including pay slip and banking details. The attacker went on to make configuration changes to the hacked inbox, deleting items and making updates that may have allowed them to cover their tracks.

Darktrace first observed the employee’s account log in from unusual IP ranges. The particular account had never logged in from Bulgaria before, and the peer accounts belonging to those from the same department had not exhibited similar behavioral traits. This in itself was a low-level anomaly and not necessarily indicative of malicious activity – employees might change locations after all.

The unusual login location was then accompanied by an unusual login time and a new user-agent. All of these anomalies triggered Cyber AI Analyst – Darktrace’s automated threat investigation technology – to launch a deeper analysis.

Darktrace then identified that the account was starting to access highly sensitive information, including payroll information on a Sharepoint. Two examples that were highlighted by AI Analyst are shown below:

  • hxxps://anonymised[.]sharepoint[.]com/anonymised/pages/Understanding-my-payslip[.]aspx
  • hxxps:// anonymised [.]sharepoint[.]com/anonymised /pages/Changing-my-bank-details[.]aspx

The attacker tried to gain insights about payment information and credit card details, with the likely intention of changing the payroll details to an attacker-controlled bank account. But with its ability to automatically analyze events to piece together attack narratives, Cyber AI Analyst was able to put together these weak signals of a threat and illuminate the likely account compromise. The security team was then able to lock the account and alert the user, who subsequently changed their credentials.

Anonymized SaaS Threat 2: Box.com Compromise

Figure 2: The timeline of attack for the Box.com Compromise

Darktrace observed a case of unauthorized access to a corporate Box.com file storage account belonging to an employee of a global supply company. The Box.com account login took place in the US – the same country that this organization operates in – but from an unusual IP space and ASN. Made suspicious by this low-level anomaly, Cyber AI Analyst did further, ongoing investigations into the user’s activity.

The actor behind the account logged in to Box.com successfully, and then proceeded to download expense reports, invoices, and other financial documents. It became evident that the account started accessing files that were highly unusual for the account to access. Darktrace recognized that neither the account itself, nor its peer group were usually accessing the file called ‘PASSWORD SHEET.xlsx’.

With Cyber AI’s bespoke knowledge of ‘self’ for every member of the organization’s workforce, the technology was able to identify the threat immediately. The Darktrace Cyber AI Platform detected that the activity occurred at a highly unusual time for the legitimate user, and that the location of the actor’s IP address was also anomalous compared to the employee’s previous access locations for this particular SaaS service.

While accessing these documents may have been normal for the employee in another context, Darktrace Cyber AI’s deep understanding of user behavior and granular visibility within the Box.com application allowed it to spot the subtle signs of account compromise. Moreover, when Darktrace’s Cyber AI Analyst automatically investigated the threat, it was able to illuminate the wider narrative, understanding that each unauthorized file exposure was part of a connected incident and highlighted the breach as a key concern for the security team.

Conclusion

Traditional detection approaches like ‘more than X failed logins from Y’ are not enough to ensure sufficient security across SaaS applications. Keeping threat intelligence lists up to date is even more difficult, as most SaaS attacks don’t involve any Command & Control – just indiscriminate logins from remote devices. Attackers may use VPN, Tor, other compromised devices, dynamic DNS, or virtual private servers to further mask their tracks.

A more intricate and effective approach to SaaS security requires understanding the dynamic individual behind the account. SaaS applications are fundamentally platforms for humans to communicate – allowing them to exchange and store ideas and information. Abnormal, threatening behavior is therefore impossible to detect without a nuanced understanding of those unique individuals: where and when do they typically access a SaaS account, which files are they like to access, who do they typically connect with?

Cyber AI asks these questions, continuously analyzing data not only across SaaS platforms, but from the unique ‘patterns of life’ of every user and device in the organization as a whole. With this context, it can chain together seemingly disparate anomalies – unusual login times, login locations, access of new or unusual files, and hundreds of other indicators of threat. These anomalies then act as a trigger for more in-depth investigations via Cyber AI Analyst that can link the anomalies together and create a coherent attack narrative.

Both of the above SaaS attacks were comprehensively but succinctly investigated and fully reported on by the Darktrace’s Cyber AI Analyst, which then surfaced an easy-to-understand incident report, ready for executive review. For a more in-depth look at how Cyber AI Analyst investigated an emerging APT threat in the wild, read: Catching APT41 exploiting a zero-day vulnerability.

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 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.

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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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