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November 21, 2022

How Darktrace Finds Misconfigurations

Explore Darktrace’s strategies for preventing IT misconfigurations. Our blog provides actionable insights and use cases.
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
Carlos Gray
Senior Product Marketing Manager, Email
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21
Nov 2022

During an initial demo with a water management company, Darktrace found an industrial control system exposed to the internet. Immediately, the organization went into incident response mode because this system was mission critical and could potentially impact the water facilities, as it had the power to adjust water flow.

This asset was exposed because of a simple misconfiguration, highlighting to the customer the need for proactive monitoring of its attack surface. In this case, the engineer who set up the system had simply not noticed the mistake, but these scenarios could be more dangerous and more likely if insider threat is involved.  

The growing threat of misconfigurations in cybersecurity

Misconfigurations arise when vital security settings are either not applied or applied incorrectly. Such misconfigurations produce vulnerable security openings that can be exploited by attackers to either gain a foothold in the asset or generate a more dangerous attack, like altering water flow or deploying ransomware. There is a wide variety of assets that are subject to potential misconfigurations, including web or application servers, cloud containers, custom code, network devices like desktops or servers, and entire databases.  

Unfortunately, the pervasiveness of misconfigurations is only increasing. In the past 12 months, there has been a 310% increase in hackers reporting misconfiguration vulnerabilities to the HackerOne platform.  

Unique risks for industrial control systems and critical infrastructure

Every digital environment has its own characteristics that alter the prevalence or the impact of misconfigurations. For example, industrial devices that support critical infrastructure are all the more sensitive to these types of changes, as these devices often have limited integrated security, despite their highly delicate functions. Because with every vendor and device has its own recommended configuration, security teams must take additional precautions.  

In cloud environments, the ease of deployment and increased capabilities also tend to produce more misconfigurations. Digital footprints are growing at such a pace that security departments may opt to skip onboarding processes of technologies to avoid becoming an obstacle for the business. It has become so easy for any department, regardless of their technical knowledge, to add cloud applications, software, or even hardware to the company's architecture. This is why shadow IT is so troublesome: it's impossible for the security team to ensure something is well configured if they don’t even know it exists.  

In addition, due to rapid growth, security and IT teams aren’t experts in every technology included within the enterprise architecture. So, the teams may do their best to apply security controls while being unaware the current configuration is a misconfiguration. With digital assets’ constant evolution, they may even be configured correctly at one point but become misconfigured in the future if not updated.  

Mitigating Misconfiguration

It’s human nature that we make mistakes, and the more assets and third parties that are introduced, the more mistakes are possible. However, there are certain steps organizations can take towards reducing the frequency and the impact of misconfigurations.  

Any organization needs to have discovery processes to maintain an updated inventory of their assets, and should categorize these assets based on their exposure as well as their criticality to the business. This information should feed into the organization’s risk analysis, which in turn informs the priority of mitigation actions or controls. This process, when done manually, can be long and arduous, and is not continuous: as organizations’ digital footprints are evolving so rapidly, these analyses can become obsolete quickly.  

On the other hand, organizations must also monitor the activity of these assets and not just assess them at face value. As with anything in security, security teams need to be weary of the symptoms. Inappropriate configurations will often generate alerts such as slow performance, multiple suspicious login attempts, bloatware, unexpected application behavior such as redirects or shutdowns.  

Misconfigurations are easier to identify, prioritize and remediate with an AI solution that provides continuous analysis of the organization’s external and internal attack surface. Darktrace / Proactive Exposure Management and Darktrace / Attack Surface Management achieve exactly this.  

Managing external assets

With ASM, security teams gain visibility of the entire external attack surface, including elusive assets like shadow IT and legacy devices. It frequently uncovers misconfigurations and recommends how to mitigate the risks caused by them. Some examples include email spoofing, no SPF records, no DKIM records, no DMARC records, subdomain takeover possible, and missing routes for netblocks.  

The truly unique aspect of a Self-Learning technology is that security teams receive notifications tailored to the precise assets within their architectures. In other words, the tool will only provide the misconfiguration recommendations for the specific assets that require it, instead of having to reverse engineer state-of-the-art security and then trying to see where it can apply within the organization. With Darktrace, security teams are already getting that information directly. In fact, it doesn’t stop there, misconfigurations are prirotized by the risk inherited. The security team only has to check the list of misconfigurations in order of priority and take action on them.  

Exposure management & attack paths

From an internal perspective, Darktrace / Proactive Exposure Management will map those misconfigurations to potential attack paths, answering the question of what damage each misconfiguration can lead to and more importantly how: an attacker could go from that initial misconfiguration through each lateral movement, whether it is via a device or a user, and then reach the most critical devices within the infrastructure.

Often in security, the focus can drift to the latest tactics and techniques being used by large Advanced Persistent Threats, but a simple misconfiguration caused by a rushed or distracted employee can pose an equally large threat. An innocent mistake can often open an even larger weakness in the digital architecture, as the attacker doesn’t have to force to open the window to break in.

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
Carlos Gray
Senior Product Marketing Manager, Email

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Identity

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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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Proactive Security

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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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