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June 7, 2020

How Darktrace AI Identified Microsoft 365 Breaches

We cover two real cases on how Darktrace stopped Microsoft 365 account takeovers by correlating insights across SaaS applications & email activity.
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
Dan Fein
VP, Product
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07
Jun 2020

Social engineering’. ‘Credential theft’. ‘Account takeover’. If you were a fly on the wall of a Security Operations Center in 2020, you would have heard these phrases far more often than ‘banking trojan’, ‘SQL injection’ or ‘exploit kit’. The reason for this is simple – the reality for most security teams now is that their perimeter has shifted into the cloud. Identities are being attacked more than devices.

Microsoft 365 account compromise’ is the current favorite, with 29% of organizations reporting a related incident in one month alone. Security teams struggle with these attacks because the evidence needed to detect them is scattered across the enterprise: they begin via email, are executed over the network, and progress in the cloud. This broad and spread out digital footprint means that following the breadcrumbs is not easy.

Darktrace’s Cyber AI Platform is designed to understand a user’s behavior as they move between devices and cloud services, tracking their activity to identify a compromise. To help understand how these attacks avoid detection, it is useful to look at a couple of examples of Microsoft Office 365 compromise detected recently in one of our customers.

Microsoft 365 compromised to launch external email threat

A Microsoft 365 account was recently compromised at a public accounting firm based in the United States. Darktrace initially picked up on several anomalies, including a sudden surge in outbound email traffic as well as the unusual login location – while the company and nearly all of its users were located in Wisconsin, an IP address located in Kansas was used to log in to the Microsoft 365 account. Along with the unusual login, a login to Microsoft Teams from the same Kansas IP address was detected.

Figure 1: Just after the new email rule was created, a Microsoft Teams 100% rare IP login occurred.

‘Impossible travel’ rules alone would have missed these anomalies, but an understanding of activity and behavior across different SaaS applications allowed Darktrace’s AI to recognize these events as one systematic case of credential theft. When the threat-actor subsequently created a new email rule, Darktrace was able to connect this event with the other anomalous behavior and understand its potentially malicious nature.

Figure 2: Darktrace’s SaaS Module noted a 100% rare IP logging into the user’s Microsoft 365 account and the creation of a new mailbox rules. All factors indicated 100% unusual SaaS activity.

Five minutes later, Antigena Email alerted on a large number of outbound emails containing a generic subject line and an attached PDF. The technology also detected that there was a clear spike in outbound emails from this user and flagged each of these emails with the “Out of Character” tag, which in this case denoted a change from normal behavior with the surge in recipients, and likely internal compromise.

Figure 3: Antigena Email detected a surge in recipients that indicated a serious breach of normal behavior for this user.

The unusual login behavior detected by Darktrace’s SaaS Module could be connected to the anomalous outbound email behavior flagged by Antigena Email, allowing the security team to see the extent of the attack and neutralize it as it emerged. It was clear that the account was being used to engage in malicious activity, as each of the 220 outbound emails used a generic subject line and contained a suspicious attachment. The security team therefore immediately disabled the compromised account.

Figure 4: A recreation of the email sent by the attacker, containing the malicious attachment.

‘Change of bank details’ sent from accounts department

When an Accounts Department’s Microsoft 365 account was compromised and used to send targeted phishing emails, Darktrace was able to track the attacker’s movement within the inbox, tying together information from Darktrace’s SaaS Module with Antigena Email’s alerts to understand the full picture of the threat and stop the attack.

The SaaS account appears to have been compromised via an inbound spear phishing attack, or some other form of attack that occurred before Darktrace began monitoring the organization. While Darktrace Cyber AI had no oversight of the initial compromise, it was still able to distinguish later attacker behavior as malicious, based on its actively evolving understanding of the organization and its workforce.

When the account user logged in from a 100% rare French IP address, Darktrace’s SaaS Module picked up on the anomaly immediately, and further detected a series of activities carried out after the unusual login. At the same time, Antigena Email noted an email being sent.

Figure 5: The login from a French IP was noted as 100% rare for this user and SaaS account.

Darktrace then identified more activity occurring from a second rare login location, a Swiss IP address. Very little email activity occurred when the account was logged in from this IP. Instead, Cyber AI saw the threat-actor using their illegitimate SaaS access to view information on the legitimate account user and files related to banking, invoices, and payments.

Antigena Email then identified a series of email communications that, when seen in the context of the SaaS account compromise, pointed to a clear threat. There were no obvious malicious attachments or links in the emails. However, the subject of the final reply was ‘Change of Bank Details’, and the email prompted a high Solicitation Inducement Score within Antigena Email, strongly implying that the malicious actor had sent emails instructing the destination to change payment details in order to route money to the attacker, instead of the company.

It seems the attackers went through the banking and invoicing files in order to find a customer with a big bill to pay, then used the compromised email account to launch an outbound phishing attack, changing the billing details. With Darktrace AI correlating information within the SaaS platform and insights from Antigena Email, this targeted phishing attack could be contained before further compromise or damage could occur.

The below screenshot also indicates a series of inbox processing rules made on the compromised account, showing actions that are typical of an account takeover.

Figure 6: Darktrace’s records of new inbox rules being set up on the compromised SaaS account.

The benefits of a unified approach

These stories are all too familiar. Most security tools would not be able to take action on any one of these steps individually. But the combination reveals the tell-tale sign of a Microsoft 365 account hijack. Organizations are struggling to manage their user identities across their cloud infrastructure, and rule and policy-based detection is no longer feasible.

However, by learning identities and behavior across the enterprise, Darktrace is able to detect, and seamlessly respond, to combat these threats. Hundreds of organizations are now using Antigena Email to protect their email and cloud environments continuously, trusting it to dynamically enforce MFA, lock accounts, block network traffic, and withhold emails when necessary.

As cloud-native applications become more popular, organizations face the growing problem of separate end-to-end security solutions for each type of workload. With Antigena Email working in conjunction with Darktrace’s Enterprise Immune System, defenders can be assured that a single, unified platform is tracking every suspicious behavior, wherever it arises in the organization.

Learn more about Antigena Email

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
Dan Fein
VP, Product

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November 19, 2025

Securing Generative AI: Managing Risk in Amazon Bedrock with Darktrace / CLOUD

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Security risks and challenges of generative AI in the enterprise

Generative AI and managed foundation model platforms like Amazon Bedrock are transforming how organizations build and deploy intelligent applications. From chatbots to summarization tools, Bedrock enables rapid agent development by connecting foundation models to enterprise data and services. But with this flexibility comes a new set of security challenges, especially around visibility, access control, and unintended data exposure.

As organizations move quickly to operationalize generative AI, traditional security controls are struggling to keep up. Bedrock’s multi-layered architecture, spanning agents, models, guardrails, and underlying AWS services, creates new blind spots that standard posture management tools weren’t designed to handle. Visibility gaps make it difficult to know which datasets agents can access, or how model outputs might expose sensitive information. Meanwhile, developers often move faster than security teams can review IAM permissions or validate guardrails, leading to misconfigurations that expand risk. In shared-responsibility environments like AWS, this complexity can blur the lines of ownership, making it critical for security teams to have continuous, automated insight into how AI systems interact with enterprise data.

Darktrace / CLOUD provides comprehensive visibility and posture management for Bedrock environments, automatically detecting and proactively scanning agents and knowledge bases, helping teams secure their AI infrastructure without slowing down expansion and innovation.

A real-world scenario: When access goes too far

Consider a scenario where an organization deploys a Bedrock agent to help internal staff quickly answer business questions using company knowledge. The agent was connected to a knowledge base pointing at documents stored in Amazon S3 and given access to internal services via APIs.

To get the system running quickly, developers assigned the agent a broad execution role. This role granted access to multiple S3 buckets, including one containing sensitive customer records. The over-permissioning wasn’t malicious; it stemmed from the complexity of IAM policy creation and the difficulty of identifying which buckets held sensitive data.

The team assumed the agent would only use the intended documents. However, they did not fully consider how employees might interact with the agent or how it might act on the data it processed.  

When an employee asked a routine question about quarterly customer activity, the agent surfaced insights that included regulated data, revealing it to someone without the appropriate access.

This wasn’t a case of prompt injection or model manipulation. The agent simply followed instructions and used the resources it was allowed to access. The exposure was valid under IAM policy, but entirely unintended.

How Darktrace / CLOUD prevents these risks

Darktrace / CLOUD helps organizations avoid scenarios like unintended data exposure by providing layered visibility and intelligent analysis across Bedrock and SageMaker environments. Here’s how each capability works in practice:

Configuration-level visibility

Bedrock deployments often involve multiple components: agents, guardrails, and foundation models, each with its own configuration. Darktrace / CLOUD indexes these configurations so teams can:

  1. Inspect deployed agents and confirm they are connected only to approved data sources.
  2. Track evaluation job setups and their links to Amazon S3 datasets, uncovering hidden data flows that could expose sensitive information.
  3. Maintain full awareness of all AI components, reducing the chance of overlooked assets introducing risk.

By unifying configuration data across Bedrock, SageMaker, and other AWS services, Darktrace / CLOUD provides a single source of truth for AI asset visibility. Teams can instantly see how each component is configured and whether it aligns with corporate security policies. This eliminates guesswork, accelerates audits, and helps prevent misaligned settings from creating data exposure risks.

 Agents for bedrock relationship views.
Figure 1: Agents for bedrock relationship views

Architectural awareness

Complex AI environments can make it difficult to understand how components interact. Darktrace / CLOUD generates real-time architectural diagrams that:

  1. Visualize relationships between agents, models, and datasets.
  1. Highlight unintended data access paths or risk propagation across interconnected services.

This clarity helps security teams spot vulnerabilities before they lead to exposure. By surfacing these relationships dynamically, Darktrace / CLOUD enables proactive risk management, helping teams identify architectural drift, redundant data connections, or unmonitored agents before attackers or accidental misuse can exploit them. This reduces investigation time and strengthens compliance confidence across AI workloads.

Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping
Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping

Access & privilege analysis

IAM permissions apply to every AWS service, including Bedrock. When Bedrock agents assume IAM roles that were broadly defined for other workloads, they often inherit excessive privileges. Without strict least-privilege controls, the agent may have access to far more data and services than required, creating avoidable security exposure. Darktrace / CLOUD:

  1. Reviews execution roles and user permissions to identify excessive privileges.
  2. Flags anomalies that could enable privilege escalation or unauthorized API actions.

This ensures agents operate within the principle of least privilege, reducing attack surface. Beyond flagging risky roles, Darktrace / CLOUD continuously learns normal patterns of access to identify when permissions are abused or expanded in real time. Security teams gain context into why an action is anomalous and how it could affect connected assets, allowing them to take targeted remediation steps that preserve productivity while minimizing exposure.

Misconfiguration detection

Misconfigurations are a leading cause of cloud security incidents. Darktrace / CLOUD automatically detects:

  1. Publicly accessible S3 buckets that may contain sensitive training data.
  2. Missing guardrails in Bedrock deployments, which can allow inappropriate or sensitive outputs.
  3. Other issues such as lack of encryption, direct internet access, and root access to models.  

By surfacing these risks early, teams can remediate before they become exploitable. Darktrace / CLOUD turns what would otherwise be manual reviews into automated, continuous checks, reducing time to discovery and preventing small oversights from escalating into full-scale incidents. This automated assurance allows organizations to innovate confidently while keeping their AI systems compliant and secure by design.

Configuration data for Anthropic foundation model
Figure 3: Configuration data for Anthropic foundation model

Behavioral anomaly detection

Even with correct configurations, behavior can signal emerging threats. Using AWS CloudTrail, Darktrace / CLOUD:

  1. Monitors for unusual data access patterns, such as agents querying unexpected datasets.
  2. Detects anomalous training job invocations that could indicate attempts to pollute models.

This real-time behavioral insight helps organizations respond quickly to suspicious activity. Because it learns the “normal” behavior of each Bedrock component over time, Darktrace / CLOUD can detect subtle shifts that indicate emerging risks, before formal indicators of compromise appear. The result is faster detection, reduced investigation effort, and continuous assurance that AI-driven workloads behave as intended.

Conclusion

Generative AI introduces transformative capabilities but also complex risks that evolve alongside innovation. The flexibility of services like Amazon Bedrock enables new efficiencies and insights, yet even legitimate use can inadvertently expose sensitive data or bypass security controls. As organizations embrace AI at scale, the ability to monitor and secure these environments holistically, without slowing development, is becoming essential.

By combining deep configuration visibility, architectural insight, privilege and behavior analysis, and real-time threat detection, Darktrace gives security teams continuous assurance across AI tools like Bedrock and SageMaker. Organizations can innovate with confidence, knowing their AI systems are governed by adaptive, intelligent protection.

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Adam Stevens
Senior Director of Product, Cloud | Darktrace

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November 19, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

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What is Vo1d APK malware?

Vo1d malware first appeared in the wild in September 2024 and has since evolved into one of the most widespread Android botnets ever observed. This large-scale Android malware primarily targets smart TVs and low-cost Android TV boxes. Initially, Vo1d was identified as a malicious backdoor capable of installing additional third-party software [1]. Its functionality soon expanded beyond the initial infection to include deploying further malicious payloads, running proxy services, and conducting ad fraud operations. By early 2025, it was estimated that Vo1d had infected 1.3 to 1.6 million devices worldwide [2].

From a technical perspective, Vo1d embeds components into system storage to enable itself to download and execute new modules at any time. External researchers further discovered that Vo1d uses Domain Generation Algorithms (DGAs) to create new command-and-control (C2) domains, ensuring that regardless of existing servers being taken down, the malware can quickly reconnect to new ones. Previous published analysis identified dozens of C2 domains and hundreds of DGA seeds, along with new downloader families. Over time, Vo1d has grown increasingly sophisticated with clear signs of stronger obfuscation and encryption methods designed to evade detection [2].

Darktrace’s coverage

Earlier this year, Darktrace observed a surge in Vo1d-related activity across customer environments, with the majority of affected customers based in South Africa. Devices that had been quietly operating as expected began exhibiting unusual network behavior, including excessive DNS lookups. Open-source intelligence (OSINT) has long highlighted South Africa as one of the countries most impacted by Vo1d infections [2].

What makes the recent activity particularly interesting is that the surge observed by Darktrace appears to be concentrated specifically in South African environments. This localized spike suggests that a significant number of devices may have been compromised, potentially due to vulnerable software, outdated firmware, or even preloaded malware. Regions with high prevalence of low-cost, often unpatched devices are especially susceptible, as these everyday consumer electronics can be quietly recruited into the botnet’s network. This specifically appears to be the case with South Africa, where public reporting has documented widespread use of low-cost boxes, such as non-Google-certified Android TV sticks, that frequently ship with outdated firmware [3].

The initial triage highlighted the core mechanism Vo1d uses to remain resilient: its use of DGA. A DGA deterministically creates a large list of pseudo-random domain names on a predictable schedule. This enables the malware to compute hundreds of candidate domains using the same algorithm, instead of using a hard-coded single C2 hostname that defenders could easily block or take down. To ensure reproducible from the infected device’s perspective, Vo1d utilizes DGA seeds. These seeds might be a static string, a numeric value, or a combination of underlying techniques that enable infected devices to generate the same list of candidate domains for a time window, provided the same DGA code, seed, and date are used.

Interestingly, Vo1d’s DGA seeds do not appear to be entirely unpredictable, and the generated domains lack fully random-looking endings. As observed in Figure 1, there is a clear pattern in the names generated. In this case, researchers identified that while the first five characters would change to create the desired list of domain names, the trailing portion remained consistent as part of the seed: 60b33d7929a, which OSINT sources have linked to the Vo1d botnet. [2]. Darktrace’s Threat Research team also identified a potential second DGA seed, with devices in some cases also engaging in activity involving hostnames matching the regular expression /[a-z]{5}fc975904fc9\.(com|top|net). This second seed has not been reported by any OSINT vendors at the time of writing.

Another recurring characteristic observed across multiple cases was the choice of top-level domains (TLDs), which included .com, .net, and .top.

Figure 1: Advanced Search results showing DNS lookups, providing a glimpse on the DGA seed utilized.

The activity was detected by multiple models in Darktrace / NETWORK™, which triggered on devices making an unusually large volume of DNS requests for domains uncommon across the network.

During the network investigation, Darktrace analysts traced Vo1d’s infrastructure and uncovered an interesting pattern related to responder ASNs. A significant number of connections pointed to AS16509 (AMAZON-02). By hosting redirectors or C2 nodes inside major cloud environments, Vo1d is able to gain access to highly available and geographically diverse infrastructure. When one node is taken down or reported, operators can quickly enable a new node under a different IP within the same ASN. Another feature of cloud infrastructure that hardens Vo1d’s resilience is the fact that many organizations allow outbound connections to cloud IP ranges by default, assuming they are legitimate. Despite this, Darktrace was able to identify the rarity of these endpoints, identifying the unusualness of the activity.

Analysts further observed that once a generated domain successfully resolved, infected devices consistently began establishing outbound connections to ephemeral port ranges like TCP ports 55520 and 55521. These destination ports are atypical for standard web or DNS traffic. Even though the choice of high-numbered ports appears random, it is likely far from not accidental. Commonly used ports such as port 80 (HTTP) or 443 (HTTPS) are often subject to more scrutiny and deeper inspection or content filtering, making them riskier for attackers. On the other hand, unregistered ports like 55520 and 55521 are less likely to be blocked, providing a more covert channel that blends with outbound TCP traffic. This tactic helps evade firewall rules that focus on common service ports. Regardless, Darktrace was able to identify external connections on uncommon ports to locations that the network does not normally visit.

The continuation of the described activity was identified by Darktrace’s Cyber AI Analyst, which correlated individual events into a broader interconnected incident. It began with the multiple DNS requests for the algorithmically generated domains, followed by repeated connections to rare endpoints later confirmed as attacker-controlled infrastructure. Cyber AI Analyst’s investigation further enabled it to categorize the events as part of the “established foothold” phase of the attack.

Figure 2: Cyber AI Analyst incident illustrating the transition from DNS requests for DGA domains to connections with resolved attacker-controlled infrastructure.

Conclusion

The observations highlighted in this blog highlight the precision and scale of Vo1d’s operations, ranging from its DGA-generated domains to its covert use of high-numbered ports. The surge in affected South African environments illustrate how regions with many low-cost, often unpatched devices can become major hubs for botnet activity. This serves as a reminder that even everyday consumer electronics can play a role in cybercrime, emphasizing the need for vigilance and proactive security measures.

Credit to Christina Kreza (Cyber Analyst & Team Lead) and Eugene Chua (Principal Cyber Analyst & Team Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

  • Anomalous Connection / Devices Beaconing to New Rare IP
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / DGA Beacon
  • Compromise / Domain Fluxing
  • Compromise / Fast Beaconing to DGA
  • Unusual Activity / Unusual External Activity

List of Indicators of Compromise (IoCs)

  • 3.132.75[.]97 – IP address – Likely Vo1d C2 infrastructure
  • g[.]sxim[.]me – Hostname – Likely Vo1d C2 infrastructure
  • snakeers[.]com – Hostname – Likely Vo1d C2 infrastructure

Selected DGA IoCs

  • semhz60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • ggqrb60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • eusji60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • uacfc60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • qilqxfc975904fc9[.]top – Hostname – Possible Vo1d C2 DGA endpoint

MITRE ATT&CK Mapping

  • T1071.004 – Command and Control – DNS
  • T1568.002 – Command and Control – Domain Generation Algorithms
  • T1568.001 – Command and Control – Fast Flux DNS
  • T1571 – Command and Control – Non-Standard Port

[1] https://news.drweb.com/show/?lng=en&i=14900

[2] https://blog.xlab.qianxin.com/long-live-the-vo1d_botnet/

[3] https://mybroadband.co.za/news/broadcasting/596007-warning-for-south-africans-using-specific-types-of-tv-sticks.html

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content.

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About the author
Christina Kreza
Cyber Analyst
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