July 8, 2021

Minimizing the REvil Impact Delivered via Kaseya Servers

Ransomware group REvil recently infiltrated Managed Service Providers for 1,500+ companies. See how Darktrace's autonomous response protected customer data.

Written by

Max Heinemeyer

Global Field CISO

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

Jul 2021

As the USA prepared for a holiday weekend ahead of the Fourth of July, the ransomware group REvil were leveraging a vulnerability in Kaseya software to attack Managed Service Providers (MSPs) and their downstream customers. At least 1,500 companies appear to have been affected, even ones with no direct relationship to Kaseya.

At the time of writing, it appears that a zero-day vulnerability was used to gain access to the Kaseya VSA servers, before deploying ransomware on the endpoints managed by those VSA servers. This modus operandi vastly differs from previous ransomware campaigns which have traditionally been human-operated, direct intrusions.

The analysis below offers Darktrace’s insights into the campaign by looking at a real-life example. It highlights how Self-Learning AI detected the ransomware attack, and how Antigena protected customer data on the network from being encrypted.

Dissecting REvil ransomware from the network perspective

Antigena detected the first signs of ransomware on the network as soon as encryption had begun. The graphic below illustrates the start of the ransomware encryption over SMB shares. When the graphic was taken, the attack was happening live and had never been seen before. As it was a novel threat, Darktrace stopped the network encryption without any static signatures or rules.

*Figure 1: Darktrace detects encryption from the infected device*

The ransomware began to take action at 11:08:32, shown by the ‘SMB Delete Success’ from the infected laptop to an SMB server. While the laptop sometimes reads files on that SMB server, it never deletes these types of files on this particular file share, so Darktrace detected this activity as new and unusual.

Simultaneously, the infected laptop created the ransom note ‘943860t-readme.txt’. Again, the ‘SMB Write Success’ to the SMB server was new activity – and crucially, Darktrace did not look for a static string or a known ransom note. Instead – by previously learning the ‘normal’ behavior of every entity, peer group, and the overall enterprise – it identified that the activity was unusual and new for this organization and device.

By detecting and correlating these subtle anomalies, Darktrace identified this as the earliest stages of ransomware encryption on the network and Antigena took immediate action.

*Figure 2: Snapshot of Antigena’s actions*

Antigena took two precise steps:

Enforce ‘pattern of life’ for five minutes: This prevented the infected laptop from making any connections that were new or unusual. In this case, it prevented any further new SMB encryption activity.
Quarantine device for 24 hours: Usually, Antigena would not take such drastic action, but it was clear that this activity closely resembled ransomware behavior, so Antigena decided to quarantine the device on the network completely to prevent it from doing any further damage.

For several minutes, the infected laptop kept trying to connect to other internal devices via SMB to continue the encryption activity. It was blocked by Antigena at every stage, limiting the spread of the attack and mitigating any damage posed via the network encryption.

On a technical level, Antigena delivered the blocking mechanisms via integrations with native security controls such as existing firewalls, or by taking action itself to disrupt the connections.

The below graphic shows the ‘pattern of life’ for all network connections for the infected laptop. The three red dots represent Darktrace’s detections and pinpoint the exact moment in time when REvil ransomware was installed on the laptop. The graphic also shows an abrupt stop to all network communication as Antigena quarantined the device.

*Figure 4: Network connections from the compromised laptop*

Attacks will always get in

During the incident, part of the encryption happened locally on the endpoint device, which Darktrace had no visibility over. Furthermore, the Internet-facing Kaseya VSA server that was initially compromised was not visible to Darktrace in this case.

Nevertheless, Self-Learning AI detected the infection as soon as it reached the network. This shows the importance of being able to defend against active ransomware within the enterprise. Organizations cannot rely solely on a single layer of defense to keep threats out. An attacker will always – eventually – breach your environment. Defense therefore needs to change its approach towards detecting and mitigating damage once an adversary is inside.

Many cyber-attacks succeed in bypassing endpoint controls and begin to spread aggressively in corporate environments. Autonomous Response can provide resilience in such cases, even for novel campaigns and new strains of malware.

Thanks to Self-Learning AI, ransomware from the REvil attack could not perform any encryption over the network, and files available on that network were saved. This included the organization’s critical file servers which did not have Kaseya installed and thus did not receive the initial payload via the malicious update directly. By interrupting the attack as it happened, Antigena prevented thousands of files on network shares from being encrypted.

Further observations

Data exfiltration

In contrast to other REvil intrusions Darktrace has caught in the past, no data exfiltration has been observed. This is interesting as it differs from the general trend this last year where cyber-criminal groups generally focus more on the exfiltration of data to hold their victims to ransom, in response to companies becoming better with backups.

Bitcoin

REvil has demanded a total payment of $70 million in Bitcoin. For a group that tries to maximize their profits, this seems odd for two reasons:

How do they expect a single entity to collect $70 million from potentially thousands of affected organizations? They must be aware of the massive logistical challenges behind this, even if they do expect Kaseya to act as a focal point for collecting the money.
Since DarkSide lost access to most of the Colonial Pipeline ransom, ransomware groups have shifted to demanding payments in Monero rather than Bitcoin. Monero appears to be more difficult to track for law enforcement agencies. The fact REvil are using Bitcoin, a more traceable cryptocurrency, appears counter-productive to their usual goal of maximizing profits.

Ransomware-as-a-Service (RaaS)

Darktrace also noticed that other, more traditional ‘big game hunting’ REvil ransomware operations took place over the same weekend. This is not surprising as REvil is running a RaaS model, so it is likely some affiliate groups continued their regular big game hunting attacks while the Kaseya supply chain attack was underway.

Unpredictable is not undefendable

The weekend of the Fourth of July experienced major supply chain attacks against Kaseya and separately, against California-based distributor Synnex. Threats are coming from every direction – leveraging zero-days, social engineering tactics, and other advanced tools.

The case study above demonstrates how self-learning technology detects such attacks and minimizes the damage. It functions as a crucial part of defense-in-depth when other layers – such as endpoint protection, threat intelligence or known signatures and rules – fail to detect unknown threats.

The attack happened in milliseconds, faster than any human security team could react. Autonomous Response has proven invaluable in outpacing this new generation of machine-speed threats. It keeps thousands of organizations safe around the world, around the clock, stopping an attack every second.

Darktrace model detections

Compromise / Ransomware / Suspicious SMB Activity
Compromise / Ransomware / Suspicious SMB File Extension
Compromise / Ransomware / Ransom or Offensive Words Written to SMB
Compromise / Ransomware / Ransom or Offensive Words Read from SMB

Written by

Max Heinemeyer

Global Field CISO

Inside the SOC

Written by

Max Heinemeyer

Global Field CISO

•

September 8, 2025

Emma Foulger

Global Threat Research Operations Lead

Product Marketing Manager, Network Detection & Response

Watch the NIS2 Webinar

Blog

Network

September 9, 2025

The benefits of bringing together network and email security

In many organizations, network and email security operate in isolation. Each solution is tasked with defending its respective environment, even though both are facing the same advanced, multi-domain threats.

This siloed approach overlooks a critical reality: email remains the most common vector for initiating cyber-attacks, while the network is the primary stage on which those attacks progress. Without direct integration between these two domains, organizations risk leaving blind spots that adversaries can exploit.

A modern security strategy needs to unify email and network defenses, not just in name, but in how they share intelligence, conduct investigations, and coordinate response actions. Let’s take a look at how this joined-up approach delivers measurable technical, operational, and commercial benefits.

Technical advantages

Pre-alert intelligence: Gathering data before the threat strikes

Most security tools start working when something goes wrong – an unusual login, a flagged attachment, a confirmed compromise. But by then, attackers may already be a step ahead.

By unifying network and email security under a single AI platform (like the Darktrace Active AI Security Platform), you can analyze patterns across both environments in real time, even when there are no alerts. This ongoing monitoring builds a behavioral understanding of every user, device, and domain in your ecosystem.

That means when an email arrives from a suspicious domain, the system already knows whether that domain has appeared on your network before – and whether its behavior has been unusual. Likewise, when new network activity involves a domain first spotted in an email, it’s instantly placed in the right context.

This intelligence isn’t built on signatures or after-the-fact compromise indicators – it’s built on live behavioral baselines, giving your defenses the ability to flag threats before damage is done.

Alert-related intelligence: Connecting the dots in real time

Once an alert does fire, speed and context matter. The Darktrace Cyber AI Analyst can automatically investigate across both environments, piecing together network and email evidence into a single, cohesive incident.

Instead of leaving analysts to sift through fragmented logs, the AI links events like a phishing email to suspicious lateral movement on the recipient’s device, keeping the full attack chain intact. Investigations that might take hours – or even days – can be completed in minutes, with far fewer false positives to wade through.

This is more than a time-saver. It ensures defenders maintain visibility after the first sign of compromise, following the attacker as they pivot into network infrastructure, cloud services, or other targets. That cross-environment continuity is impossible to achieve with disconnected point solutions or siloed workflows.

Operational advantages

Streamlining SecOps across teams

In many organizations, email security is managed by IT, while network defense belongs to the SOC. The result? Critical information is scattered between tools and teams, creating blind spots just when you need clarity.

When email and network data flow into a single platform, everyone is working from the same source of truth. SOC analysts gain immediate visibility into email threats without opening another console or sending a request to another department. The IT team benefits from the SOC’s deeper investigative context.

The outcome is more than convenience: it’s faster, more informed decision-making across the board.

Reducing time-to-meaning and enabling faster response

A unified platform removes the need to manually correlate alerts between tools, reducing time-to-meaning for every incident. Built-in AI correlation instantly ties together related events, guiding analysts toward coordinated responses with higher confidence.

Instead of relying on manual SIEM rules or pre-built SOAR playbooks, the platform connects the dots in real time, and can even trigger autonomous response actions across both environments simultaneously. This ensures attacks are stopped before they can escalate, regardless of where they begin.

Commercial advantages

While purchasing “best-of-breed" for all your different tools might sound appealing, it often leads to a patchwork of solutions with overlapping costs and gaps in coverage. However good a “best-in-breed" email security solution might be in the email realm, it won't be truly effective without visibility across domains and an AI analyst piecing intelligence together. That’s why we think “best-in-suite" is the only “best-in-breed" approach that works – choosing a high-quality platform ensures that every new capability strengthens the whole system.

On top of that, security budgets are under constant pressure. Managing separate vendors for email and network defense means juggling multiple contracts, negotiating different SLAs, and stitching together different support models.

With a single provider for both, procurement and vendor management become far simpler. You deal with one account team, one support channel, and one unified strategy for both environments. If you choose to layer on managed services, you get consistent expertise across your whole security footprint.

Even more importantly, an integrated AI platform sets the stage for growth. Once email and network are under the same roof, adding coverage for other attack surfaces – like cloud or identity – is straightforward. You’re building on the same architecture, not bolting on new point solutions that create more complexity.

Check out the white paper, The Modern Security Stack: Why Your NDR and Email Security Solutions Need to Work Together, to explore these benefits in more depth, with real-world examples and practical steps for unifying your defenses.

[related-resource]

About the author

Mikey Anderson

Product Marketing Manager, Network Detection & Response

Blog

September 9, 2025

Unpacking the Salesloft Incident: Insights from Darktrace Observations

Introduction

On August 26, 2025, Google Threat intelligence Group released a report detailing a widespread data theft campaign targeting the sales automation platform Salesloft, via compromised OAuth tokens used by the third-party Drift AI chat agent [1][2]. The attack has been attributed to the threat actor UNC6395 by Google Threat Intelligence and Mandiant [1].

The attack is believed to have begun in early August 2025 and continued through until mid-August 2025 [1], with the threat actor exporting significant volumes of data from multiple Salesforce instances [1]. Then sifting through this data for anything that could be used to compromise the victim’s environments such as access keys, tokens or passwords. This had led to Google Threat Intelligence Group assessing that the primary intent of the threat actor is credential harvesting, and later reporting that it was aware of in excess of 700 potentially impacted organizations [3].

Salesloft previously stated that, based on currently available data, customers that do not integrate with Salesforce are unaffected by this campaign [2]. However, on August 28, Google Threat Intelligence Group announced that “Based on new information identified by GTIG, the scope of this compromise is not exclusive to the Salesforce integration with Salesloft Drift and impacts other integrations” [2]. Google Threat Intelligence has since advised that any and all authentication tokens stored in or connected to the Drift platform be treated as potentially compromised [1].

This campaign demonstrates how attackers are increasingly exploiting trusted Software-as-a-Service (SaaS) integrations as a pathway into enterprise environment.

By abusing these integrations, threat actors were able to exfiltrate sensitive business data at scale, bypassing traditional security controls. Rather than relying on malware or obvious intrusion techniques, the adversaries leveraged legitimate credentials and API traffic that resembled legitimate Salesforce activity to achieve their goals. This type of activity is far harder to detect with conventional security tools, since it blends in with the daily noise of business operations.

The incident underscores the escalating significance of autonomous coverage within SaaS and third-party ecosystems. As businesses increasingly depend on interconnected platforms, visibility gaps become evident that cannot be managed by conventional perimeter and endpoint defenses.

By developing a behavioral comprehension of each organization's distinct use of cloud services, anomalies can be detected, such as logins from unexpected locations, unusually high volumes of API requests, or unusual document activity. These indications serve as an early alert system, even when intruders use legitimate tokens or accounts, enabling security teams to step in before extensive data exfiltration takes place

What happened?

The campaign is believed to have started on August 8, 2025, with malicious activity continuing until at least August 18. The threat actor, tracked as UNC6395, gained access via compromised OAuth tokens associated with Salesloft Drift integrations into Salesforce [1]. Once tokens were obtained, the attackers were able to issue large volumes of Salesforce API requests, exfiltrating sensitive customer and business data.

Initial Intrusion

The attackers first established access by abusing OAuth and refresh tokens from the Drift integration. These tokens gave them persistent access into Salesforce environments without requiring further authentication [1]. To expand their foothold, the threat actor also made use of TruffleHog [4], an open-source secrets scanner, to hunt for additional exposed credentials. Logs later revealed anomalous IAM updates, including unusual UpdateAccessKey activity, which suggested attempts to ensure long-term persistence and control within compromised accounts.

Internal Reconnaissance & Data Exfiltration

Once inside, the adversaries began exploring the Salesforce environments. They ran queries designed to pull sensitive data fields, focusing on objects such as Cases, Accounts, Users, and Opportunities [1]. At the same time, the attackers sifted through this information to identify secrets that could enable access to other systems, including AWS keys and Snowflake credentials [4]. This phase demonstrated the opportunistic nature of the campaign, with the actors looking for any data that could be repurposed for further compromise.

Lateral Movement

Salesloft and Mandiant investigations revealed that the threat actor also created at least one new user account in early September. Although follow-up activity linked to this account was limited, the creation itself suggested a persistence mechanism designed to survive remediation efforts. By maintaining a separate identity, the attackers ensured they could regain access even if their stolen OAuth tokens were revoked.

Accomplishing the mission

The data taken from Salesforce environments included valuable business records, which attackers used to harvest credentials and identify high-value targets. According to Mandiant, once the data was exfiltrated, the actors actively sifted through it to locate sensitive information that could be leveraged in future intrusions [1]. In response, Salesforce and Salesloft revoked OAuth tokens associated with Drift integrations on August 20 [1], a containment measure aimed at cutting off the attackers’ primary access channel and preventing further abuse.

How did the attack bypass the rest of the security stack?

The campaign effectively bypassed security measures by using legitimate credentials and OAuth tokens through the Salesloft Drift integration. This rendered traditional security defenses like endpoint protection and firewalls ineffective, as the activity appeared non-malicious [1]. The attackers blended into normal operations by using common user agents and making queries through the Salesforce API, which made their activity resemble legitimate integrations and scripts. This allowed them to operate undetected in the SaaS environment, exploiting the trust in third-party connections and highlighting the limitations of traditional detection controls.

Darktrace Coverage

Anomalous activities have been identified across multiple Darktrace deployments that appear associated with this campaign. This included two cases on customers based within the United States who had a Salesforce integration, where the pattern of activities was notably similar.

On August 17, Darktrace observed an account belonging to one of these customers logging in from the rare endpoint 208.68.36[.]90, while the user was seen active from another location. This IP is a known indicator of compromise (IoC) reported by open-source intelligence (OSINT) for the campaign [2].

Figure 1: Cyber AI Analyst Incident summarizing the suspicious login seen for the account.

The login event was associated with the application Drift, further connecting the events to this campaign.

Figure 2: Advanced Search logs showing the Application used to login.

Following the login, the actor initiated a high volume of Salesforce API requests using methods such as GET, POST, and DELETE. The GET requests targeted endpoints like /services/data/v57.0/query and /services/data/v57.0/sobjects/Case/describe, where the former is used to retrieve records based on a specific criterion, while the latter provides metadata for the Case object, including field names and data types [5,6].

Subsequently, a POST request to /services/data/v57.0/jobs/query was observed, likely to initiate a Bulk API query job for extracting large volumes of data from the Ingest Job endpoint [7,8].

Finally, a DELETE request to remove an ingestion job batch, possibly an attempt to obscure traces of prior data access or manipulation.

A case on another US-based customer took place a day later, on August 18. This again began with an account logging in from the rare IP 208.68.36[.]90 involving the application Drift. This was followed by Salesforce GET requests targeting the same endpoints as seen in the previous case, and then a POST to the Ingest Job endpoint and finally a DELETE request, all occurring within one minute of the initial suspicious login.

The chain of anomalous behaviors, including a suspicious login and delete request, resulted in Darktrace’s Autonomous Response capability suggesting a ‘Disable user’ action. However, the customer’s deployment configuration required manual confirmation for the action to take effect.

Figure 3: An example model alert for the user, triggered due to an anomalous API DELETE request.

Figure 4: Model Alert Event Log showing various model alerts for the account that ultimately led to an Autonomous Response model being triggered.

Conclusion

In conclusion, this incident underscores the escalating risks of SaaS supply chain attacks, where third-party integrations can become avenues for attacks. It demonstrates how adversaries can exploit legitimate OAuth tokens and API traffic to circumvent traditional defenses. This emphasizes the necessity for constant monitoring of SaaS and cloud activity, beyond just endpoints and networks, while also reinforcing the significance of applying least privilege access and routinely reviewing OAuth permissions in cloud environments. Furthermore, it provides a wider perspective into the evolution of the threat landscape, shifting towards credential and token abuse as opposed to malware-driven compromise.

‍

Credit to Emma Foulger (Global Threat Research Operations Lead), Calum Hall (Technical Content Researcher), Signe Zaharka (Principal Cyber Analyst), Min Kim (Senior Cyber Analyst), Nahisha Nobregas (Senior Cyber Analyst), Priya Thapa (Cyber Analyst)

Appendices

Darktrace Model Detections

· SaaS / Access / Unusual External Source for SaaS Credential Use

· SaaS / Compromise / Login From Rare Endpoint While User Is Active

· SaaS / Compliance / Anomalous Salesforce API Event

· SaaS / Unusual Activity / Multiple Unusual SaaS Activities

· Antigena / SaaS / Antigena Unusual Activity Block

· Antigena / SaaS / Antigena Suspicious Source Activity Block

Customers should consider integrating Salesforce with Darktrace where possible. These integrations allow better visibility and correlation to spot unusual behavior and possible threats.