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May 6, 2020

The Ongoing Threat of Dharma Ransomware Attacks

Stay informed about the dangers of Dharma ransomware and its methods of attack, ensuring your defenses are strong against potential intrusions.
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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06
May 2020

Executive summary

  • In the past few weeks, Darktrace has observed an increase in attacks against internet-facing systems, such as RDP. The initial intrusions usually take place via existing vulnerabilities or stolen, legitimate credentials. The Dharma ransomware attack described in this blog post is one such example.
  • Old threats can be damaging – Dharma and its variants have been around for four years. This is a classic example of ‘legacy’ ransomware morphing and adapting to bypass traditional defenses.
  • The intrusion shows signs that indicate the threat-actors are aware of – and are actively exploiting – the COVID-19 situation.
  • In the current threat landscape surrounding COVID-19, Darktrace recommends monitoring internet-facing systems and critical servers closely – keeping track of administrative credentials and carefully considering security when rapidly deploying internet-facing infrastructure.

Introduction

In mid-April, Darktrace detected a targeted Dharma ransomware attack on a UK company. The initial point of intrusion was via RDP – this represents a very common attack method of infection that Darktrace has observed in the broader threat landscape over the past few weeks.

This blog post highlights every stage of the attack lifecycle and details the attacker’s techniques, tools and procedures (TTP) – all detected by Darktrace.

Dharma – a varient of the CrySIS malware family – first appeared in 2016 and uses multiple intrusion vectors. It distributes its malware as an attachment in a spam email, by disguising it as an installation file for legitimate software, or by exploiting an open RDP connection through internet-facing servers. When Dharma has finished encrypting files, it drops a ransom note with the contact email address in the encrypted SMB files.

Darktrace had strong, real-time detections of the attack – however the absence of eyes on the user interface prior to the encryption activity, and without Autonomous Response deployed in Active Mode, these alerts were only actioned after the ransomware was unleashed. Fortunately, it was unable to spread within the organization, thanks to human intervention at the peak of the attack. However, Darktrace Antigena in active mode would have significantly slowed down the attack.

Timeline

The timeline below provides a rough overview of the major attack phases over five days of activity.

Figure 1: A timeline of the attack

Technical analysis

Darktrace detected that the main device hit by the attack was an internet-facing RDP server (‘RDP server’). Dharma used network-level encryption here: the ransomware activity takes place over the network protocol SMB.

Below is a chronological overview of all Darktrace detections that fired during this attack: Darktrace detected and reported every single unusual or suspicious event occurring on the RDP server.

Figure 2: An overview of Darktrace detections

Initial compromise

On April 7, the RDP server began receiving a large number of incoming connections from rare IP addresses on the internet.

On April 7, the RDP server began receiving a large number of incoming connections from rare IP addresses on the internet. This means a lot of IP addresses on the internet that usually don’t connect to this company started connection attempts over RDP. The top five cookies used to authenticate show that the source IPs were located in Russia, the Netherlands, Korea, the United States, and Germany.

It is highly likely that the RDP credential used in this attack had been compromised prior to the attack – either via common brute-force methods, credential stuffing attacks, or phishing. Indeed, a TTP growing in popularity is to buy RDP credentials on marketplaces and skip to initial access.

Attempted privilege escalation

The following day, the malicious actor abused the SMB version 1 protocol, notorious for always-on null sessions which offer unauthenticated users’ information about the machine – such as password policies, usernames, group names, machine names, user and host SIDs. What followed was very unusual: the server connected externally to a rare IP address located in Morocco.

Next, the attacker attempted a failed SMB session to the external IP over an unusual port. Darktrace detected this activity as highly anomalous, as it had previously learned that SMB is usually not used in this fashion within this organization – and certainly not for external communication over this port.

Figure 3: Darktrace detecting the rare external IP address

Figure 4: The SMB session failure and the rare connection over port 1047

Command and control traffic

As the entire attack occurred over five days, this aligns with a smash-and-grab approach, rather than a highly covert, low-and-slow operation.

Two hours later, the server initiated a large number of anomalous and rare connections to external destinations located in India, China, and Italy – amongst other destinations the server had never communicated with before. The attacker was now attempting to establish persistence and create stronger channels for command and control (C2). As the entire attack occurred over five days, this aligns with a smash-and-grab approach, rather than a highly covert, low-and-slow operation.

Actions on target

Notwithstanding this approach, the malicious actor remained dormant for two days, biding their time until April 10 — a public holiday in the UK — when security teams would be notably less responsive. This pause in activity provides supporting evidence that the attack was human-driven.

Figure 5: The unusual RDP connections detected by Darktrace

The RDP server then began receiving incoming remote desktop connections from 100% rare IP addresses located in the Netherlands, Latvia, and Poland.

Internal reconnaissance

The IP address 85.93.20[.]6, hosted at the time of investigation in Panama, made two connections to the server, using an administrative credential. On April 12, as other inbound RDP connections scanned the network, the volume of data transferred by the RDP server to this IP address spiked. The RDP server never scans the internal network. Darktrace identified this as highly unusual activity.

Figure 6: Darktrace detects the anomalous external data transfer

Lateral movement and payload execution

Finally, on April 12, the attackers executed the Dharma payload at 13:45. The RDP server wrote a number of files over the SMB protocol, appended with a file extension containing a throwaway email account possibly evoking the current COVID-19 pandemic, ‘cov2020@aol[.]com’. The use of string ‘…@aol.com].ROGER’ and presence of a file named ‘FILES ENCRYPTED.txt’ resembles previous Dharma compromises.

Parallel to the encryption activity, the ransomware tried to spread and infect other machines by initiating successful SMB authentications using the same administrator credential seen during the internal reconnaissance. However, the destination devices did not encrypt any files themselves.

It was during the encryption activity that the internal IT staff pulled the plug from the compromised RDP server, thus ending the ransomware activity.

Conclusion

This incident supports the idea that ‘legacy’ ransomware may morph to resurrect itself to exploit vulnerabilities in remote working infrastructure during this pandemic.

Dharma executed here a fast-acting, planned, targeted, ransomware attack. The attackers used off-the-shelf tools (RDP, abusing SMB1 protocol) blurring detection and attribution by blending in with typical administrator activity.

Darktrace detected every stage of the attack without having to depend on threat intelligence or rules and signatures, and the internal security team acted on the malicious activity to prevent further damage.

This incident supports the idea that ‘legacy’ ransomware may morph to resurrect itself to exploit vulnerabilities in remote working infrastructure during this pandemic. Poorly-secured public-facing systems have been rushed out and security is neglected as companies prioritize availability – sacrificing security in the process. Financially-motivated actors weaponize these weak points.

The use of the COVID-related email ‘cov2020@aol[.]com’ during the attack indicates that the threat-actor is aware of and abusing the current global pandemic.

Recent attacks, such as APT41’s exploitation of the Zoho Manage Engine vulnerability last March, show that attacks against internet-facing infrastructure are gaining popularity as the initial intrusion vector. Indeed, as many as 85% of ransomware attacks use RDP as an entry vector. Ensuring that backups are isolated, configurations are hardened, and systems are patched is not enough – real-time detection of every anomalous action can help protect potential victims of ransomware.

Technical Details

Some of the detections on the RDP server:

  • Compliance / Internet Facing RDP server – exposure of critical server to Internet
  • Anomalous Connection / Application Protocol on Uncommon Port – external connections using an unusual port to rare endpoints
  • Device / Large Number of Connections to New Endpoints – indicative of peer-to-peer or scanning activity
  • Compliance / Incoming Remote Desktop – device is remotely controlled from an external source, increased rick of bruteforce
  • Compromise / Ransomware / Suspicious SMB Activity – reading and writing similar volumes of data to remote file shares, indicative of files being overwritten and encrypted
  • Anomalous File / Internal / Additional Extension Appended to SMB File – device is renaming network share files with an added extension, seen during ransomware activity

The graph below shows the timeline of Darktrace detections on the RDP server. The attack lifecycle is clearly observable.

Figure 7: The model breaches occurring over time

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 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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Carlos Gray
Senior Product Marketing Manager, Email

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July 24, 2026

When Guardrails Break: Why Securing AI Requires Behavioral Detection and Autonomous Containment

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Bottom line up front: Governance, guardrails, identity controls, and secure development are necessary to secure AI, but they are not sufficient. AI systems are probabilistic, adaptive, and non-deterministic. Therefore, organizations need two critical layers of security:

  1. Behavioral-based detection that can identify when AI begins to act outside its intended purpose; and  
  2. Surgical, explainable autonomous containment that can stop risky activity before it causes material damage.  

That capability depends on multiple specialized AI models working together, not one LLM making every decision.

Organizations are embedding AI into development, business operations, and security workflows faster than most security programs can adapt. The risk is no longer limited to the model. It extends across prompts, data, identities, agents, memory, APIs, tools, permissions, and the trust relationships connecting them.

In my recent blog, Securing AI: Analysis of the Complete Security Stack with Governance and Controls, I outlined a defense-in-depth strategy spanning governance, identity, data security, secure development, runtime detection, autonomous containment, and recovery. The most urgent requirement across that architecture is the ability to understand how AI behaves in practice and contain it when that behavior becomes risky.  

Why non-deterministic systems require behavioral-based detection

Traditional controls remain foundational. Organizations need least privilege, strong identity controls, secure-by-design architecture, data governance, AI inventories, guardrails, testing, and clear boundaries on autonomy.

But deterministic controls, which assume predictable and repeatable behavior, cannot fully secure non-deterministic systems, where the same input may not always produce the same outcome.

AI agents can interpret the same instruction differently, chain individually authorized actions into an unsafe outcome, or pursue a legitimate goal through a method the organization did not anticipate. One of the most recent examples of this is the incident that OpenAI and Hugging Face jointly disclosed, where an autonomous agent escaped its intended testing boundaries and compromised Hugging Face infrastructure.  

An agent may have permission to access data and invoke a tool, but that does not mean every use of that access is appropriate. It is not enough to know whether an action is allowed. Organizations need to know whether it makes sense.

  • Is this normal for this agent?  
  • Is it acting within its intended purpose?  
  • Is it accessing unusual data, invoking an unexpected tool, or beginning to drift?  
  • Do a series of ordinary-looking actions become risky when viewed together?

Behavioral-based detection specific to an environment or organization with an understanding of context and risk enables provides the needed detection engineering for AI systems. It learns normal activity across people, systems, data, devices, and AI agents, then identifies deviations and evaluates their risk, intent, and context. This enables detection of misuse, abuse, compromise, manipulation, and unintended behavior even when no known attack signature or explicit policy violation exists.

Why accuracy is the foundation for SOC optimization

AI will only improve the SOC if it produces accurate, explainable, and actionable outcomes.

If analysts must manually validate every AI-generated finding because they cannot understand the evidence or confidence behind it, automation has not reduced workload. It has moved the workload. False positives increase fatigue. False negatives cause the most risk and damage to organizations. Inaccurate autonomous actions can disrupt critical operations.

Accuracy is therefore more than a model-performance metric. It is the prerequisite for analyst trust, SOC optimization, and safe autonomous response.

That accuracy is unlikely to come from one model.

Generative AI is valuable for natural-language analysis, summarization, and human interaction. But an LLM should not be the sole analytical engine for behavioral-based detection, investigation, risk assessment, and containment. Interpretability and consistency are required for high-consequence security decisions.

A stronger architecture uses multiple specialized AI systems collaboratively:  

  • Behavioral models can establish normal activity.  
  • Unsupervised learning can identify novel anomalies.  
  • Graph analysis can evaluate relationships among agents, identities, systems, and tools.  
  • Other models can correlate events, investigate competing hypotheses, and assess risk.  
  • Semantic models can analyze language where behavior-based language analysis is needed but this can be used in tandem with vector embeddings, graph neural networks, and a variety of other AI systems.

Each model contributes a different analytical perspective. Their outputs can corroborate one another, improving accuracy and creating a more reliable basis for response. The objective is not one model operating as an oracle. It is layered, adaptive intelligence designed to produce decisions the SOC can understand and trust.

Autonomous containment is required to secure autonomous systems

Many SOCs remain hesitant to trust LLM-based agents with autonomous containment. That concern is reasonable. A poorly selected response can isolate the wrong asset, stop a critical workflow, block a legitimate identity, or create more operational damage than the original incident.

But relying exclusively on human response is also not viable.

AI systems can operate at machine speed. They can expose sensitive data, execute workflows, modify records, call tools, or propagate actions across connected systems before an analyst can investigate and intervene. The behavior may be unintentional, the result of an agent optimizing toward a goal, or caused by misuse, compromise, prompt injection, or offensive AI.

Intent affects the investigation. It does not change the need to stop the damage.

Organizations need autonomous response, but it must be surgical and explainable. The objective is not to shut down an entire agent, user, application, or business process whenever an anomaly occurs. It is to interrupt the specific risky behavior: block an unusual connection, constrain a tool call, stop an abnormal data transfer, or temporarily limit an agent when it is performing anomalous, risky activity.  

That buys humans time. It stops the spread, limits damage, and allows the SOC to investigate without unnecessarily disrupting the business.

Layered, Adaptive AI provides a path forward

Darktrace has spent more than a decade researching and operationalizing layered, behavioral, Adaptive AI that learns a specific organization rather than relying only on historic attacks or predefined signatures.

The approach is designed to understand normal behavior, identify anomalous activity, assess its risk, correlate related events, autonomously investigate, and, when necessary, apply targeted containment while normal operations continue.

That sequence matters. Autonomous response cannot simply be added to the end of an LLM workflow. Trusted containment depends on broad visibility, continuous behavioral understanding, multiple analytical techniques, risk and context evaluation, autonomous investigation, explainability, and precise response actions.

This represents a more responsible model for security autonomy: not automation for its own sake, but controlled autonomy built to improve security outcomes and protect business operations.

Security must enable AI adoption

The answer for security teams is not to block AI. Organizations are adopting it to improve productivity, accelerate development, and create new business value.

But innovation without behavioral detection and autonomous containment is not sustainable.

Organizations should continue investing in governance, identity, least privilege, data security, secure MLOps, guardrails, testing, evaluation, validation, verification, kill switches, rollback, and forensic readiness. At the same time, they cannot wait for every governance program to mature before addressing runtime risk.

Behavioral-based detection and autonomous containment provide an immediate layer of resilience. They allow organizations to detect exploitation and risky AI behavior they did not anticipate, contain it at machine speed, and preserve human control over broader remediation.

The future of AI security will not be defined by a single model making every decision. It will be defined by multiple specialized AI systems working collaboratively, with sufficient accuracy, transparency, and context to support trusted autonomous action.

Surgical, explainable autonomous containment is no longer a future capability. It is a requirement for scaling AI securely today.

Learn how to build a defense-in-depth strategy for securing AI at scale in our talk at Black Hat on August 5 at 3:15 PM.  

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