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July 8, 2020

Cryptomining Campaigns & Technical Analysis of Vulnerability

Crypto-mining campaigns stood no chance against Darktrace's AI as it identified the threat in real time. Put your trust in Darktrace's assistance!
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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08
Jul 2020

Introduction

The speed with which attackers can weaponize vulnerabilities is steadily increasing. While technology is rapidly evolving and cyber-attacks are becoming more sophisticated, the advantages of exploiting software vulnerabilities over devising a more elaborate and lengthy attack plan have not been overlooked by hackers. These vulnerabilities are also a quick way to gain access into a businesses’ infrastructure. In recent years, attackers have found great benefit and substantial success through quickly weaponizing vulnerabilities in web-facing systems.

Just recently, critical vulnerabilities in Citrix Gateway resulted in a spate of activity targeting Darktrace customers, as reported earlier this year. Without an immediate patch released upon the public announcement of the discovered flaws in Citrix, exploits quickly followed. Similarly, in late April, SaltStack developers reported vulnerabilities in Salt, an open source framework used to monitor and update the state of servers in cloud environments and data centers.

The vulnerabilities found in Salt would allow hackers to bypass authentication and authorization controls and execute code in Salt master servers exposed to the internet. The Salt master is responsible for sending commands to Salt minions and can manage thousands of minions at once. Due to this structure, one exposed Salt master can lead to a compromise of all underlying minions.

On May 2, Darktrace detected successful crypto-miner infections across a number of its customers exploiting the CVE-2020-11651 and CVE-2020-11652 vulnerabilities in SaltStack server management software. In the same weekend, LineageOS — an Android mobile operating system – and Ghost — a blogging platform – both reported suffering a crypto-mining attack due to exposed, unpatched Salt servers. Most notable about these attacks was the sheer speed from a vulnerability being published to a widespread attack campaign.

Timeline

Figure 1: A timeline of events identified by Darktrace on May 3

Technical analysis

Initial compromise

Darktrace initially detected that a number of customer servers running SaltStack were making external connections to endpoints previously not seen on the network. The connections used the curl or wget utilities to download and execute a bash script, which would install a secondary-stage payload containing a cryptocurrency miner.

The systems were targeted directly utilizing 2020-11651 and CVE-2020-11652 vulnerabilities in the ZeroMQ protocol running on SaltStack. These vulnerabilities would allow direct remote code execution as root on the targeted systems, allowing the script to be downloaded and executed successfully with highest system privileges.

The downloader script is almost identical to the one utilized in March in H2Miner infections targeting exposed Docker APIs and Redis instances.

Before downloading the secondary stage payload, the script cleans the target system of a number of pre-existing infections and miners, as well as disabling a number of known security tools and software.

Figure 2: The downloader script

Following the initial clean up, the script would iterate through three functions to download the crypto-miner payload — salt-storer

SHA256 837d768875417578c0b1cab4bd0aa38146147799f643bb7b3c6c6d3d82d7aa2a

— from three different hard-coded servers. An MD5 check for the downloaded executable would be performed prior to execution. The below screenshot illustrates two out of the three downloader functions that would be invoked.

Figure 3: Two of the downloader functions

Second stage payload

Following the cryptographic checks, the downloaded ELF LSB executable kicks into action. No payload analysis was carried out, however it’s execution would result in a crypto-miner being installed and a C2 channel opened.

OSINT indicates that several new versions of the payload were observed carrying additional capabilities, including database dumping and advanced persistence methods. The variants detected by Darktrace’s AI included the more advanced “Version 5” payload purported to have worming capabilities, but in this case they were not observed directly.

Command and control

Upon the execution of an LSB executable, a plaintext HTTP C2 channel would be established, sending basic metadata about the infected host such as processor architecture, available resources, and whether root execution was achieved. This indicates that the C2 mechanisms were likely repurposed from other infections, as this particular infection would execute as root, making the respective component redundant.

Figure 4: A Command and control channel

The complete attack lifecycle was investigated and reported on by Darktrace’s Cyber AI Analyst, which automatically surfaced some crucial details regarding the C2 communication, including other servers that were seen making similar communication patterns, as seen in the bottom right below.

Figure 5: The Cyber AI Analyst automatically generating a natural-language summary of the overall security incident

Figure 6: Further information on the suspicious endpoints

Actions on target

Lastly, devices began mining for cryptocurrency. Cryptocurrency mining demands a substantial proportion of a device’s processing power, such as CPU and GPU, in order to calculate hashes. However, except for the occasional increase in CPU or RAM usage, it can go undetected for months as traditional security products do not normally detect its pattern of behavior as malicious.

Conclusion

Failing to patch vulnerabilities quickly and decisively can have serious consequences. Sometimes, however, the window of opportunity before an attack hits is too short for patching to be feasible. This example demonstrates how quickly unpatched vulnerabilities can be exploited following an initial public disclosure. And yet, even two months after SaltStack published the updates, many Salt servers remain unpatched and run the risk of becoming compromised.

In the case of Citrix, some exploits led to a ransomware attack. Darktrace’s AI-powered Immune System technology not only detected every stage of these ransomware attacks, but its autonomous response was able to halt any anomalous event and contain further damage.

Because new vulnerabilities are, by nature, unexpected, traditional security tools relying on rules and signatures don’t know to look for malicious activity that arises as a result. However, with its constantly evolving understanding of ‘normal’, Darktrace’s AI detects and investigates any unusual behavior, regardless of its origin or whether an attack has been seen before.

Crypto-mining is still favored among many threat actors due to its ability to generate profits, and a successfully infection can have a serious impact on the confidentiality and integrity of the corporate network. The need for Cyber AI that can detect new vulnerabilities and novel threats, and autonomously respond to stop an attack in its tracks, are critical to ensuring businesses remain secure in the face of cyber-criminals who are mobilizing to exploit vulnerabilities more quickly than ever.

IoCs:

IoCComment144.217.129[.]111Likely C2, URIs: /ms /h /s91.215.152[.]69Likely C2, URI: /h89.223.121[.]139Download of payload sa.sh217.12.210[.]192Download of payload sa.sh45.147.201[.]62Destination for crypto-mining217.12.210[.]245Download of payload salt_storer

Darktrace model breaches:

  • Device / Initial Breach Chain Compromise
  • Compromise / SSL or HTTP Beacon
  • Device / Large Number of Model Breaches
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Script from Rare External
  • Compromise / Beaconing Activity To External Rare
  • Anomalous Connection / Multiple Failed Connections to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compliance / Crypto Currency Mining 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
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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