Blog
/
/
July 7, 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
Default blog image
07
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

More in this series

No items found.

Blog

/

Proactive Security

/

June 1, 2026

Defend What You Trust: Stories from the Front Lines of Modern Cyber Defense

Default blog imageDefault blog image

Modern attacks don’t always announce themselves, follow obvious patterns, or rely on known malware. Often, they move quietly inside trusted systems, authenticated sessions, and everyday behavior.

They don’t break in. They blend in.

That’s why an AI-powered defense is essential. It turns invisible signals into actionable insights at a scale neither analysts nor traditional tools can achieve alone.

Confidence is creating risk

One of the most dangerous assumptions in cybersecurity today is that strong controls equal strong protection.

Multi-factor authentication (MFA), for example, is widely viewed as a foundational safeguard. But as the CISO for a professional sports organization explains, that confidence can be misplaced. “A lot of organizations assume that once you have MFA, those accounts are safe. That’s not true.”

In one instance, his team identified a sophisticated attack where a threat actor bypassed MFA entirely, not by breaking it, but by going around it. A user’s authenticated session was hijacked and re-used, allowing the attacker to impersonate them without triggering traditional controls.

“Darktrace picked up that a session had been re-injected by the hacker, and we were able to block it right away,” he explains.

Attackers anticipate what we miss

Even well-trained users can become entry points.

“An email bypassed our existing security tools,” shares the VP of IT at a U.S.-based risk management services provider.  “The user missed one signal and entered their credentials into a malicious site. That’s what the bad guys count on.”

The organization responded quickly, but not before damage was done. Crucially, this occurred while Darktrace was in “watch mode,” before autonomous response was fully enabled. “Darktrace would have seen that and shut it down immediately,” he notes.

Mistakes and oversights like misconfigurations, forgotten machines, and missed patches can create serious vulnerabilities.

The CIO of a utility services organization shares an instance when Darktrace detected a breach to a client’s network via their ZTNA VPN due to misconfigured MFA. “Darktrace alerted us and autonomously blocked the scanning, preventing what could have been a ransomware-type incident.”  

The most dangerous threats are already inside

The Head of Security at a global business services provider knows firsthand how blind spots can persist inside environments. His team uncovered evidence of dormant ransomware artifacts sitting unnoticed within a company’s environment ¬¬– long before modern detection was in place.

“During a routine file transfer, Darktrace flagged the suspicious activity, identified the ransomware, and immediately quarantined the server,” he recalls.  While the attack was never executed, the implication was significant: the risk existed long before it was finally detected.

Cyber threats are also successful because they take advantage of normal human behavior, exploiting moments of cognitive overload, urgency, and trust.

The Executive Director of IT and Business Applications at a pharmaceutical lab describes the time Darktrace flagged an employee logging into Microsoft 365 from Singapore, despite him being physically located in the U.S. Darktrace immediately cut off his access and within minutes revealed that the employee’s son was using a VPN to play a video game.

While the threat was benign, it demonstrated the strength of AI to use contextual information to detect threats other tools miss. The information also saved security analysts hours of investigation and minimized downtime for the employee. “That level of precision and speed isn’t just convenient, it’s game changing.”

“Unusual” behavior is the new red flag

Detecting modern threats requires an understanding of what “normal” looks like and recognizing when something subtly deviates.

One security leader  at an AI technology enterprise described a scenario in which an employee connected to a proxy service in China. The service itself was legitimate, and although traditional tools didn’t flag it, the behavior was unusual for that user specifically.

“That’s what Darktrace picked up on. The activity turned out to be benign, but without visibility into behavioral deviations, it could just as easily have been something more serious.”

AI shifts defense from reaction to anticipation

These stories point to a fundamental shift by cyber attackers, both tactically and strategically. Because traditional security tools were built to detect what’s already known, modern attacks are often:

  • Credential-based, not malware-based
  • Behavioral, not signature-based
  • Subtle, not overt

They may operate within the boundaries of what appears normal, exploiting what organizations trust, not what they block:

  • Trusted sessions
  • Legitimate services
  • Human error

This is where AI is changing the equation. Rather than relying on predefined rules or known threat signatures, AI can:

  • Establish a baseline of normal behavior
  • Detect subtle anomalies in real time
  • Act autonomously to contain potential threats

Resilience, not perfection, is the new security standard

As these frontline experiences show, the organizations that lead are those that move beyond reactive defense and embrace AI as a core part of their strategy.

It eliminates the blind spots and uncertainty, says the CISO of a professional sports organization. “If you lack visibility, you’re not managing risk, you’re assuming it. AI gives you the actionable insights needed to turn uncertainty into control.”

And it provides the speed and agility that are vital when seconds matter, says the Executive Director of IT and Business Applications. “When Darktrace alerted us at 3:00 am to a ransomware attack, it had already quarantined the affected systems, blocked the attacker’s access, and provided us with the critical details and time needed to investigate. That action likely saved us hundreds of thousands, if not millions, of dollars.”

The modern SOC has become a cornerstone of enterprise resilience, responsible for protecting data and operational continuity while enabling digital growth and innovation. For today’s security professional, that means success is no longer measured by what they keep out, but by what they protect: revenue, reputation, and trust.

Continue reading
About the author

Blog

/

/

May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

AI in manufacturingDefault blog imageDefault blog image

How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

Continue reading
About the author
Oakley Cox
Director of Product
Your data. Our AI.
Elevate your network security with Darktrace AI