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July 27, 2023

Revealing Outlaw's Returning Features & New Tactics

Darktrace's investigation of the latest Outlaw crypto-mining operation, covering the resurgence of old tactics along with the emergence of new ones.
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
Adam Potter
Senior Cyber Analyst
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27
Jul 2023

What is Outlaw Cryptocurrency Mining Operation?

The cybersecurity community has been aware of the threat of Outlaw cryptocurrency mining operation, and its affiliated activities since as early as 2018. Despite its prominence, Outlaw remains largely elusive to researchers and analysts due to its ability to adapt its tactics, procedures, and payloads.

Outlaw gained notoriety in 2018 as security researchers began observing the creation of affiliated botnets.[1][2]  Researchers gave Outlaw  its name based on the English translation of the “Haiduc” tool observed during their initial activity on compromised devices.[3],[4] By 2019, much of the initial Outlaw activity  focused on the targeting of Internet of Things (IoT) devices and other internet facing servers, reportedly focusing operations in China and on Chinese devices.[5],[6]  From the outset, mining operations featured as a core element of botnets created by the group.[7] This initial focus may have been a sign of caution by threat actors or a preliminary means of testing procedures and operation efficacy. Regardless, Outlaw actors inevitably expanded scope, targeting larger organizations and a wider range of internet facing devices across geographic scope.

Following a short period of inactivity, security researchers began to observe new Outlaw activity, showcasing additional capabilities such as the ability to kill existing crypto-mining processes on devices, thereby reclaiming devices already compromised by crypto-jacking. [8],[9]

Latest News on Outlaw

Although the more recently observed incidents of Outlaw did demonstrate some new tactics, many of its procedures remained the same, including its unique bundling of payloads that combine crypto-mining and botnet capabilities. [10] In conjunction, the continued use of mining-specific payloads and growth of affiliated botnets has bolstered the belief that Outlaw actors historically prioritizes financial gain, in lieu of overt political objectives.

Given the tendency for malicious actors to share tools and capabilities, true attribution of threat or threat group is extremely difficult in the wild. As such, a genuine survey of activity from the group across a customer base has not always been possible. Therefore, we will present an updated look into more recent activity associated with Outlaw detected across the Darktrace customer base.  

Darktrace vs Outlaw

Since late 2022, Darktrace has observed a rise in probable cyber incidents involving indicators of compromise (IoCs) associated with Outlaw. Given its continued prevalence and relative dearth of information, it is essential to take a renewed look at the latest campaign activity associated with threats like Outlaw to avoid making erroneous assumptions and to ensure the threat posed is correctly characterized.

While being aware of previous IoCs and tactics known to be employed in previous campaigns will go some way to protecting against future Outlaw attacks, it is paramount for organizations to arm themselves with an autonomous intelligent decision maker that can identify malicious activity, based on recognizing deviations from expected patterns of behavior, and take preventative action to effectively defend against such a versatile threat.

Darktrace’s anomaly-based approach to threat detection means it is uniquely positioned to detect novel campaign activity by recognizing subtle deviations in affected devices’ behavior that would have gone unnoticed by traditional security tools relying on rules, signatures and known IoCs.

Outlaw Attack Overview & Darktrace Coverage

From late 2022 through early 2023, Darktrace identified multiple cyber events involving IP addresses, domains, and payloads associated with Outlaw on customer networks. In this recent re-emergence of campaign activity, Darktrace identified numerous attack vectors and IoCs that had previously been associated with Outlaw, however it also observed significant deviations from previous campaigns.

Returning Features

As outlined in a previous blog, past iterations of Outlaw compromises include four identified, distinct phases:

1. Targeting of internet facing devices via SSH brute-forcing

2. Initiation of crypto-mining operations

3. Download of shell script and/or botnet malware payloads

4. Outgoing external SSH scanning to propagate the botnet

Nearly all affected devices analyzed by Darktrace were tagged as internet facing, as identified in previous campaigns, supporting the notion that Outlaw continues to focus on easily exposed devices. In addition to this, Darktrace observed three other core returning features from previous Outlaw campaigns in affected devices between late 2022 and early 2023:

1. Gzip and/or Script Download

2. Beaconing Activity (Command and Control)

3. Crypto-mining

Gzip and/or Script Download

Darktrace observed numerous devices downloading the Dota malware, a strain that is previously known to have been associated with the Outlaw botnet, as either a gzip file or a shell script from rare external hosts.

In some examples, IP addresses that provided the payload were flagged by open-source intelligence (OSINT) sources as having engaged in widespread SSH brute-forcing activities. While the timing of the payload transfer to the device was not consistent, download of gzip files featured prominently during directly observed or potentially affiliated activity. Moreover, Darktrace detected multiple devices performing HTTP requests for shell scripts (.sh) according to detected connection URIs. Darktrace DETECT was able to identify these anomalous connections due to the rarity of the endpoint, payloads, and connectivity for the devices.

Figure 1: Darktrace Cyber AI Analyst technical details summary from an incident during the analysis timeframe that highlights a breach device retrieving the anomalous shell scripts using wget.

Beaconing Activity – Command and Control (C2) Endpoint

Across all Outlaw activity identified by Darktrace, devices engaged in some form of beaconing behavior, rather than one-off connections to IPs associated with Outlaw. While the use of application protocol was not uniform, repeated connectivity to rare external IP addresses related to Outlaw occurred across many analyzed incidents. Darktrace’s Self-Learning AI understood that this beaconing activity represented devices deviating from their expected patterns of life and was able to bring it to the immediate attention of customer security teams.

Figure 2: Model breach log details showing sustained, repeated connectivity to Outlaw affiliated endpoint over port 443, indicating potential C2 activity.

Crypto-mining

In almost every incident of Outlaw identified across the fleet, Darktrace detected some form of cryptocurrency mining activity. Devices affected by Outlaw were consistently observed making anomalous connections to external endpoints associated with crypto-mining operations. Furthermore, the Minergate protocol appeared consistently across hosts; even when devices did not make direct crypto-mining commands, such hosts attempted connections to external entities that were known to support crypto-mining operations.

Figure 3: Advanced Search results showing a sudden spike in mining activity from a device observed connecting to Outlaw-affiliated IP addresses. Such crypto-mining activity was observed consistently across analyzed incidents.

Is Outlaw Using New Tactics?

While in the past, Outlaw activity was identified through a systematic kill chain, recent investigations conducted by Darktrace show significant deviations from this.

For instance, affected devices do not necessarily follow the previously outlined kill chain directly as they did previously. Instead, Darktrace observed affected devices exhibiting these phases in differing orders, repeating steps, or missing out attack phases entirely.

It is essential to study such variation in the kill chain to learn more about the threat of Outlaw and how threat actors are continuing to use it is varying ways. These discrepancies in kill chain elements are likely impacted by visibility into the networks and devices of Darktrace customers, with some relevant activity falling outside of Darktrace’s purview. This is particularly true for internet-exposed devices and hosts that repeatedly performed the same anomalous activity (such as making Minergate requests). Moreover, some devices involved in Outlaw activity may have already been compromised prior to Darktrace’s visibility into the network. As such, these conclusions must be evaluated with a degree of uncertainty.

SSH Activity

Although external SSH connectivity was apparent in some of the incidents detected by Darktrace, it was not directly related to brute-forcing activity. Affected devices did receive anomalous incoming SSH connections, however, wide ranging SSH failed connectivity following the initiation of mining operations by compromised devices was not readily apparent across analyzed compromises. Connections over port 22 were more frequently associated with beaconing and/or C2 activity to endpoints associated with Outlaw, than with potential brute-forcing. As such, Darktrace could not, with high confidence correlate such SSH activity to brute-forcing. This could suggest that threat actors are now portioning or rotation of botnet devices for different operations, for example dividing between botnet expansion and mining operations.

Command line tools

In cases of Outlaw investigated by Darktrace, there was also a degree of variability involving the tools used to retrieve payloads. On the networks of customers affected by Outlaw, Darktrace DETECT identified the use of user agents and command line tools that it considered to be out of character for the network and its devices.

When retrieving the Dota malware payload or shell script data, compromised devices frequently relied on numerous versions of wget and curl user agents. Although the use of such tools as a tactic cannot be definitively linked to the crypto-mining campaign, the employment of varying and/or outdated native command line tools attests to the procedural flexibility of Outlaw campaigns, and its potential for continued evolution.

Figure 4: Breach log data showing use of curl and wget tools to connect to IP addresses associated with Outlaw.

Outlaw in 2023

Given Outlaw’s widespread notoriety and its continued activities, it is likely to remain a prominent threat to organizations and security teams across the threat landscape in 2023 and beyond.

As Darktrace has observed within its customer base from late 2022 through early 2023, activity linked with the Outlaw cryptocurrency mining campaign continues to transpire, offering security teams and research a renewed look at how it has evolved and adapted over the years. While many of its features and tactics appear to have remained consistent, Darktrace has identified numerous signs of Outlaw deviating from its previously known activities.

While relying on previously established IoCs and known tactics from previous campaigns will go some way to protecting an organization’s network from Outlaw compromises, there is a greater need than ever to go further than this. Rather than depending on a list of known-bads or traditional signatures and rules, Darktrace’s anomaly-based approach to threat detection and unparallel autonomous response capabilities mean it is uniquely positioned to DETECT and RESPOND to Outlaw activity, regardless of how it evolves in the future.

Credit to: Adam Potter, Cyber Analyst, Nahisha Nobregas, SOC Analyst, and Ryan Traill, Threat Content Lead

Relevant DETECT Model Breaches:

Compliance / Incoming SSH  

Device / New User Agent and New IP

Device / New User Agent  

Anomalous Connection / New User Agent to IP Without Hostname  

Compromise / Crypto Currency Mining Activity  

Anomalous File / Internet Facing System File Download  

Anomalous Server Activity / New User Agent from Internet Facing System  

Anomalous File / Zip or Gzip from Rare External Location  

Anomalous File / Script from Rare External Location  

Anomalous Connection / Multiple Failed Connections to Rare Endpoint  

Compromise / Large Number of Suspicious Failed Connections  

Anomalous Server Activity / Outgoing from Server  

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Indicators of Compromise

Indicator - Type - Description

/dota3.tar.gz​

File  URI​

Outlaw  payload​

/tddwrt7s.sh​

File  URI​

Outlaw  payload​

73e5dbafa25946ed636e68d1733281e63332441d​

SHA1  Hash​

Outlaw  payload​

debian-package[.]center​

Hostname​

Outlaw  C2 endpoint​

161.35.236[.]24​

IP  address​

Outlaw  C2 endpoint​

138.68.115[.]96​

IP  address​

Outlaw C2  endpoint​

67.205.134[.]224​

IP  address​

Outlaw C2  endpoint​

138.197.212[.]204​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]59 ​

IP  address​

Possible  Outlaw C2 endpoint​

45.9.148[.]117​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]125​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]129​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]99 ​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]234​

IP  address​

Possible  Outlaw C2 endpoint​

45.9.148[.]236​

IP  address​

Possible  Outlaw C2 endpoint​

159.203.102[.]122​

IP  address​

Outlaw C2  endpoint​

159.203.85[.]196​

IP  address​

Outlaw C2  endpoint​

159.223.235[.]198​

IP  address​

Outlaw C2  endpoint​

MITRE ATT&CK Mapping

Tactic -Technique

Initial Access -T1190  Exploit - Public Facing Application

Command and Control - T1071 - Application - Layer Protocol

T1071.001 - Application Layer Protocol: Web Protocols

Impact - T1496 Resource Hijacking

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
Adam Potter
Senior Cyber Analyst

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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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About the author
Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace
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