September 18, 2021

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A Hands-On Introduction to Mandiant’s Approach to OT Red Teaming

A Hands-On Introduction to Mandiant’s Approach to OT Red Teaming

Operational technology (OT) asset owners have historically considered
red teaming of OT and industrial control system (ICS) networks to be
too risky due to the potential for disruptions or adverse impact to
production systems. While this mindset has remained largely unchanged
for years, Mandiant’s experience in the field suggests that these
perspectives are changing; we are increasingly delivering value to
customers by safely red teaming their OT production networks.

This increasing willingness to red team OT is likely driven by a
couple of factors, including the growing number and visibility of
threats to OT systems, the increasing adoption of IT hardware and
software into OT networks, and the maturing of OT security teams. In
this context, we deemed it relevant to share some details on
Mandiant’s approach to red teaming in OT based on years of experience
supporting customers learning about tangible threats in their
production environments.

In this post we introduce Mandiant’s approach to OT red teaming and
walk through a case study. During that engagement, it took Mandiant
only six hours to gain administrative control on the target’s OLE for
Process Control (OPC) servers and clients in the target’s Distributed
Control System (DCS) environment. We then used this access to collect
information and develop an attack scenario simulating the path a
threat actor could take to prepare for and attack the physical process
(We highlight that the red team did not rely on weaknesses of the DCS,
but instead weak password implementations in the target environment).

NOTE: Red teaming in OT production systems requires
planning, preparation and “across the aisle”
collaboration. The red team must have deep knowledge of industrial
process control and the equipment, software, and systems used to
achieve it. The red team and the asset owner must establish
acceptable thresholds before performing any activities.

Visit our website for more
 or to request Mandiant
 or threat intelligence.

Mandiant’s Approach for Safe Red Teaming in OT

Mandiant’s approach to red teaming OT production systems consists of
two phases: active testing on IT and/or OT intermediary systems, and
custom attack modeling to develop one or more realistic attack
scenarios. Our approach is designed to mirror the OT-targeted attack
lifecycle—with active testing during initial stages (Initial
Compromise, Establish Foothold, Escalate Privileges, and Internal
Reconnaissance), and a combination of active/passive data collection
and custom threat modeling to design feasible paths an attacker would
follow to complete the mission.

A Hands-On Introduction to Mandiant’s Approach to OT Red Teaming

Figure 1: Mandiant OT red teaming approach

  • Mandiant’s OT red teaming may begin either from the
    perspective of an external attacker leveraging IT compromises to
    pivot into the OT network, or from the perspective of an actor who
    has already gained access into the OT network and is ready to
    escalate the intrusion.
  • We then leverage a range of
    commodity tools and utilities that are widely available in most
    target environments to pivot across OT intermediary systems and gain
    privileged access to target ICS.
  • Throughout this process,
    we maintain constant communication with the customer to establish
    safety thresholds. Active participation from the defenders will also
    enable the organization to learn about the techniques we use to
    extract information and the weaknesses we exploit to move across the
    target network.
  • Once the active testing stops at the agreed
    safety threshold, we compile this information and perform additional
    research on the system and processes to develop realistic and
    target-specific attack scenarios based on our expertise of threat
    actor behaviors.

Mandiant’s OT red teaming can be scoped in different ways depending
on the target environment, the organization’s goals, and the asset
owner’s cyber security program maturity. For example, some
organizations may test the full network architecture, while others
prefer to sample only an attack on a single system or process. This
type of sampling is useful for organizations that own a large number
of processes and are unlikely to test them one by one, but instead
they can learn from a single-use case that reflects target-specific
weaknesses and vulnerabilities. Depending on the scope, the red
teaming results can be tailored to:

  • Model attack scenarios based on target-specific
    vulnerabilities and determine the scope and consequences if a threat
    actor were to exploit them in their environment.
  • Model
    attack paths across the early stages of reconnaissance and lateral
    movement to identify low-hanging fruit that adversaries may exploit
    to enable further compromise of OT.
  • Operationalize threat
    intelligence to model scenarios based on tactics, techniques, and
    procedures (TTPs) from known actors, such as advanced
    persistent threats
  • Test specific processes or
    systems deemed at high risk of causing a disruption to safety or
    operations. This analysis highlights gaps or weaknesses to determine
    methods needed to secure high-risk system(s).

Red Teaming in OT Provides Unique Value to Defenders

Red teaming in OT can be uniquely helpful for defenders, as it
generates value in a way very specific to an organizations’ needs,
while decreasing the gap between the “no holds barred” world
of real attackers and the “safety first” responsibility of
the red team. While it is common for traditional red teaming
engagements to end shortly after the attacker pivots into a production
OT segment, a hybrid approach, such as the one we use, makes it
possible for defenders to gain visibility into the specific strengths
and weaknesses of their OT networks and security implementations. Here
are some other benefits of red teaming in OT production networks:

  • It helps defenders understand and foresee possible paths that
    sophisticated actors may follow to reach specific goals. While cyber
    threat intelligence is another great way to build this knowledge,
    red teaming allows for additional acquisition of site-specific
  • It responds to the needs of defenders to account for
    varying technologies and architectures present in OT networks across
    different industries and processes. As a result, it accounts for
    outliers that are often not covered by general security best
    practices guidance.
  • It results in tangible and realistic
    outputs based on our active testing showing what can really happen
    in the target network. Mandiant’s OT red teaming results often show
    that common security testing tools are sufficient for actors to
    reach critical process networks.
  • It results in conceptual
    attack scenarios based on real attacker behaviors and specific
    knowledge about the target. While the scenarios may sometimes
    highlight weaknesses or vulnerabilities that cannot be patched,
    these provide defenders with the knowledge needed to define
    alternative mitigations to mitigate risks earlier in the
  • It can help to identify real weaknesses that could
    be exploited by an actor at different stages of the attack
    lifecycle. With this knowledge, defenders can define ways to stop
    threat activity before it reaches critical production systems, or at
    least during early phases of the intrusion.

Applying Our Approach in the Real World: Big Steam Works

During this engagement, we were tasked with gaining access to
critical control systems and designing a destructive attack in an
environment where industrial steaming boilers are operated with an
Distributed Control System (DCS). In this description, we redacted
customer information—including the name, which we refer to as
“Big Steam Works”—and altered sensitive details. However,
the overall attack techniques remain unchanged. The main objective of
Big Steam Works is to deliver steam to a nearby chemical production company.

For the scope of this red team, the customer wanted to focus
entirely on its OT production network. We did not perform any tests in
IT networks and instead begun the engagement with initial access
granted in the form of a static IP address in Big Steam Work’s OT
network. The goal of the engagement was to deliver consequence-driven
analysis exploring a scenario that could cause a significant physical
impact to both safety and operations. Following our red teaming
approach, the engagement was divided in two phases: active testing
across IT and/or OT intermediary systems, and custom attack modeling
to foresee paths an attacker may follow to complete its mission.

We note that during the active testing phase we were very careful to
maintain high safety standards. This required not only highly skilled
personnel with knowledge about both IT and OT, but also constant
engagement with the customer. Members from Big Steam Works helped us
to set safety thresholds to stop and evaluate results before moving
forward, and actively monitored the test to observe, learn, and remain
vigilant for any unintended changes in the process.

Phase 1 – Active Testing

During this phase, we leveraged publicly accessible offensive
security tools (including Wireshark, Responder, Hashcat, and
CrackMapExec) to collect information, escalate privileges, and move
across the OT network. In close to six hours, we achieved
administrative control on several Big Steam Works’ OLE for Process
Control (OPC) servers and clients in their DCS environment. We
highlight that the test did not rely on weaknesses of the DCS, but
instead weak password implementations in the target environment.
Figure 2 details our attack path:

Figure 2: Active testing in Big Steam
Work’s OT network

  1. We collected network traffic using Wireshark to map network
    communications and identify protocols we could use for credential
    harvesting, lateral movement, and privilege escalation. Passive
    analysis of the capture showed Dynamic Host Configuration Protocol
    (DHCP) broadcasts for IPv6 addresses, Link-Local Multicast Name
    Resolution (LLMNR) protocol traffic, and NetBios Name Service
    (NBT-NS) traffic.
  2. We responded to broadcast LLMNR, NBT-NS,
    and WPAD name resolution requests from devices using a publicly
    available tool called Responder. As we supplied our IP address in
    response to broadcasted name resolution requests from other clients
    on the subnet, we performed man-in-the-middle (MiTM) attacks and
    obtained NTLMv1/2 authentication protocol password hashes from
    devices on the network.
  3. We then used Hashcat to crack the
    hashed credentials and use them for further lateral movement and
    compromise. The credentials we obtained included, but were not
    limited to, service accounts with local administrator rights on OPC
    servers and clients. We note that Hashcat cracked the captured
    credentials in only six seconds due to the lack of password strength
    and complexity.
  4. With the credentials captured in the first
    three steps, we accessed other hosts on the network using
    CrackMapExec. We dumped additional cached usernames, passwords, and
    password hashes belonging to both local and domain accounts from
    these hosts.
  5. This resulted in privileged access and control
    over the DCS’s OPC clients and servers in the network. While we did
    not continue to execute any further attack, the level of access
    gained at this point enabled us to perform further reconnaissance
    and data collection to design and conceptualize the last steps of a
    targeted attack on the industrial steaming boilers.

The TTPs we used during the active testing phase resemble some of
the simplest resources that can be used by threat actors during real
OT intrusions. The case results are concerning given that they
illustrate only a few of the most common weaknesses we often observe
across Mandiant OT red team engagements. We highlight that all the
tools used for this intrusion are known and publicly available. An
attacker with access to Big Steam Works could have used these methods
as they represent low-hanging fruit and can often be prevented with
simple security mitigations.

Phase 2 – Custom Attack Modeling

For roughly a week, Mandiant gathered additional information from
client documentation and research on industrial steaming boilers. We
then mirrored the process an attacker would follow to design a
destructive attack on the target process given the results achieved
during phase 1. At this point of the intrusion, the attacker would
have already obtained complete control over Big Steam Works’ OPC
clients and servers, gaining visibility and access to the DCS environment.

Before defining the path to follow, the attacker would likely have
to perform further reconnaissance (e.g., compromising additional
systems, data, and credentials within the Big Steam Works DCS
environment). Specifically, the attacker could:

  • Gain access to the DCS configuration software/engineering
  • Obtain configuration/control logic files
  • Determine the type/function of the different DCS nodes in the
  • Use native DCS tools for system overview,
    graphics display, and point drill down
  • Identify
    alarms/alerts monitored by operators via remote HMI screens and map
    them to defined points
  • Map the flow of the physical process
    based on data collection and review

Our next step was to develop the custom scenario. For this example,
we were tasked with modeling a case where the attacker was attempting
to create a condition that had a high likelihood of causing physical
damage and disruption of operations (see Figure 3). In this scenario,
the attacker attempted to achieve this by lowering the water level in
a boiler drum below the safe threshold while not tripping the burner
management system or other safety mechanisms. If successful, this
would result in rapid and extreme overheating in the boiler. Opening
the feedwater valve under such conditions could result in a
catastrophic explosion.

Figure 3: Custom attack model diagram for
Big Steam Works

Figure 3 describes how a real attacker might pursue their mission
after gaining access to the OPC servers and clients. As the actor
moves closer to their goals, it becomes more difficult to assess both
the probability of success and the actual impact of their actions due
to nuances specific to the client environment and additional safety
and security controls built into the process. However, the analysis
holds significant value as it illustrates the overall structure of the
physical process and potential attacker behaviors aimed at achieving
specific end goals. Furthermore, it proceeds directly from the results
obtained during the first phase of the red teaming.

The model presents one feasible combination of actions that an
attacker could perform to access devices governing the boiler drum and
modify the water level while remaining undetected. With the level of
access obtained from phase 1, the attacker would likely be able to
compromise engineering workstations (EWS) for the boiler drum’s
controller using similar tools. This would likely enable the actor to
perform actions such as changing the drum level setpoints, modifying
the flow of steam scaling, or modifying water flow scaling. While the
model does not reflect all additional safety and security measures
that may be present deeper in the process, it does account for the
attacker’s need to modify alarms and control sensor outputs to remain undetected.

By connecting the outcomes produced in the test to the potential
physical impacts and motivations involved in a real attack, this model
provided Big Steam Works with a realistic overview of cyber security
threats to a specific physical process. Further collaboration with the
customer enabled us to validate the findings and support the
organization to mitigate the risks reflected in the model.


Mandiant’s OT red teaming supports organizations by combining both
the hands-on analysis of vulnerabilities and weaknesses in IT and OT
networks with the conceptual modeling of attacker goals and possible
avenues to reach specific outcomes. It also enables security
practitioners to adopt the attacker’s perspective and explore attack
vectors that may otherwise have not been conceived regardless of their
value as low-hanging fruit for OT intrusions.

Our approach presents realistic scenarios based upon technical
evidence of intrusion activity upon OT intermediary systems in the
tested network. In this way, it is tailored to support
consequence-driven analysis of threats to specific critical systems
and processes. This enables organizations to identify attack scenarios
involving digital assets and determine safeguards that can best help
to protect the process and ensure the safety of their facilities.

Head over to our website for more
or to request Mandiant
or threat intelligence.