Red + Blue, How Purple Are You?
Identifying Gaps in The Spectrum of Security
© 2023 Specter Ops, Inc.

What is Purple Teaming?
• A collaborative approach to cybersecurity that brings together red and
blue teams to test and improve an organization’s security posture.
• Coursera
• In true purple fashion, the goal of the course is to educate students on how
adversarial techniques can be emulated (manual and automated) and
detected (use cases/rules and anomaly-based detection).
• SANS SEC699
• The goal of purple teaming is to bring the blue and red team functions
together.
• ATTACKIQ
• Meant to better understand our organization’s ability to detect and
respond to real-world attacks.
• GitLab
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© 2023 Specter Ops, Inc.

What is Purple Teaming?
• A function designed to enhance the information sharing between – and the
ultimate effectiveness of – an organization’s Red and Blue Teams. The ultimate
purpose of improving the organization’s defenses.
• Daniel Miessler
• A blue team becomes “purple” when it emulates the adversary as a means of
self-evaluation.
• Jonathan Reiber
• [Purple Teaming] refers to multiple cybersecurity teams working together to
improve an organization’s security posture…
• Xena Olsen
• Actively pinpoint weaknesses in protection and detection capabilities.
• TIBER-EU
• Enables defenders to gain better understanding of adversary TTP.
• Cristian Pescariu (Pluralsight)
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https://github.com/ch33r10/EnterprisePurpleTeaming

What is Purple Teaming?
• Collaboration between Offense (Red) and Defense (Blue).
• Increase familiarity with or understanding of adversary TTP.
• Self-evaluation of existing security posture.
• Improving an organizations security posture or defenses.
• Preventative Controls
• Detective Controls
• Response Procedures
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ATT&CK Heat Maps
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https://cyberwardog.blogspot.com/2017/07/how-hot-is-your-hunt-team.html

Problem Statement
• Do our security controls achieve their goals?
• If our goal is to detect processes named “mimikatz.exe” do we detect every
time a process is named “mimikatz.exe”
• Are the goals we set for our security controls properly scoped?
• The adversary dictates the proper scope.
• Detection is a prediction of which variation we expect the attacker to use.
• If the answer to either of the previous is “no”, why?
• Signature vs. Behavior
• Vendor rules vs. Organic rules
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Existing Solutions
How has the industry been addressing this problem?
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Existing Solutions: Adversary Emulation Plans
• Protocol
• Execute a coherent attack path to achieve some objective.
• Base selection of procedures on explicitly observed examples.
• Benefits
• Test a broad range of techniques.
• Simulate a ”real” attack path.
• Limitations
• Attack Path must be coherent, so often executes only one variation of a technique.
• Procedural variations are limited to known or observed variations.
• Past observations are not necessarily predictive of future actions.
• Examples
• Red Team Exercises
• MITRE ATT&CK EDR Evaluations
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Techniques
Variations
Breadth
Depth

PowerShell to C#
• Microsoft addressed PowerShell malware, as
a class, so effectively that using PowerShell
became untenable for many attackers.
• Sharpdump is one example where the code
from Out-Minidump was directly ported from
PowerShell to C#, in order to, bypass controls.
• Demonstrated that the tool or modality had
been detected, not the behavior.
• It is not possible to draw broad conclusions
(we detect LSASS Credential Dumping) from a
single test case.
• Efficacy evaluation must include multiple
variations of a behavior.
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Existing Solution: Dynamic Test Cases
• Protocol
• Create test cases that represent adversary tradecraft.
• Execute test cases in the environment.
• Observe the result of security controls.
• Benefits
• Allows for a more complete understanding of control efficacy.
• Execution is simplified and streamlined.
• Provides a repeatable process, especially with detective controls because they are static.
• Limitations
• Efficacy is dependent on the representative nature of selected test cases.
• Finite resources are used to ensure depth which causes loss in breadth.
• Examples
• Atomic Red Team
• Breach and Attack Simulation Products
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Similarity
• What does it mean for two malware samples to be the SAME?
• How can we measure similarity?
• Cryptographic Hashes (MD5, SHA1, SHA256)
• Only measure ABSOLUTE similarity
• There’s no way to determine if one bit changed or if the entire sample is different.
• Piecewise and Fuzzy Hashing1
• Generate traditional hash, but also generate hash values for segments of files.
• This assumes that changes will be localized to certain locations (change mimikatz to
mimidogz).
• Imphash2
• Idea that Portable Executables that import the same API functions are probably similar in
function despite changes to less significant bits.
• Still lacks ability to distinguish between small and large changes.
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1. https://www.sciencedirect.com/science/article/pii/S1742287606000764?via%3Dihub
2.https://www.mandiant.com/resources/blog/tracking-malware-import-hashing

Electromagnetic Spectrum
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Defense Evasion Spectrum
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Prototypical or Traditional Variation
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Minimize Difference Between Tests
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Maximize Difference Between Tests
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Quantity vs. Quality
• Phenomena can be measured in two ways:
• Quantitative measurements
• Number of test cases
• Qualitative measurements
• Likelihood of Occurrence
• Uniqueness relative to existing test cases
• Appears that Quantitative measures are being used as a PROXY for
Quantitative measures.
• If we increase the number of test cases, then surely, we will increase coverage.
• Unfortunately, there are so many possible variations that random selection is
unlikely to provide a valid sampling.
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Functional Variations
• Software is extraordinarily complicated, especially when it is being
designed with evasion in mind.
• Most Attack Techniques can be implemented in MANY different ways:
• Process Injection
• 4.4 Million Functional Variations Across 8 Procedures
• Access Token Manipulation
• 2.5 Million Functional Variations Across 22 Procedures
• LSASS Credential Dumping
• 33,000 Functional Variations Across 4 Procedures
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Reality of Current Solutions
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Sampling
• Selection of a subset of the of
individuals from within the
statistical population to estimate
characteristics of the whole
population.
• Examples
• Political Polling
• Pharmaceutical Testing
• Problems
• Selection Bias
• Sampling Size Issues
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Defense Evasion Spectrum
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Our Approach
How we select test cases for Purple Team testing at SpecterOps
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Golden Tickets
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Golden Tickets
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Classic Shellcode Injection
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https://github.com/DGRonpa/Process_Injection/blob/main/Classical_Injeciton/Shellcode_Injection.cpp#L40-L48

Classic Shellcode Injection
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https://github.com/DGRonpa/Process_Injection/blob/main/Classical_Injeciton/Shellcode_Injection.cpp#L40-L48

Function Call Stack – kernel32!OpenProcess
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Function Chain + Call Stacks
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Function Call Stacks
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Win32 API Functional Variation
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Direct Syscall Functional Variation
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https://github.com/badBounty/directInjectorPOC/blob/master/directInjectorPOC/Program.cs

1st Hypothetical Functional Variation
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2nd Hypothetical Functional Variation
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Functional Variations
• 900 total ”functional” or ”inter-
procedural” variations are possible given
the call stacks generated from our initial
sample.
• 5x6x5x6 = 900
• Relatively insignificant difference.
• Can bypass some naïve EDR sensors.
• Kernel telemetry is robust.
• Like moving the small weight on the
physician’s scale.
• Not the first thing one should worry
about.
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Operations Categorize Function Call Stacks
• Each function in a Call Stack can be
transparently exchanged for any
other function.
• From a developer point of view,
these small changes make little
difference.
• From an attacker point of view,
these small changes can evade
naïve EDR products.
• From a defender point of view,
these small changes can be ignored
by conceiving of the attack via
Operations instead of Functions.
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Operational Chain – Classic Shellcode Injection
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EDRs Perceive Operations
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OpenProcessApiCall NtAllocateVirtualMemoryRemoteApiCall WriteProcessMemoryApiCall CreateRemoteThreadApiCall

“Align your conception to your perception.”
We should think about attack techniques (Process Injection) at the level of
resolution at which we perceive them.
We don’t SEE Process Injection, we SEE Thread Creations, Process Writes, and
Process Opens.
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© 2023 Specter Ops, Inc.

Why Change it?
• Security Controls!
• This is analogous to natural selection, insofar as, if a control prevents or
endangers the operation it WILL act as a selective pressure.
• The more robust the control, the greater the change.
• [Small] Signatures looking for the string “mimikatz” result in ‘s/katz/dogz/g’.
• [Medium] Controls focused on specific API functions (CreateRemoteThread)
result in non-standard API functions being used (syscall!NtCreateThreadEx).
• [Large] Controls focused on the Execution method (Thread Creation) result in
novel Execution methods (Asynchronous Procedure Calls).
• Attackers often implement the “Minimal Stimulus Necessary”
principle.
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Example 1: Classic Shellcode Injection
https://github.com/DGRonpa/Process_Injection/blob/main/Classical_Injeciton/Shellcode_Injection.cpp#L40-L48 41

Example 2: Asynchronous Procedure Call
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https://github.com/DGRonpa/Process_Injection/blob/main/APC_Injection/APC_Injection_Shellcode.cpp

Example 2: Asynchronous Procedure Call
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https://github.com/DGRonpa/Process_Injection/blob/main/APC_Injection/APC_Injection_Shellcode.cpp

Example 3: File Mapping
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https://github.com/DGRonpa/Process_Injection/blob/main/Mapping_Injection/Mapping_Injection_CreateRemoteThread.cpp

Example 3: File Mapping
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https://github.com/DGRonpa/Process_Injection/blob/main/Mapping_Injection/Mapping_Injection_CreateRemoteThread.cpp

Example 4: Atom Bombing
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https://github.com/BreakingMalwareResearch/atom-bombing

Example 4: Atom Bombing
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https://github.com/BreakingMalwareResearch/atom-bombing

Example 4: Atom Bombing
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https://github.com/BreakingMalwareResearch/atom-bombing

Example 5: Thread Execution Hijacking
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https://github.com/DGRonpa/Process_Injection/blob/main/Thread_Hijack/Thread_Hijack.cpp

Example 5: Thread Execution Hijacking
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https://github.com/DGRonpa/Process_Injection/blob/main/Thread_Hijack/Thread_Hijack.cpp

Process Injection: Operation Chains
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https://github.com/DGRonpa/Process_Injection/blob/main/Thread_Hijack/Thread_Hijack.cpp

Plotting Operation Chains
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Operational Variations
• There are relatively few
“operational” variations for
Process Injection.
• We’ve looked at 5 in this webinar.
• Represent major difference.
• Potentially remove the operation
that your detective or preventative
control was based on.
• Like moving the large weight on
the physician’s scale.
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© 2023 Specter Ops, Inc.

SpecterOps Purple Team Resources
• On Detection Blog Series
• https://posts.specterops.io/on-detection/home
• Malware Morphology (4-hour Workshop)
• First offering at NorthSec in Montreal (May 18-19, 2023)
• Adversary Tactics: Tradecraft Analysis (4 Day Training)
• Next public offering at Black Hat USA (August 5-8, 2023)
• Purple Team Service
• Detection and Response Program Development Service
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© 2023 Specter Ops, Inc.

SpecterOps Purple Team Resources
• On Detection Blog Series
• https://posts.specterops.io/on-detection/home
• Malware Morphology (4-hour Workshop)
• First offering at NorthSec in Montreal (May 18-19, 2023)
• Adversary Tactics: Tradecraft Analysis (4 Day Training)
• Next public offering at Black Hat USA (August 5-8, 2023)
• Purple Team Service
• Detection and Response Program Development Service
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© 2023 Specter Ops, Inc.

Deliberate Test Selection
• Ability to sample variations is our key technical differentiator.
• Deliberate test case selection using qualitative measurements.
• Does this sample call the same functions as a previous sample? (redundant)
• Does this sample implement a different Operation Chain? (novel)
• Triangulate estimation of coverage.
• Begin with maximal differentiation.
• Fine tune with smaller and smaller differences over time.
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Security Control Evaluation
• Provide feedback on your controls along three measurements:
• Prevention
• Detection
• Perception (Telemetry)
• Controls are evaluated independently.
• Using 1 rule to detect 5 tests is robust.
• Using 5 rules to detect 5 tests is brittle.
• Goal is to infer or predict how likely (the probability) it is that the set
of controls would detect/prevent the attacker’s arbitrary selection of
a variation.
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Education
• We believe that you can’t detect (or prevent) what you don’t understand.
• Golden Ticket Epiphany
• In addition to the evaluation results, we teach clients about the technique
we are testing”
• How it works.
• Why attackers use it.
• The types of variations that exist (big and small).
• Common issues we’ve seen with detection attempts.
• Prevention opportunities.
• Increase the resolution with which clients view attack techniques.
• Help to understand the correct level of abstraction.
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Modeling to Improve Solutions
• Understand control’s resilience by comparing test results.
• Identify the best data source for detection engineering.
• Help to understand distinction between uni- or multi-chain detections.
• Provide guidance on optimal scope of controls.
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Modeling to Improve Solutions
• Understand control’s resilience by comparing test results.
• Identify the best data source for detection engineering.
• Help to understand distinction between uni- or multi-chain detections.
• Provide guidance on optimal scope of controls.
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