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MITRE ATT&CK® Technique

T1001.001: Junk Data

Adversaries may add junk data to protocols used for command and control to make detection more difficult.[1] By adding random or meaningless data to the protocols used for command and control, adversaries can prevent trivial methods for decoding, deciphering, or otherwise analyzing the traffic. Examples may include appending/prepending data with junk characters or writing junk characters between significant characters.

EnterpriseT1001.001Sub-techniqueObject v1.1 Modified
Discuss defensive coverage Back to ATT&CK
Glexia's Take

Analyst context for executives and security teams

Analyst confidence High

Junk Data matters because it turns otherwise recognizable command-and-control traffic into something harder for simple signatures, decoders, and analyst tooling to interpret. For leaders, the issue is not the junk characters themselves; it is whether network monitoring can still identify suspicious outbound control channels when the content is intentionally padded or distorted.

Executive priority

Treat this as a resilience and evidence question for network detection programs: can the organization prove that boundary monitoring, managed detection, and incident response workflows can investigate obfuscated C2 across ESXi, Linux, macOS, and Windows environments? Budget and control decisions should prioritize visibility into outbound traffic and the ability to tune detections beyond brittle string matching, especially because ATT&CK links this behavior to multiple malware families and at least one named group relationship.

Technical view

T1001.001 is a command-and-control sub-technique of Data Obfuscation. Defenders should validate detection logic against traffic where meaningless or random data is appended, prepended, or inserted between meaningful protocol elements. ATT&CK provides no official detection text, but the relationship to DET0011 points toward behavioral analysis of C2 channels. The related mitigation is M1031, Network Intrusion Prevention, using intrusion detection signatures to block traffic at network boundaries. SOC teams should test whether existing IDS/IPS, proxy, packet, and network metadata workflows still surface abnormal C2-like patterns when content is padded or malformed rather than plainly encoded.

Likely telemetry

  • Network intrusion detection and prevention events at internet and inter-segment boundaries
  • Proxy, gateway, and egress filtering logs for outbound sessions
  • Network flow metadata such as source, destination, port, protocol, duration, byte counts, and connection frequency
  • Full packet capture or protocol transaction logs where legally and operationally available
  • Endpoint-to-network correlation for ESXi, Linux, macOS, and Windows systems initiating unusual outbound communications

Detection direction

  • Do not rely only on exact string signatures or simple decoders; validate behavior-based analytics for unusual outbound sessions, abnormal payload structure, and inconsistent protocol use.
  • Tune detections with local baselines so benign application padding, custom protocols, or noisy middleware do not create excessive false positives.
  • Confirm whether encrypted, proxied, or unmanaged egress paths reduce visibility into payload structure and therefore limit detection of junk data itself.
  • Use relationship context carefully: multiple ATT&CK software entries use this technique, but those relationships should guide hunt hypotheses rather than imply local exposure or confirmed activity.
  • Map detections to the parent behavior, Data Obfuscation, so analysts consider junk data alongside other C2 obfuscation methods during triage.

Mitigation priorities

  • Prioritize M1031-aligned network intrusion prevention at network boundaries, including maintained signatures and blocking logic for known malicious or malformed C2 traffic.
  • Strengthen egress monitoring and control so unusual outbound channels from servers, workstations, and virtualization platforms are visible to the SOC.
  • Pair blocking controls with investigation playbooks, because junk data may be intended to defeat straightforward content analysis rather than stop all metadata-based detection.
  • Regularly test detection and response coverage using safe simulations of padded or obfuscated protocol traffic, without assuming vendor defaults provide coverage.
Analyst notes and limits

ATT&CK links Junk Data to APT28 and numerous software entries including P2P ZeuS, Uroburos, Downdelph, PLEAD, WellMess, SUNBURST, GoldMax, and others. This supports prioritizing the behavior as a recurring C2 tradecraft pattern, not as evidence of any specific actor in a local environment. UPSTYLE’s relationship includes Network Devices/Linux, while the technique object itself lists ESXi, Linux, macOS, and Windows; scope coverage decisions should follow local assets and ATT&CK platform mapping.

The official ATT&CK object does not provide detection guidance, procedure details, or guaranteed observables for this sub-technique. Recommendations therefore focus on conservative validation of network visibility, behavioral analysis, and the supplied M1031 mitigation relationship. Local protocol use, encryption, logging depth, and retention will determine practical detection quality.

Official MITRE ATT&CK definition

Junk Data

Adversaries may add junk data to protocols used for command and control to make detection more difficult.[1] By adding random or meaningless data to the protocols used for command and control, adversaries can prevent trivial methods for decoding, deciphering, or otherwise analyzing the traffic. Examples may include appending/prepending data with junk characters or writing junk characters between significant characters.

View the same entry on attack.mitre.org (MITRE-hosted reference; in-page links above use the Glexia ATT&CK library.)

Glexia analysis

How security teams should use this page

Treat this object as behavior context, not an attribution claim. Validate the related groups, software, data sources, and mitigations against official ATT&CK relationships and your own telemetry before making control-coverage decisions.

ATT&CK relationship table

Related techniques

This mirrors the MITRE pattern of making group, software, campaign, and technique relationships scannable. Relationship notes come from mirrored ATT&CK relationship text when available.

1 rows
Domain ID Name Relationship / procedure
Enterprise T1001 Data Obfuscation This object subtechnique of Data Obfuscation.
Associated objects

Groups, software, and campaigns

Group Enterprise

G0007: APT28

APT28 is a threat group that has been attributed to Russia's General Staff Main Intelligence Directorate (GRU) 85th Main Special Service Center (GTsSS) military unit 26165.[1][2] This group has been active since at least 2004.[3][4][5][6][7][8][9][10][11][12][13]

APT28 reportedly compromised the Hillary Clinton campaign, the Democratic National Committee, and the Democratic Congressional Campaign Committee in 2016 in an attempt to interfere with the U.S. presidential election.[5] In 2018, the US indicted five GRU Unit 26165 officers associated with APT28 for cyber operations (including close-access operations) conducted between 2014 and 2018 against the World Anti-Doping Agency (WADA), the US Anti-Doping Agency, a US nuclear facility, the Organization for the Prohibition of Chemical Weapons (OPCW), the Spiez Swiss Chemicals Laboratory, and other organizations.[14] Some of these were conducted with the assistance of GRU Unit 74455, which is also referred to as Sandworm Team.

Malware Enterprise

S0016: P2P ZeuS

P2P ZeuS is a closed-source fork of the leaked version of the ZeuS botnet. It presents improvements over the leaked version, including a peer-to-peer architecture. [1]

Windows
Malware Enterprise

S9020: LODEINFO

LODEINFO is a fileless backdoor malware first identified in 2020 that has been used by actors including MirrorFace, primarily against media, diplomatic, governmental, and public sector organizations in Japan.[1][2][3]

Windows
Malware Enterprise

S0574: BendyBear

BendyBear is an x64 shellcode for a stage-zero implant designed to download malware from a C2 server. First discovered in August 2020, BendyBear shares a variety of features with Waterbear, malware previously attributed to the Chinese cyber espionage group BlackTech.[1]

Windows
Malware Enterprise

S0022: Uroburos

Uroburos is a sophisticated cyber espionage tool written in C that has been used by units within Russia's Federal Security Service (FSB) associated with the Turla toolset to collect intelligence on sensitive targets worldwide. Uroburos has several variants and has undergone nearly constant upgrade since its initial development in 2003 to keep it viable after public disclosures. Uroburos is typically deployed to external-facing nodes on a targeted network and has the ability to leverage additional tools and TTPs to further exploit an internal network. Uroburos has interoperable implants for Windows, Linux, and macOS, employs a high level of stealth in communications and architecture, and can easily incorporate new or replacement components.[1][2]

LinuxWindowsmacOS
Malware Enterprise

S0435: PLEAD

PLEAD is a remote access tool (RAT) and downloader used by BlackTech in targeted attacks in East Asia including Taiwan, Japan, and Hong Kong.[1][2] PLEAD has also been referred to as TSCookie, though more recent reporting indicates likely separation between the two. PLEAD was observed in use as early as March 2017.[3][2]

Windows
Malware Enterprise

S1164: UPSTYLE

UPSTYLE is a Python-based backdoor associated with exploitation of Palo Alto firewalls using CVE-2024-3400 in early 2024. UPSTYLE has only been observed in relation to this exploitation activity, which involved attempted install on compromised devices by the threat actor UTA0218.[1][2]

Network DevicesLinux
Malware Enterprise

S1020: Kevin

Kevin is a backdoor implant written in C++ that has been used by HEXANE since at least June 2020, including in operations against organizations in Tunisia.[1]

Windows
Malware Enterprise

S0588: GoldMax

GoldMax is a second-stage C2 backdoor written in Go with Windows and Linux variants that are nearly identical in functionality. GoldMax was discovered in early 2021 during the investigation into the SolarWinds Compromise, and has likely been used by APT29 since at least mid-2019. GoldMax uses multiple defense evasion techniques, including avoiding virtualization execution and masking malicious traffic.[1][2][3]

WindowsLinux
Relationship explorer

All related ATT&CK context

uses · Malware S0016: P2P ZeuS Enterprise uses · Malware S0134: Downdelph Enterprise uses · Malware S1047: Mori Enterprise uses · Malware S9020: LODEINFO Enterprise uses · Malware S0574: BendyBear Enterprise subtechnique of · Technique T1001: Data Obfuscation Enterprise uses · Malware S0682: TrailBlazer Enterprise uses · Malware S0022: Uroburos Enterprise uses · Malware S0626: P8RAT Enterprise mitigates · Mitigation M1031: Network Intrusion Prevention Enterprise uses · Malware S0435: PLEAD Enterprise uses · Malware S1164: UPSTYLE Enterprise uses · Malware S1020: Kevin Enterprise uses · Malware S0588: GoldMax Enterprise uses · Malware S1246: BeaverTail Enterprise uses · Malware S0559: SUNBURST Enterprise uses · Malware S0647: Turian Enterprise uses · Malware S0632: GrimAgent Enterprise uses · Group G0007: APT28 Enterprise detects · Detection Strategy DET0011: Detecting Junk Data in C2 Channels via Behavioral Analysis Enterprise uses · Malware S0514: WellMess Enterprise
Mitigations

Mitigation direction

Change history

Object version and sync metadata

The fields below describe the current mirrored snapshot. When Glexia retains multiple ATT&CK source imports, you can open the table to compare the same object across releases (hashes and MITRE timestamps). For MITRE’s own release notes and roadmap, see ATT&CK resources — Updates .

ATT&CK release
19.1
Object version
1.1
Created
Modified
Raw hash
fe942b11ab3a6d00...
Imported snapshots across ATT&CK releases (1)
Release Bundle imported Object version Modified Status Raw hash
19.1 1.1 Current bundle fe942b11ab3a…
Raw source

Mirrored ATT&CK source object

The raw object is retained through the mirrored ATT&CK source bundle and object hash. The raw endpoint returns the exact object from the mirrored bundle when available.

Open mirrored raw JSON Open MITRE-hosted copy
Source references

External references and citations

MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.

  1. [1]
    FireEye SUNBURST Backdoor December 2020

    FireEye. (2020, December 13). Highly Evasive Attacker Leverages SolarWinds Supply Chain to Compromise Multiple Global Victims With SUNBURST Backdoor. Retrieved January 4, 2021.

    Open source URL
  2. [2]
    University of Birmingham C2

    Gardiner, J., Cova, M., Nagaraja, S. (2014, February). Command & Control Understanding, Denying and Detecting. Retrieved April 20, 2016.

    Open source URL
  3. [3]
    mitre-attack T1001.001
    Open source URL
Source and licensing

Source: MITRE ATT&CK®. © 2026 The MITRE Corporation. This work is reproduced and distributed with the permission of The MITRE Corporation. MITRE ATT&CK and ATT&CK are registered trademarks of The MITRE Corporation. Glexia is not affiliated with or endorsed by MITRE.

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