Attack Ent T1129 Shared Modules

MITRE ATT&CK T1129: Shared Modules

When to use this skill

Use this skill when the task involves T1129, Shared Modules, enterprise ATT&CK, TTP mapping, detection engineering, hunting, incident-response enrichment, control validation, or authorized adversary-emulation planning. Treat it as a defensive analysis aid: keep outputs focused on understanding, detecting, mitigating, and safely validating this ATT&CK technique.

Technique context

• ATT&CK domain: enterprise
• ATT&CK ID: T1129
• Technique name: Shared Modules
• Type: technique
• ATT&CK URL: https://attack.mitre.org/techniques/T1129
• Tactics: execution
• Platforms: Linux, macOS, Windows
• Required permissions: Not specified
• Effective permissions: Not specified
• Defenses bypassed: Not specified

ATT&CK description

Adversaries may execute malicious payloads via loading shared modules. Shared modules are executable files that are loaded into processes to provide access to reusable code, such as specific custom functions or invoking OS API functions (i.e., Native API).

Adversaries may use this functionality as a way to execute arbitrary payloads on a victim system. For example, adversaries can modularize functionality of their malware into shared objects that perform various functions such as managing C2 network communications or execution of specific actions on objective.

The Linux & macOS module loader can load and execute shared objects from arbitrary local paths. This functionality resides in dlfcn.h in functions such as dlopen and dlsym. Although macOS can execute .so files, common practice uses .dylib files.(Citation: Apple Dev Dynamic Libraries)(Citation: Linux Shared Libraries)(Citation: RotaJakiro 2021 netlab360 analysis)(Citation: Unit42 OceanLotus 2017)

The Windows module loader can be instructed to load DLLs from arbitrary local paths and arbitrary Universal Naming Convention (UNC) network paths. This functionality resides in NTDLL.dll and is part of the Windows Native API which is called from functions like LoadLibrary at run time.(Citation: Microsoft DLL)

Agent workflow

1. Clarify scope: identify the system, asset class, log sources, cloud or endpoint platform, and whether the user wants triage, detection, coverage assessment, or safe emulation planning.
2. Load bundled resources as needed: use references/technique-profile.json for structured metadata, references/detection-and-mitigation.md for triage and telemetry guidance, references/known-threat-context.md for ATT&CK relationship context, and templates/ for repeatable outputs.
3. Map observations to ATT&CK: compare the user's evidence to the ATT&CK description, tactics, platforms, and known procedure patterns before asserting a match.
4. Produce defensive outputs: prioritize hypotheses, telemetry requirements, detection logic ideas, validation steps, containment guidance, and mitigations.
5. Preserve uncertainty: distinguish confirmed evidence, plausible indicators, assumptions, and gaps. Recommend what to collect next.
6. Stay safe: do not provide malware, credential theft, persistence, evasion, destructive automation, or unauthorized exploitation instructions. For adversary emulation, keep steps bounded to approved lab or control-validation contexts and omit operational abuse details.

Bundled resources

• references/technique-profile.json: machine-readable ATT&CK metadata for this technique.
• references/detection-and-mitigation.md: detection notes, telemetry checklist, triage questions, mitigation candidates, and false-positive considerations.
• references/known-threat-context.md: ATT&CK relationship context with attribution cautions.
• templates/detection-brief.md: detection engineering brief template.
• templates/hunt-plan.md: threat hunt plan template.
• templates/incident-response-note.md: incident response note template.
• templates/coverage-assessment.md: ATT&CK coverage assessment template.
• scripts/render_brief.py: local helper that renders a Markdown defensive brief from technique-profile.json.
• assets/output-schema.json: JSON schema for structured technique analysis outputs.

To generate a quick brief, run python scripts/render_brief.py --output brief.md from inside this skill directory, or adapt the templates directly.

Detection guidance

No ATT&CK detection guidance was present in the source STIX object.

Useful telemetry and data sources

• Not specified in the STIX object.

Mitigations to consider

• Execution Prevention

Known threat context

Use these examples only as contextual leads, not as proof that an observed event is this technique:

• Astaroth (malware)
• Attor (malware)
• BLINDINGCAN (malware)
• BOOSTWRITE (malware)
• Bumblebee (malware)
• DarkWatchman (malware)
• Dtrack (malware)
• Ebury (malware)
• FoggyWeb (malware)
• Hydraq (malware)
• KillDisk (malware)
• LightSpy (malware)
• Metamorfo (malware)
• Mustang Panda (intrusion-set)
• OSX_OCEANLOTUS.D (malware)
• PUNCHBUGGY (malware)
• PipeMon (malware)
• RotaJakiro (malware)
• Stuxnet (malware)
• TajMahal (malware)

Recommended output pattern

When responding with this skill, structure the answer as:

• Assessment: whether the evidence supports this ATT&CK mapping and why.
• Evidence: specific indicators, logs, behaviors, and assumptions.
• Detection: telemetry sources, analytic logic, and tuning considerations.
• Response: containment, eradication, recovery, and validation actions.
• Coverage gaps: missing logs, sensors, controls, or environmental details.
• References: include the ATT&CK URL and any user-provided evidence references.

ATT&CK contributors

• Stefan Kanthak