# Attack Ent T1055 001 Dynamic Link Library Injection

> Analyze MITRE ATT&CK T1055.001 Dynamic-link Library Injection in the enterprise matrix. Use for TTP triage, detection engineering, hunting, defensive emulation planning, mitigations, incident response mapping, ATT&CK coverage, or questions mentioning T1055.001, Dynamic-link Library Injection, or enterprise ATT&CK. Adversaries may inject dynamic-link libraries (DLLs) into processes in order to evade process-based defenses as well as possibly elevate privileges.

- Skill: `santosomar/attack-ent-t1055-001-dynamic-link-library-injection` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add santosomar/attack-ent-t1055-001-dynamic-link-library-injection`
- Raw SKILL.md: https://api.skillmd.com/api/skills/santosomar/attack-ent-t1055-001-dynamic-link-library-injection/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MITRE ATT&CK Terms of Use apply to ATT&CK-derived content. See h
- Author: santosomar (https://skillmd.com/u/santosomar)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/santosomar/attack-ent-t1055-001-dynamic-link-library-injection

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# MITRE ATT&CK T1055.001: Dynamic-link Library Injection

## When to use this skill

Use this skill when the task involves T1055.001, Dynamic-link Library Injection, 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 sub-technique.

## Technique context

- ATT&CK domain: enterprise
- ATT&CK ID: T1055.001
- Technique name: Dynamic-link Library Injection
- Type: sub-technique
- ATT&CK URL: https://attack.mitre.org/techniques/T1055/001
- Tactics: privilege-escalation, stealth
- Platforms: Windows
- Required permissions: Not specified
- Effective permissions: Not specified
- Defenses bypassed: Not specified

## ATT&CK description

Adversaries may inject dynamic-link libraries (DLLs) into processes in order to evade process-based defenses as well as possibly elevate privileges. DLL injection is a method of executing arbitrary code in the address space of a separate live process.  

DLL injection is commonly performed by writing the path to a DLL in the virtual address space of the target process before loading the DLL by invoking a new thread. The write can be performed with native Windows API calls such as <code>VirtualAllocEx</code> and <code>WriteProcessMemory</code>, then invoked with <code>CreateRemoteThread</code> (which calls the <code>LoadLibrary</code> API responsible for loading the DLL). (Citation: Elastic Process Injection July 2017) 

Variations of this method such as reflective DLL injection (writing a self-mapping DLL into a process) and memory module (map DLL when writing into process) overcome the address relocation issue as well as the additional APIs to invoke execution (since these methods load and execute the files in memory by manually preforming the function of <code>LoadLibrary</code>).(Citation: Elastic HuntingNMemory June 2017)(Citation: Elastic Process Injection July 2017) 

Another variation of this method, often referred to as Module Stomping/Overloading or DLL Hollowing, may be leveraged to conceal injected code within a process. This method involves loading a legitimate DLL into a remote process then manually overwriting the module's <code>AddressOfEntryPoint</code> before starting a new thread in the target process.(Citation: Module Stomping for Shellcode Injection) This variation allows attackers to hide malicious injected code by potentially backing its execution with a legitimate DLL file on disk.(Citation: Hiding Malicious Code with Module Stomping) 

Running code in the context of another process may allow access to the process's memory, system/network resources, and possibly elevated privileges. Execution via DLL injection may also evade detection from security products since the execution is masked under a legitimate process.

## 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

- Behavior Prevention on Endpoint

## Known threat context

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

- Aria-body (malware)
- BADHATCH (malware)
- BackdoorDiplomacy (intrusion-set)
- BlackEnergy (malware)
- Bumblebee (malware)
- C0015 (campaign)
- Carberp (malware)
- Carbon (malware)
- Cobalt Strike (malware)
- ComRAT (malware)
- Conti (malware)
- DarkTortilla (malware)
- Derusbi (malware)
- Duqu (malware)
- Dyre (malware)
- Elise (malware)
- Emissary (malware)
- Emotet (malware)
- FinFisher (malware)
- FunnyDream (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

- Boominathan Sundaram