Agent skill

Detecting Process Injection Techniques

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading.

Apache-2.0Auto-check passedSecurity

Install Detecting Process Injection Techniques

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-process-injection-techniques -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-process-injection-techniques --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-process-injection-techniques .claude/skills/detecting-process-injection-techniques && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
detecting-process-injection-techniques
GitHub stars
34k
Token cost
~3.5k tokens
SKILL.md length
639 words
Files
4 (incl. scripts, references)
Skills in repo
639
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading.

  • Works in 6 steps: Identify Injection via Memory Forensics → Classify the Injection Technique → Detect Injection via Sysmon Events → …
  • Tasks that involve Digital forensics
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3; reaches schemas.microsoft.com

What it does

Detecting Process Injection Techniques is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat detection.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Digital forensics. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Digital forensics

Example prompts

  • “Use the detecting-process-injection-techniques skill to detect and analyzes process injection techniques used by malware including classic DLL…”
  • “/detecting-process-injection-techniques”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Identify Injection via Memory Forensics
  2. Classify the Injection Technique
  3. Detect Injection via Sysmon Events
  4. Analyze Injected Code
  5. Map to MITRE ATT&CK
  6. Create Detection Signatures

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • schemas.microsoft.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Process Injection Techniques loads about 3.5k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 115 tokens; SKILL.md has 639 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~115
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 639 words, ~3,513 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-process-injection-techniques/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-process-injection-techniques
description
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat detection.
domain
cybersecurity
subdomain
malware-analysis
tags
malware, process-injection, detection, memory-forensics, defense-evasion
version
1.0.0
author
mahipal
license
Apache-2.0
d3fend_techniques
Executable Denylisting, Execution Isolation, File Metadata Consistency Validation, Content Format Conversion, File Content Analysis
nist_csf
DE.AE-02, RS.AN-03, ID.RA-01, DE.CM-01
mitre_attack
T1027, T1055, T1140, T1497, T1070

Detecting Process Injection Techniques

When to Use

  • EDR alerts on suspicious API call sequences (VirtualAllocEx + WriteProcessMemory + CreateRemoteThread)
  • A legitimate process (explorer.exe, svchost.exe) exhibits unexpected network connections or file operations
  • Memory forensics reveals executable code in memory regions that should not contain it
  • Investigating living-off-the-land attacks where malware hides inside trusted processes
  • Building detection logic for specific injection techniques in EDR or SIEM rules

Do not use for standard DLL loading analysis; injection implies unauthorized code placement in a process without that process's cooperation.

Prerequisites

  • Volatility 3 for memory forensics analysis of injection artifacts
  • Sysmon configured with Event IDs 8 (CreateRemoteThread) and 10 (ProcessAccess)
  • API Monitor or x64dbg for observing injection API calls in real-time
  • Process Hacker or Process Explorer for inspecting process memory regions
  • Understanding of Windows memory management (VirtualAlloc, VAD, page protections)
  • Isolated analysis environment for safe malware execution and monitoring

Workflow

Step 1: Identify Injection via Memory Forensics

Use Volatility to detect injected code in process memory:

bash
# malfind: Primary injection detection plugin
vol3 -f memory.dmp windows.malfind

# malfind detects:
# - Memory regions with PAGE_EXECUTE_READWRITE (RWX) protection
# - PE headers (MZ signature) in non-image VAD entries
# - Executable memory not backed by a file on disk

# Filter by specific process
vol3 -f memory.dmp windows.malfind --pid 852

# Dump injected memory regions for analysis
vol3 -f memory.dmp windows.malfind --dump

# Check VAD (Virtual Address Descriptor) tree for anomalies
vol3 -f memory.dmp windows.vadinfo --pid 852

# Detect hollowed processes (mapped image doesn't match disk)
vol3 -f memory.dmp windows.hollowfind
Step 2: Classify the Injection Technique

Identify which injection method was used based on artifacts:

Process Injection Techniques and Detection Artifacts:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

1. Classic DLL Injection
   APIs: OpenProcess -> VirtualAllocEx -> WriteProcessMemory -> CreateRemoteThread
   Artifact: Loaded DLL in target process not present in known-good baseline
   Detection: New DLL in dlllist not matching disk hash, CreateRemoteThread event

2. Process Hollowing (RunPE)
   APIs: CreateProcess(SUSPENDED) -> NtUnmapViewOfSection -> VirtualAllocEx ->
         WriteProcessMemory -> SetThreadContext -> ResumeThread
   Artifact: Process image in memory doesn't match file on disk
   Detection: hollowfind plugin, mismatched PE headers vs disk file

3. APC Injection
   APIs: OpenProcess -> VirtualAllocEx -> WriteProcessMemory -> QueueUserAPC
   Artifact: Alertable thread has queued APC pointing to injected code
   Detection: Thread start addresses outside known modules

4. Thread Hijacking
   APIs: OpenProcess -> VirtualAllocEx -> WriteProcessMemory ->
         SuspendThread -> GetThreadContext -> SetThreadContext -> ResumeThread
   Artifact: Thread instruction pointer changed to injected code
   Detection: Thread context modification, EIP/RIP outside module boundaries

5. Reflective DLL Injection
   APIs: VirtualAllocEx -> WriteProcessMemory -> CreateRemoteThread (to reflective loader)
   Artifact: DLL loaded in memory but NOT in loaded module list
   Detection: malfind (PE in non-image memory), module not in ldrmodules

6. Process Doppelganging
   APIs: NtCreateTransaction -> NtCreateFile(transacted) -> NtWriteFile ->
         NtCreateSection -> NtRollbackTransaction -> NtCreateProcessEx
   Artifact: Process created from transacted file that was rolled back
   Detection: Process with no corresponding file on disk

7. AtomBombing
   APIs: GlobalAddAtom -> NtQueueApcThread (with GlobalGetAtomName)
   Artifact: Code stored in global atom table, APC triggers copy to target
   Detection: Unusual atom table entries, APC injection indicators
Step 3: Detect Injection via Sysmon Events

Analyze Sysmon and Windows Event Log data:

bash
# Sysmon Event ID 8: CreateRemoteThread
# Detect when one process creates a thread in another
wevtutil qe "Microsoft-Windows-Sysmon/Operational" \
  /q:"*[System[EventID=8]]" /f:text /c:20

# Sysmon Event ID 10: ProcessAccess
# Detect suspicious access rights to other processes
# DesiredAccess containing PROCESS_VM_WRITE (0x0020) + PROCESS_CREATE_THREAD (0x0002)
wevtutil qe "Microsoft-Windows-Sysmon/Operational" \
  /q:"*[System[EventID=10]]" /f:text /c:20

# Sysmon Event ID 1: Process Creation
# Detect process hollowing via suspicious parent-child relationships
wevtutil qe "Microsoft-Windows-Sysmon/Operational" \
  /q:"*[System[EventID=1]]" /f:text /c:20
python
# Parse Sysmon events for injection indicators
import xml.etree.ElementTree as ET
import subprocess

# Query CreateRemoteThread events
result = subprocess.run(
    ["wevtutil", "qe", "Microsoft-Windows-Sysmon/Operational",
     "/q:*[System[EventID=8]]", "/f:xml", "/c:100"],
    capture_output=True, text=True
)

suspicious_injections = []
for event_xml in result.stdout.split("</Event>"):
    if not event_xml.strip():
        continue
    try:
        root = ET.fromstring(event_xml + "</Event>")
        ns = {"e": "http://schemas.microsoft.com/win/2004/08/events/event"}
        data = {}
        for d in root.findall(".//e:EventData/e:Data", ns):
            data[d.get("Name")] = d.text

        source = data.get("SourceImage", "")
        target = data.get("TargetImage", "")

        # Flag injections from unusual sources into system processes
        system_procs = ["svchost.exe", "explorer.exe", "lsass.exe", "winlogon.exe"]
        if any(p in target.lower() for p in system_procs):
            if not any(p in source.lower() for p in ["csrss.exe", "services.exe", "lsass.exe"]):
                print(f"[!] Suspicious injection: {source} -> {target}")
                suspicious_injections.append(data)
    except:
        pass
Step 4: Analyze Injected Code

Examine the injected payload to understand its purpose:

bash
# Dump injected code from Volatility malfind
vol3 -f memory.dmp windows.malfind --pid 852 --dump

# Analyze the dumped region
file malfind.*.dmp

# If it contains a PE (MZ header), analyze as a standalone executable
python3 << 'PYEOF'
import pefile

# Attempt to parse as PE
try:
    pe = pefile.PE("malfind.852.0x400000.dmp")
    print("Injected PE detected!")
    print(f"  Architecture: {'x64' if pe.FILE_HEADER.Machine == 0x8664 else 'x86'}")
    print(f"  Imports:")
    if hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'):
        for entry in pe.DIRECTORY_ENTRY_IMPORT:
            print(f"    {entry.dll.decode()}: {len(entry.imports)} functions")
except:
    print("Not a valid PE - likely shellcode")
    # Analyze as shellcode
    with open("malfind.852.0x400000.dmp", "rb") as f:
        shellcode = f.read()
    print(f"  Size: {len(shellcode)} bytes")
    print(f"  First bytes: {shellcode[:32].hex()}")
PYEOF

# Disassemble shellcode
python3 -c "
from capstone import Cs, CS_ARCH_X86, CS_MODE_64
with open('malfind.852.0x400000.dmp', 'rb') as f:
    code = f.read()[:256]
md = Cs(CS_ARCH_X86, CS_MODE_64)
for insn in md.disasm(code, 0x400000):
    print(f'  0x{insn.address:X}: {insn.mnemonic} {insn.op_str}')
"

# Scan with YARA for known payloads
vol3 -f memory.dmp yarascan.YaraScan --pid 852 --yara-file malware_rules.yar
Step 5: Map to MITRE ATT&CK

Classify detected techniques in the ATT&CK framework:

MITRE ATT&CK Process Injection Sub-Techniques (T1055):
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
T1055.001  Dynamic-link Library Injection
T1055.002  Portable Executable Injection
T1055.003  Thread Execution Hijacking
T1055.004  Asynchronous Procedure Call (APC)
T1055.005  Thread Local Storage
T1055.008  Ptrace System Calls (Linux)
T1055.009  Proc Memory (/proc/pid/mem - Linux)
T1055.011  Extra Window Memory Injection
T1055.012  Process Hollowing
T1055.013  Process Doppelganging
T1055.014  VDSO Hijacking (Linux)
T1055.015  ListPlanting
Step 6: Create Detection Signatures

Build detection rules for the identified technique:

yaml
# Sigma rule for CreateRemoteThread injection
title: Suspicious CreateRemoteThread into System Process
logsource:
    product: windows
    service: sysmon
detection:
    selection:
        EventID: 8
        TargetImage|endswith:
            - '\svchost.exe'
            - '\explorer.exe'
            - '\lsass.exe'
    filter:
        SourceImage|endswith:
            - '\csrss.exe'
            - '\services.exe'
            - '\svchost.exe'
    condition: selection and not filter
level: high

Key Concepts

TermDefinition
Process InjectionTechnique of executing code within the address space of another process, typically to evade detection and inherit the target's trust level
Process HollowingCreating a legitimate process in suspended state, unmapping its memory, writing malicious code, and resuming execution to masquerade as the legitimate process
Reflective DLL InjectionLoading a DLL into a process's memory without using the Windows loader, so the DLL does not appear in the loaded module list
APC InjectionQueuing an Asynchronous Procedure Call to a thread in the target process, causing it to execute injected code when the thread enters an alertable state
VAD (Virtual Address Descriptor)Windows kernel structure describing memory regions in a process; anomalous VAD entries (RWX permissions, non-image PE) indicate injection
CreateRemoteThreadWindows API creating a thread in another process; the primary mechanism for classic DLL injection and many other injection techniques
PAGE_EXECUTE_READWRITEMemory protection allowing read, write, and execute; rarely used by legitimate applications, common indicator of injected code
Show full SKILL.md (248 more words)Show less

Tools & Systems

  • Volatility (malfind): Memory forensics plugin detecting injected code through VAD analysis and PE header scanning in non-image memory regions
  • Sysmon: System Monitor providing detailed Windows event logging including CreateRemoteThread (EID 8) and ProcessAccess (EID 10)
  • Process Hacker: Advanced process management tool showing detailed memory regions, thread stacks, and injected modules
  • API Monitor: Windows tool for monitoring and logging API calls made by processes, useful for observing injection sequences in real-time
  • pe-sieve: Tool scanning running processes for signs of code injection, hooking, and hollowing

Common Scenarios

Scenario: Investigating a Hollowed svchost.exe Process

Context: EDR alerts on svchost.exe making HTTPS connections to an external IP. Svchost.exe should only communicate with Microsoft services. Memory analysis is needed to confirm process hollowing.

Approach:

  1. Capture memory dump of the suspicious svchost.exe process
  2. Run Volatility malfind to detect injected PE in the process memory
  3. Compare the in-memory image base with the on-disk svchost.exe file hash
  4. Check the process parent (should be services.exe) and creation parameters
  5. Dump the hollowed executable from memory and analyze with Ghidra
  6. Run netscan to confirm the network connections from the hollowed process
  7. Scan dumped code with YARA for malware family identification

Pitfalls:

  • Assuming all svchost.exe instances are identical (each loads different service DLLs)
  • Not checking the parent process (hollowed processes often have wrong parents)
  • Relying only on process name matching (attackers specifically target svchost.exe because multiple instances are expected)
  • Missing the injection source process that may have already terminated

Output Format

PROCESS INJECTION ANALYSIS REPORT
====================================
Dump File:        memory.dmp
Analysis Tool:    Volatility 3.2 + Sysmon

INJECTION DETECTED
Target Process:   svchost.exe (PID: 852)
Source Process:    malware.exe (PID: 2184) [terminated]
Technique:        Process Hollowing (T1055.012)

EVIDENCE
malfind Results:
  PID 852 (svchost.exe):
    Address: 0x00400000
    Size:    184,320 bytes
    Protection: PAGE_EXECUTE_READWRITE
    Header: MZ (PE32 executable)
    NOT backed by disk file

Process Verification:
  Expected Image: C:\Windows\System32\svchost.exe (SHA-256: aaa...)
  In-Memory Image: Unknown PE (SHA-256: bbb...)
  Result: MISMATCH - HOLLOWED PROCESS

Sysmon Events:
  [4688] malware.exe (PID 2184) created svchost.exe (PID 852) SUSPENDED
  [10]   malware.exe accessed svchost.exe with PROCESS_VM_WRITE
  [8]    malware.exe created remote thread in svchost.exe

INJECTED PAYLOAD ANALYSIS
SHA-256:          bbb123def456...
YARA Match:       CobaltStrike_Beacon_x64
Type:             Cobalt Strike Beacon (HTTP)
C2:               hxxps://185.220.101[.]42/updates

MITRE ATT&CK
T1055.012  Process Hollowing
T1071.001  Web Protocols (HTTPS C2)
T1036.005  Match Legitimate Name (svchost.exe)

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/detecting-process-injection-techniques of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Detecting Process Injection Techniques

What does Detecting Process Injection Techniques do?

Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Detecting Process Injection Techniques is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading.

When should I use Detecting Process Injection Techniques?

Detecting Process Injection Techniques fits situations like: tasks that involve Digital forensics.

How do I install Detecting Process Injection Techniques in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-process-injection-techniques -a claude-code`. Or copy the skill folder (skills/detecting-process-injection-techniques in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-process-injection-techniques in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Process Injection Techniques in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-process-injection-techniques -a codex`. Or copy the skill folder (skills/detecting-process-injection-techniques in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-process-injection-techniques in your project. Codex loads it when a task matches its description.

Can I use Detecting Process Injection Techniques in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-process-injection-techniques -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-process-injection-techniques, .gemini/skills/detecting-process-injection-techniques, .github/skills/detecting-process-injection-techniques and .opencode/skills/detecting-process-injection-techniques in your project.

What does Detecting Process Injection Techniques need to run?

Going by SKILL.md and its folder, Detecting Process Injection Techniques needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Detecting Process Injection Techniques access the network?

SKILL.md names 1 domain. In commands or code: schemas.microsoft.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Detecting Process Injection Techniques safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Process Injection Techniques use?

Detecting Process Injection Techniques is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Process Injection Techniques use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 662 tokens, read only when the agent opens those files.

What are the alternatives to Detecting Process Injection Techniques?

Skills that share tags, products or a category with Detecting Process Injection Techniques: Oss Forensics (Tommy-yw/RunbookHermes, 546 stars), Ctf Malware (ljagiello/ctf-skills, 3.4k stars), Dfir (transilienceai/communitytools, 559 stars) and TShark Traffic Analysis (AgentSecOps/SecOpsAgentKit, 219 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Process Injection Techniques?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,870 GitHub stars. The repository holds 639 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.