analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded…
Skill by mukul975
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: data
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/analyzing-memory-dumps-with-volatility
What's inside
- Skill name
analyzing-memory-dumps-with-volatility- Description
- Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.
- License
- Apache-2.0
- Instructions
- 284 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- body: contains an absolute path from the author's machine
Files (4)
- LICENSE
- SKILL.md
- api-reference.md · references/
- agent.py · scripts/
SKILL.md (first lines)
# Analyzing Memory Dumps with Volatility ## When to Use - A compromised system's RAM has been captured and needs forensic analysis for malware artifacts - Detecting fileless malware that exists only in memory without persistent disk artifacts - Extracting encryption keys, passwords, or decrypted configuration from process memory - Identifying process injection, DLL injection, or process hollowing in a compromised system - Analyzing rootkit activity that hides from standard disk-based forensic tools **Do not use** for disk image analysis; use Autopsy, FTK, or Sleuth Kit for disk forensics. ## Prerequisites - Volatility 3 installed (`pip install volatility3`) with symbol tables for target OS - Memory dump file acquired from the target system (using WinPmem, LiME, or DumpIt) - Knowledge of the source OS version for correct profile/symbol selection - Sufficient disk space (memory dumps can be 4-64 GB) - YARA rules for scanning memory for known malware signatures - Strings utility for extracting readable strings from memory regions ## Workflow ### Step 1: Identify the Memory Dump Profile Determine the operating system and version from the memory dump: ```bash # Volatility 3: Automatic OS detection vol3 -f memory.dmp windows.info # List available plugins vol3 -f memory.dmp --help # If symbols are needed, download from: # https://downloads.volatilityfoundation.org/volatility3/symbols/ # For Volatility 2 (legacy): vol2 -f memory.dmp imageinfo vol2 -f memory.dmp kdbgscan ``` ### Step 2: Enumerate Running Processes List all processes and identify suspicious entries: ```bash # List all processes vol3 -f memory.dmp windows.pslist # Process tree (parent-child relationships) vol3 -f memory.dmp windows.pstree # Scan for hidden/unlinked processes (rootkit detection) vol3 -f memory.dmp windows.psscan # Compare pslist vs psscan to find hidden processes # Processes in psscan but not pslist are potentially hidden by rootkits # Check for process hollowing vol3 -f memory.dmp windows.pslist --dump # Then verify the dumped EXE matches the expected binary on disk ``` ``` Suspicious Process Indicators: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - svchost.exe not spawned by services.exe (wrong parent) - csrss.exe/lsass.exe with unusual parent process - Multiple instances of lsass.exe (should be only one) - Processes with misspelled names (scvhost.exe, lssas.exe) - cmd.exe or powershell.exe spawned by WINWORD.EXE or browser - Processes running from unusual paths (%TEMP%, %APPDATA%) - Processes with no parent (orphaned - parent terminated) ``` ### Step 3: Detect Malicious Code Injection Scan for injected code and process hollowing: ```bash # Detect injected code in processes (malfind) vol3 -f memory.dmp windows.malfind # Malfind looks for: # - Memory regions with PAGE_EXECUTE_READWRITE protection # - Memory regions containing PE headers (MZ/PE signature) # - VAD (Virtual Address Descriptor) anomalies # Dump injected memory regions for analysis vol3 -f memory.dmp windows.malfind --dump --pid 2184 # List loaded DLLs per process vol3 -f memory.dmp windows.dlllist --pid 2184 # Detect hollowed processes by comparing mapped image to disk vol3 -f memory.dmp windows.hollowfind # Scan for loaded drivers (potential rootkit drivers) vol3 -f memory.dmp windows.driverscan # List kernel modules vol3 -f memory.dmp windows.modules ``` ### Step 4: Analyze Network Connections Extract active and closed network connections: ```bash # List all network connections (active and listening) vol3 -f memory.dmp windows.netscan # Output columns: Offset, Protocol, LocalAddr, LocalPort, ForeignAddr, ForeignPort, State, PID, Owner # Filter for established connections to external IPs vol3 -f memory.dmp windows.netscan | grep ESTABLISHED # For older Windows (XP/2003):
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Signals
- Stars
- 34,941
- Forks
- 4,256
- Open issues
- 24
- Last commit (this folder)
- Apr 6, 2026 (7 months ago)
- Last release
- Jun 22, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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analyzing-memory-dumps-with-volatility skill. Agent Mart. https://agentmart.dev/skills/mukul975-analyzing-memory-dumps-with-volatility (last updated 2026-10-24)