--- name: conducting-memory-forensics-with-volatility-1959b65c644e description: 'Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition. ' domain: cybersecurity subdomain: incident-response tags: - memory-forensics - volatility - RAM-analysis - process-injection - DFIR mitre_attack: - T1055 - T1003.001 - T1014 - T1059.001 - T1620 version: 1.0.0 author: mahipal license: Apache-2.0 nist_csf: - RS.MA-01 - RS.MA-02 - RS.AN-03 - RC.RP-01 --- # Conducting Memory Forensics with Volatility ## When to Use - An endpoint has been contained during an active incident and volatile evidence must be preserved - EDR alerts suggest process injection or fileless malware that only exists in memory - Encryption keys need to be recovered from a ransomware-infected system before shutdown - Credential theft (Mimikatz, LSASS dumping) is suspected and evidence must be confirmed - A rootkit or kernel-level compromise is suspected and disk-based analysis is insufficient **Do not use** for analyzing disk images or file system artifacts; use disk forensics tools (Autopsy, FTK) for those tasks. ## Prerequisites - Memory acquisition tool deployed or available: WinPmem, Magnet RAM Capture, DumpIt, or AVML (Linux) - Volatility 3 installed with Python 3.8+ and required symbol tables - Sufficient storage for memory dumps (equal to system RAM size, typically 8-64 GB) - YARA rules for malware detection in memory (Florian Roth's signature-base, custom rules) - Reference baseline of normal processes and DLLs for the OS version being analyzed - Chain of custody documentation for evidence handling ## Workflow ### Step 1: Acquire Memory Image Capture RAM from the target system using a forensically sound method: **Windows (WinPmem):** ``` winpmem_mini_x64.exe output.raw ``` **Windows (Magnet RAM Capture):** ``` MagnetRAMCapture.exe # GUI-based, select output path, generates .raw file ``` **Windows (DumpIt):** ``` DumpIt.exe # Creates memory dump in current directory automatically ``` **Linux (AVML - Acquire Volatile Memory for Linux):** ``` ./avml output.lime ``` Document acquisition metadata: ``` Acquisition Record: ━━━━━━━━━━━━━━━━━ Target Host: WKSTN-042 RAM Size: 16 GB Dump File: WKSTN-042_20251115_1445.raw Dump Size: 16,843,612,160 bytes SHA-256: a4b3c2d1e5f6... Acquisition Tool: WinPmem 4.0 Acquired By: [Analyst Name] Timestamp: 2025-11-15T14:45:00Z ``` ### Step 2: Identify the Operating System and Profile Volatility 3 automatically identifies the OS, but verify: ```bash # Get system information vol -f WKSTN-042_20251115_1445.raw windows.info # Output includes: # OS: Windows 10 22H2 (Build 19045.3693) # Kernel Base: 0xf8066c200000 # DTB: 0x1aa000 # Symbols: ntkrnlmp.pdb ``` ### Step 3: Analyze Running Processes Examine the process tree for suspicious activity: ```bash # List all running processes vol -f memory.raw windows.pslist # Show process tree (parent-child relationships) vol -f memory.raw windows.pstree # Scan for hidden/unlinked processes (rootkit detection) vol -f memory.raw windows.psscan # Compare pslist vs psscan to find hidden processes # Processes in psscan but NOT in pslist may be hidden by rootkits ``` Key indicators of compromise in process analysis: - `svchost.exe` running without `-k` parameter or with wrong parent (should be `services.exe`) - `csrss.exe` or `lsass.exe` with abnormal parent process - Processes with misspelled names (`scvhost.exe`, `lssas.exe`) - Unusual processes spawned by `outlook.exe`, `winword.exe`, or `excel.exe` - Multiple instances of processes that should be singletons (`lsass.exe`, `smss.exe`) ### Step 4: Investigate Network Connections Extract active and recently closed network connections: ```bash # List all network connections vol -f memory.raw windows.netscan # Focus output fields: # Offset Proto LocalAddr LocalPort ForeignAddr ForeignPort State PID Owner # 0xe10... TCPv4 10.1.5.42 49721 185.220.101.42 443 ESTAB 3847 update.exe ``` Cross-reference suspicious connections with the process tree to identify C2 communications. Look for: - Connections to external IPs from unexpected processes - High port numbers connecting to port 443/80 from non-browser processes - Connections from `svchost.exe` or system processes to external IPs ### Step 5: Detect Process Injection and Malware Use malfind to identify injected code and memory-resident malware: ```bash # Detect injected code in processes vol -f memory.raw windows.malfind # Output shows: # PID Process Start End Tag Protection Hexdump/Disassembly # 3847 explorer.exe 0x2a10000 0x2a14000 VadS PAGE_EXECUTE_READWRITE # MZ header detected - injected PE # Dump suspicious process memory vol -f memory.raw windows.memmap --pid 3847 --dump # List DLLs loaded by a suspicious process vol -f memory.raw windows.dlllist --pid 3847 # Scan memory with YARA rules vol -f memory.raw windows.yarascan --yara-file malware_rules.yar ``` ### Step 6: Extract Credentials and Artifacts Recover sensitive data from memory: ```bash # Dump registry hives from memory (for password hash extraction) vol -f memory.raw windows.registry.hivelist vol -f memory.raw windows.hashdump # Extract command line history vol -f memory.raw windows.cmdline # List handles (files, registry keys, mutexes) vol -f memory.raw windows.handles --pid 3847 # Extract clipboard contents vol -f memory.raw windows.clipboard # Dump cached files from memory vol -f memory.raw windows.dumpfiles --pid 3847 ``` ### Step 7: Generate Forensic Report Compile findings into a structured analysis report documenting all evidence extracted from memory: - Process anomalies with PIDs, parent processes, and timestamps - Network connections with associated process context - Injected code regions with memory protection flags - Extracted IOCs (hashes, IPs, domains, mutexes, registry keys) - YARA rule matches with rule names and match offsets - Credential exposure (hashes found, accounts at risk) ## Key Concepts | Term | Definition | |------|------------| | **Volatile Evidence** | Data that exists only in RAM and is lost when a system is powered off; includes running processes, network connections, encryption keys | | **Process Injection** | Technique where malware inserts code into a legitimate process's memory space to evade detection (malfind detects this) | | **EPROCESS** | Windows kernel data structure representing a process; psscan searches for these structures even when unlinked from the active process list | | **VAD (Virtual Address Descriptor)** | Windows kernel structure tracking memory regions allocated to a process; malfind examines VADs for executable but non-file-backed regions | | **Symbol Tables** | OS-specific data structures that Volatility 3 uses to parse memory; downloaded automatically based on detected OS version | | **PAGE_EXECUTE_READWRITE** | Memory protection flag indicating a region is readable, writable, and executable; common indicator of injected malicious code | | **Memory-Resident Malware** | Malware that operates entirely in RAM without writing persistent files to disk, making it invisible to traditional disk-based antivirus | ## Tools & Systems - **Volatility 3**: Primary open-source memory forensics framework; Python 3 rewrite with automatic symbol resolution - **WinPmem / DumpIt / Magnet RAM Capture**: Memory acquisition tools for Windows systems - **AVML (Acquire Volatile Memory for Linux)**: Microsoft's open-source Linux memory acquisition tool - **YARA**: Pattern matching engine for scanning memory dumps against malware signatures and behavioral rules - **MemProcFS**: Memory analysis tool that presents memory as a virtual file system for intuitive browsing ## Common Scenarios ### Scenario: Detecting Cobalt Strike Beacon in Memory **Context**: EDR detects suspicious named pipe activity but cannot identify the source. A memory dump is acquired from the suspect endpoint for analysis. **Approach**: 1. Run `windows.pstree` to identify the process hierarchy and spot abnormal parent-child relationships 2. Run `windows.malfind` to detect injected code regions, particularly in `svchost.exe` or `rundll32.exe` 3. Dump the injected memory region and scan with YARA rules for Cobalt Strike beacon signatures 4. Run `windows.netscan` to identify C2 connections and correlate with the injected process PID 5. Extract the beacon configuration (C2 URLs, sleep time, jitter, watermark) using CobaltStrikeParser 6. Run `windows.cmdline` to identify any post-exploitation commands executed **Pitfalls**: - Analyzing only the process list without running malfind (missing injected code in legitimate processes) - Not capturing memory before isolating the endpoint (EDR containment may trigger malware self-deletion) - Using Volatility 2 profiles instead of Volatility 3 automatic symbol resolution on newer Windows versions ## Output Format ``` MEMORY FORENSICS ANALYSIS REPORT ================================== Incident: INC-2025-1547 Evidence File: WKSTN-042_20251115_1445.raw SHA-256: a4b3c2d1e5f6... OS Identified: Windows 10 22H2 (Build 19045) Analysis Tool: Volatility 3.2.0 PROCESS ANOMALIES PID Process Parent Anomaly 3847 update.exe powershell Suspicious executable in Temp directory 5102 svchost.exe explorer Wrong parent (expected services.exe) --- [hidden] --- Found in psscan but not pslist INJECTED CODE PID Process Address Range Protection Finding 5102 svchost.exe 0x00A10000-0x00A14 PAGE_EXECUTE_READWRITE MZ header (PE injection) NETWORK CONNECTIONS PID Process Local Foreign State 3847 update.exe 10.1.5.42:49721 185.220.101.42:443 ESTABLISHED 5102 svchost.exe 10.1.5.42:51003 91.215.85.17:8443 ESTABLISHED YARA MATCHES Rule: CobaltStrike_Beacon_x64 Match PID: 5102 (svchost.exe) Offset: 0x00A10240 EXTRACTED IOCS Hashes: [SHA-256 of dumped injected code] C2 IPs: 185.220.101.42, 91.215.85.17 C2 Domains: [extracted from beacon config] Mutexes: Global\MSCTF.Shared.MUTEX.ZRQ ``` --- Source attribution: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/conducting-memory-forensics-with-volatility/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/conducting-memory-forensics-with-volatility/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Data Analyst, ML / AI Engineer, Security Engineer Specialties: Specialty not specified Tools: Avml, Dumpit, Magnet Ram Capture, Volatility, Winpmem Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: be1cfa4bed248e62f60f32adaa8c31e0706f3ff93e3e2844aa30dfcab4e3f72c