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mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
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name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack
| name | description | domain | subdomain | tags | version | author | license | nist_csf | mitre_attack | ||||||||||||||||
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| extracting-memory-artifacts-with-rekall | Uses Rekall memory forensics framework to analyze memory dumps for process hollowing, injected code via VAD anomalies, hidden processes, and rootkit detection. Applies plugins like pslist, psscan, vadinfo, malfind, and dlllist to extract forensic artifacts from Windows memory images. Use during incident response memory analysis. | cybersecurity | security-operations |
|
1.0 | mahipal | Apache-2.0 |
|
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Extracting Memory Artifacts with Rekall
When to Use
- When performing authorized security testing that involves extracting memory artifacts with rekall
- When analyzing malware samples or attack artifacts in a controlled environment
- When conducting red team exercises or penetration testing engagements
- When building detection capabilities based on offensive technique understanding
Prerequisites
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
Use Rekall to analyze memory dumps for signs of compromise including process injection, hidden processes, and suspicious network connections.
from rekall import session
from rekall import plugins
# Create a Rekall session with a memory image
s = session.Session(
filename="/path/to/memory.raw",
autodetect=["rsds"],
profile_path=["https://github.com/google/rekall-profiles/raw/master"]
)
# List processes
for proc in s.plugins.pslist():
print(proc)
# Detect injected code
for result in s.plugins.malfind():
print(result)
Key analysis steps:
- Load memory image and auto-detect profile
- Run pslist and psscan to find hidden processes
- Use malfind to detect injected/hollowed code in process VADs
- Examine network connections with netscan
- Extract suspicious DLLs and drivers with dlllist/modules
Examples
from rekall import session
s = session.Session(filename="memory.raw")
# Compare pslist vs psscan for hidden processes
pslist_pids = set(p.pid for p in s.plugins.pslist())
psscan_pids = set(p.pid for p in s.plugins.psscan())
hidden = psscan_pids - pslist_pids
print(f"Hidden PIDs: {hidden}")