mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
85 lines
2.4 KiB
Markdown
85 lines
2.4 KiB
Markdown
---
|
|
name: extracting-memory-artifacts-with-rekall
|
|
description: '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.
|
|
|
|
'
|
|
domain: cybersecurity
|
|
subdomain: security-operations
|
|
tags:
|
|
- extracting
|
|
- memory
|
|
- artifacts
|
|
- with
|
|
version: '1.0'
|
|
author: mahipal
|
|
license: Apache-2.0
|
|
nist_csf:
|
|
- DE.CM-01
|
|
- RS.MA-01
|
|
- GV.OV-01
|
|
- DE.AE-02
|
|
---
|
|
|
|
# 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.
|
|
|
|
```python
|
|
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:
|
|
1. Load memory image and auto-detect profile
|
|
2. Run pslist and psscan to find hidden processes
|
|
3. Use malfind to detect injected/hollowed code in process VADs
|
|
4. Examine network connections with netscan
|
|
5. Extract suspicious DLLs and drivers with dlllist/modules
|
|
|
|
## Examples
|
|
|
|
```python
|
|
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}")
|
|
```
|