Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
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
2026-06-10 06:53:01 +00:00

2.4 KiB

name description domain subdomain tags version author license nist_csf
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
extracting
memory
artifacts
with
1.0 mahipal Apache-2.0
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.

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

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}")