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Introduction:
Security engineers drown in fragmented knowledge – old pentest notes, incident writeups, payload lists, and debugging logs scattered across local drives, cloud docs, and chat history. The LLM Wiki pattern, popularized by Andrej Karpathy, transforms that chaos into a searchable, AI‑augmented knowledge base by combining a lightweight vector index with a large language model. This article walks through building your own “second brain” using Claude Code (or any LLM) to query technical memories instantly – no more forgetting that perfect reverse‑shell one‑liner from three years ago.
Learning Objectives:
- Build a local or cloud‑based searchable repository for pentest notes, payloads, incident timelines, and tool outputs.
- Integrate Claude Code (or any LLM) with a vector database (FAISS, Chroma) for semantic retrieval of security artifacts.
- Automate ingestion of Markdown, PDF, and code snippets into a “second brain” architecture with Linux/Windows automation tools.
You Should Know:
1. Structuring Your Security Knowledge Repository
Step‑by‑step guide: Start by organizing raw notes into a version‑controlled folder structure. Each note should be plain text (Markdown preferred) with consistent metadata – date, tool, CVE ID, or attack technique.
Linux/macOS:
mkdir -p ~/sec-brain/{pentest-notes,incidents,payloads,debug-logs,research}
cd ~/sec-brain
git init
echo ".log" >> .gitignore
Windows (PowerShell):
New-Item -Path "$env:USERPROFILE\sec-brain" -ItemType Directory cd "$env:USERPROFILE\sec-brain" New-Item -Path pentest-notes,incidents,payloads,debug-logs,research -ItemType Directory git init
Then populate with existing notes: cp ~/old-notes/.md pentest-notes/. Commit frequently: git add . && git commit -m "initial knowledge seed".
2. Converting Notes into Embeddings with FAISS
Step‑by‑step guide: Use Python and `sentence-transformers` to generate vector embeddings for every text chunk. FAISS indexes those vectors for ultra‑fast similarity search.
Install dependencies:
pip install sentence-transformers faiss-cpu langchain chromadb tiktoken
Ingestion script (save as `index_brain.py`):
import os
import pickle
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2') lightweight security‑aware model
docs = []
Walk through your sec-brain folder
for root, dirs, files in os.walk("/home/user/sec-brain"):
for file in files:
if file.endswith((".md", ".txt", ".py", ".log")):
with open(os.path.join(root, file), 'r', encoding='utf-8') as f:
text = f.read()
Chunk long files (e.g., every 500 chars)
for i in range(0, len(text), 500):
chunk = text[i:i+500]
docs.append({"text": chunk, "source": file})
Create embeddings
embeddings = model.encode([d["text"] for d in docs])
dim = embeddings.shape[bash]
index = faiss.IndexFlatL2(dim)
index.add(np.array(embeddings).astype('float32'))
Save index and metadata
faiss.write_index(index, "brain.index")
with open("brain_metadata.pkl", "wb") as f:
pickle.dump(docs, f)
Run `python index_brain.py` after each new note. For large knowledge bases, consider a scheduled job (cron or Task Scheduler).
3. Integrating Claude Code with Vector Search
Step‑by‑step guide: Claude Code can call a local API that returns the top‑k relevant notes for any query. Use FastAPI to expose your FAISS index as a retrieval endpoint.
Create a retrieval API (`brain_api.py`):
from fastapi import FastAPI
from pydantic import BaseModel
import pickle, faiss, numpy as np
from sentence_transformers import SentenceTransformer
app = FastAPI()
model = SentenceTransformer('all-MiniLM-L6-v2')
index = faiss.read_index("brain.index")
with open("brain_metadata.pkl", "rb") as f:
docs = pickle.load(f)
class Query(BaseModel):
text: str
k: int = 5
@app.post("/retrieve")
def retrieve(query: Query):
emb = model.encode([query.text])
D, I = index.search(np.array(emb).astype('float32'), query.k)
results = [docs[bash]["text"] + f"\nSource: {docs[bash]['source']}" for i in I[bash]]
return {"results": results}
Run with uvicorn brain_api:app --host 0.0.0.0 --port 8000. Then instruct Claude Code to call `http://localhost:8000/retrieve` whenever you ask a security question. Provide the prompt: “Before answering, retrieve relevant notes from my personal knowledge base.”
- Querying Your Brain from the Terminal (CLI Tool)
Step‑by‑step guide: Build a simple CLI to query without opening Claude Code – perfect for rapid payload retrieval during a live engagement.
Save as `ask_brain.py`:
import sys, requests, json
query = " ".join(sys.argv[1:])
resp = requests.post("http://localhost:8000/retrieve", json={"text": query, "k": 3})
for res in resp.json()["results"]:
print(f"\n\n{res}")
Usage:
python ask_brain.py "reverse shell for port 443 using python3"
Output will show your own previously saved one‑liners. On Windows, create a batch file `ask_brain.bat` calling the script.
5. Automating Ingestion of New Pentest Writeups
Step‑by‑step guide: Use a watcher script to automatically index any new file added to your `sec-brain` folder.
Linux (inotify + cron alternative):
Install `inotify-tools`, then run:
inotifywait -m ~/sec-brain -e create -e modify | while read path action file; do python /path/to/index_brain.py done
Windows (PowerShell FileSystemWatcher):
$watcher = New-Object System.IO.FileSystemWatcher
$watcher.Path = "$env:USERPROFILE\sec-brain"
$watcher.EnableRaisingEvents = $true
Register-ObjectEvent $watcher "Created" -Action { python C:\scripts\index_brain.py }
This ensures your vector index never goes stale.
6. Hardening and Access Control for Sensitive Notes
Step‑by‑step guide: Many pentest notes contain credentials or internal IPs. Encrypt the index and metadata, and restrict API access to localhost only.
Encrypt the FAISS index with GPG (Linux/macOS):
gpg --symmetric --cipher-algo AES256 brain.index rm brain.index keep only the .gpg file
In `brain_api.py`, decrypt on the fly:
import gnupg
gpg = gnupg.GPG()
with open("brain.index.gpg", "rb") as f:
decrypted = gpg.decrypt_file(f, passphrase="your-strong-passphrase")
load decrypted data into FAISS
Windows alternative: Use `7z` with a password or EFS encryption. Bind the API to `127.0.0.1` only: --host 127.0.0.1.
- Using LLM Wiki Pattern with Cloud AI (Azure OpenAI / AWS Bedrock)
Step‑by‑step guide: If you prefer a managed service, replace the local embedding model with a cloud endpoint. This reduces local compute but requires careful API hardening.
Example using Azure OpenAI embeddings:
import openai openai.api_base = "https://your-resource.openai.azure.com/" openai.api_key = "your-key" response = openai.Embedding.create(input=["your query"], engine="text-embedding-ada-002") embedding = response['data'][bash]['embedding']
Then use a cloud vector database like Pinecone or pgvector on a PostgreSQL instance. Always enable role‑based access control and audit logging for compliance.
What Undercode Say:
Key Takeaway 1 – A “second brain” is not a luxury but a force multiplier for security engineers.
The ability to recall a specific payload, a past incident’s root cause, or an obscure tool flag within seconds drastically cuts investigation time. This pattern turns tacit knowledge (what you once knew) into explicit, queryable knowledge.
Key Takeaway 2 – The LLM Wiki pattern democratizes advanced RAG (retrieval‑augmented generation).
You don’t need a full enterprise vector database. With FAISS + a simple API, any engineer can build a private, offline‑capable knowledge base that works alongside Claude Code or any LLM. The hardest part is consistent note‑taking – the technology is now trivial.
Analysis (10 lines):
This approach directly addresses a pain point every penetration tester and SOC analyst faces: information silos. By using Karpathy’s pattern (originally designed for personal AI assistants), the author repurposes it for operational security – genius. The key innovation is the “query layer” over raw notes, which eliminates the need to remember folder structures or filenames. For teams, this pattern can be extended to shared knowledge bases (with proper ACLs). However, two risks exist: (1) feeding sensitive client data into an LLM that might leak (solution: keep the LLM local or use a private instance like Llama 3), and (2) index poisoning – an attacker modifying notes to influence retrieval. Regularly hash‑verifying ingested files mitigates this. Overall, this is a practical, low‑cost win for security engineering productivity.
Expected Output:
Example query to the second brain:
python ask_brain.py "how to bypass LFI with PHP wrappers"
Returned results:
Source: pentest-notes/lfi_cheatsheet.md php://filter/convert.base64-encode/resource=index.php php://input with POST data → execute code if allow_url_include=On Source: payloads/lfi_wrappers.txt expect://id zip://file.zip%23cmd.txt
This demonstrates instant retrieval of your own curated techniques, not generic internet answers.
Prediction:
Within 18 months, most security teams will adopt lightweight “second brain” repositories as standard incident‑response tooling. As LLM context windows grow and local models (e.g., Llama 4, Mistral Next) become more capable, the line between knowledge base and real‑time assistant will blur. Expect open‑source projects that automate note ingestion from Burp Suite, BloodHound, and Splunk logs directly into vector indexes. The biggest future risk: reliance on these systems without proper validation – hallucinations may merge outdated or incorrect notes into otherwise sound reasoning. Engineers will need to implement confidence scoring and source citation natively. Meanwhile, attackers will target knowledge‑base APIs as a high‑value data exfiltration vector, shifting security focus from endpoint to memory (the AI’s memory). The arms race has already begun.
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IT/Security Reporter URL:
Reported By: Mf Akbar – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
