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Introduction:
The convergence of open-source intelligence (OSINT) and locally-run large language models (LLMs) is redefining investigative capabilities. By amassing thousands of GitHub repositories—each containing unique scripts, techniques, and data sources—and then querying them with a powerful 70B-parameter model like Hermes 4, an engineer can generate bespoke solutions in hours instead of weeks. This article dissects the methodology behind creating a massive OSINT code library and harnessing local LLMs for rapid, private, and tailored analysis, providing you with actionable commands and workflows to replicate this force-multiplying setup.
Learning Objectives:
- Build and maintain a curated collection of thousands of OSINT-related GitHub repositories for instant local access.
- Deploy and interact with a local Hermes 4 (70B) LLM for natural-language queries against codebases.
- Automate the extraction, synthesis, and execution of investigative scripts using Python, shell commands, and LLM APIs.
You Should Know:
1. Building Your Own OSINT Repository Collection
Start by cloning repositories that specialize in data scraping, geolocation, social media analysis, and breach aggregation. Use targeted GitHub searches and then script the cloning process to avoid manual work. Below is a Linux/bash workflow to initialize a massive OSINT asset library.
Step‑by‑step guide:
- Create a directory and navigate into it: `mkdir ~/osint-arsenal && cd ~/osint-arsenal`
– Use `gh` (GitHub CLI) to search for repos with high relevance: `gh search repos –topic=”osint” –language=”Python” –limit=1000 –json sshUrl –jq ‘.[].sshUrl’ > repos.txt`
– For Windows (PowerShell), install `gh` and run: `gh search repos –topic=”osint” –limit=1000 –json cloneUrl | ConvertFrom-Json | ForEach-Object { git clone $_.cloneUrl }`
– Clone all repos in parallel using `xargs` (Linux): `cat repos.txt | xargs -P 4 -I {} git clone {}`
– Deduplicate similar tooling with `rdfind` (Linux) or `fdupes` to save space. - Update weekly with a cron job: `0 3 0 cd ~/osint-arsenal && for d in /; do (cd “$d” && git pull); done`
2. Running Local LLMs for Code Analysis
Hermes 4 (70B) requires substantial RAM/VRAM, but you can run it quantized (e.g., Q4_K_M) on a dual-GPU setup (2×24GB). Use Ollama for easy management. For machines with less memory, consider 8B or 13B variants.
Step‑by‑step guide (Linux/WSL2):
- Install Ollama: `curl -fsSL https://ollama.com/install.sh | sh`
– Pull a quantized Hermes 4 model: `ollama pull nousresearch/hermes-4-70b-q4_K_M` (or use `hermes-4-70b` if available) - Run an interactive session: `ollama run hermes-4-70b`
– For API access (Python): `import requests; response = requests.post(‘http://localhost:11434/api/generate’, json={‘model’: ‘hermes-4-70b’, ‘prompt’: ‘Explain how to parse Twitter profile JSON’, ‘stream’: False})`
– Windows: Install Ollama for Windows, then use `ollama run hermes-4-70b` in PowerShell or CMD. Add `–1vidia` if you have a CUDA GPU.
- Creating a Query Engine to Search Thousands of Repos
Combine `ripgrep` (ultra-fast regex search) with an LLM summarizer. This lets you ask natural-language questions like “find all scripts that download satellite imagery” and get curated answers.
Step‑by‑step guide:
- Install ripgrep: `sudo apt install ripgrep` (Linux) or `choco install ripgrep` (Windows via Chocolatey)
- Search across all repos for a keyword and pipe results to LLM: `rg “google earth” ~/osint-arsenal –type py -l | xargs cat | ollama run hermes-4-70b “Summarize the geolocation extraction methods in these files”`
– For advanced use, write a Python script:import subprocess, requests result = subprocess.run(["rg", "instagram", "~/osint-arsenal", "-l"], capture_output=True, text=True) files = result.stdout.splitlines() content = "" for f in files[:5]: limit context with open(f, 'r', errors='ignore') as file: content += file.read()[:2000] payload = {"model": "hermes-4-70b", "prompt": f"Extract API endpoints from:\n{content}", "stream": False} response = requests.post("http://localhost:11434/api/generate", json=payload) print(response.json()["response"])
4. Automating Solution Generation from Existing Code
One of the post’s key claims is speed in creating solutions. Use the LLM to combine snippets from different repos into a working script. For example, blend a phone number validator from one repo with a carrier lookup from another.
Step‑by‑step guide:
- Extract relevant functions via ripgrep: `rg “def.phone” ~/osint-arsenal -A 10 -B 2 > phone_funcs.txt`
– Feed those snippets to the LLM with a prompt: `cat phone_funcs.txt | ollama run hermes-4-70b “Write a Python script that takes a phone number and returns carrier and location. Combine only the best methods shown.”`
– Save the output as `carrier_lookup.py` and test. For API security, if any snippet uses keys, instruct the LLM to replace them with environment variables. - Automate with `inotifywait` (Linux) to watch a folder; any new `.req` file triggers LLM generation.
5. Hardening Your Local AI Environment
Running an LLM with access to potentially malicious code (from unvetted GitHub repos) demands isolation. Also, if you expose the Ollama API remotely, harden it.
Step‑by‑step guide:
- Run Ollama inside a Docker container with limited privileges:
docker run -d --gpus all -v ollama:/root/.ollama -p 11434:11434 --1ame ollama-sandbox ollama/ollama
- For Windows, use Docker Desktop with WSL2 backend. Then inside container: `ollama run hermes-4-70b`
– Restrict API access to localhost only: Ensure `OLLAMA_HOST=127.0.0.1` (default). Use UFW on Linux: `sudo ufw deny 11434/tcp` then allow only specific IP if needed. - Scan every new repo for secrets or malware using `trufflehog` (secrets) and `clamscan` (signatures): `trufflehog filesystem –directory=~/osint-arsenal/new-repo`
- Mitigating Risks of Code Injection and Malicious Repos
Adversaries can plant backdoors in OSINT tools. Always audit before running LLM-synthesized code.
Step‑by‑step guide:
- Use `semgrep` for static analysis: `semgrep –config auto ~/osint-arsenal –output vulns.json`
– For Linux, set up a restricted user for testing: `sudo useradd -m -s /bin/bash osint-test` and execute all generated scripts as that user inside a firejail: `firejail –1et=none sudo -u osint-test python3 script.py`
– Windows equivalent: Run in Windows Sandbox or a Hyper-V VM with no network access. - Create a pre-commit hook to block dangerous patterns: `grep -r “eval(” ~/osint-arsenal && echo “Dangerous eval found” && exit 1`
7. Scaling with 70B vs. 405B Models
The post notes “405B isn’t enough on my machine.” 405B models (e.g., Llama 3.1 405B) require ~800GB of VRAM in full precision. However, you can offload to CPU+RAM with extreme quantization, but performance plummets.
Step‑by‑step guide:
- For 70B, use 4-bit quantization to fit on 2×24GB GPUs (e.g., RTX 4090) or 1×80GB A100. Install `llama.cpp` and convert the model:
git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make python3 convert.py ~/hermes-4-70b --outfile hermes-70b-q4.gguf --outtype q4_K_M ./main -m hermes-70b-q4.gguf -1 256 --gpu-layers 80
- For a single consumer GPU (24GB), use Q2 or offload half the layers to CPU: `–gpu-layers 40 –threads 16`
– Monitor memory: `nvidia-smi` (Linux) or Task Manager (Windows). If you see OOM, reduce context size (-c 2048).
What Undercode Say:
- Key Takeaway 1: A locally maintained collection of thousands of GitHub repos is a strategic asset that outperforms any online search when paired with an LLM that can read and synthesize code locally without data leakage.
- Key Takeaway 2: The real force multiplier is not just the model size (70B vs 405B) but the workflow—automated cloning, ripgrep indexing, and query templating—that turns raw code into instantly actionable investigations.
- Analysis: The engineer’s claim of “astounding speed” comes from eliminating the manual step of reading repos individually. Instead, the LLM acts as a personal librarian, but this requires rigorous sandboxing because malicious code can poison both the repos and the LLM’s output. Future iterations will likely include vector databases (e.g., ChromaDB) over the codebase for semantic search. Additionally, the limitation of running 405B locally suggests that edge AI will rely on mixture-of-experts or on-demand cloud fallbacks. For defenders, this technique means adversaries can now automate exploit development from public code; for blue teams, it offers instant incident response playbooks from past CTF solutions. The biggest risk is complacency—LLMs hallucinate function calls, so every generated script must be tested in isolation.
Prediction:
+1 Local LLMs for OSINT will democratize advanced investigations, enabling small teams to compete with intelligence agencies, as the cost of hardware (e.g., second-hand A100s) continues to drop.
-1 The ease of generating malicious tools from public repos will lower the barrier for cybercriminals, leading to a surge in automated, LLM-driven spear-phishing and zero-day repurposing by mid-2027.
+N Cloud providers will offer “OSINT-in-a-box” instances with pre-cloned repo datasets and fine-tuned Hermes-class models, making this workflow accessible to non-engineers.
-1 Legal gray areas around cloning thousands of GitHub repos (violating rate limits or terms of service) may result in account bans or litigation, pushing the activity underground.
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