Download Syd

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✅ v3.0.0 Stable. Production Ready

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↓ Download v3.0.0

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System Requirements

Community Edition (Free)

  • OS: Windows 10/11, Linux (Ubuntu 20.04+), macOS 11+
  • CPU: 4 cores minimum
  • RAM: 16 GB minimum
  • Storage: 15 GB free
  • Python: 3.10+

Pro / Enterprise Binary

  • OS: Windows 10/11 (primary)
  • CPU: 4+ cores
  • RAM: 16 GB minimum, 24 GB recommended
  • Storage: 20 GB free
  • Python: Not required. Bundled EXE

Quick Start (Community Edition)

Step 1: Clone the repository
git clone https://github.com/Sydsec/syd.git
cd syd
Step 2: Install Python dependencies
pip install -r requirements.txt
Step 3: Download the AI model

The model is downloaded automatically on first run. On an air-gapped machine, copy the hf_home/ folder from a connected machine.

Step 4: Launch Syd
python syd.py
First launch: Initial startup takes ~30 seconds to load the embedding model. Subsequent launches are faster.
Antivirus false positives: Syd is a security research tool and may trigger AV alerts. Add the Syd folder to your exclusions. This is normal for tools containing security-related signatures.

Air-Gapped Deployment

Syd is designed for 100% offline operation. Pro and Enterprise editions ship as self-contained Windows EXEs. No internet required after first download.

Community Edition: Manual air-gap transfer:
  1. On a connected machine: clone the repo and run pip download -r requirements.txt -d ./packages
  2. Copy the full Syd folder (code + packages + hf_home model folder) to USB
  3. On the air-gapped machine: pip install --no-index --find-links=./packages -r requirements.txt
  4. Run python syd.py
Pro / Enterprise: Simplified transfer:
  1. Download the signed EXE and verify the SHA256 hash
  2. Copy to USB and transfer to your secure network
  3. Run the EXE. No installation needed
Network requirements (after deployment)
  • Internet: Not required
  • Outbound calls: None
  • Telemetry: None
  • Updates: Via offline update packs (Pro/Enterprise)

Technical Specifications

AI Stack
  • LLM: Qwen 2.5 14B GGUF (Q5_K_M)
  • Inference: llama-cpp-python (CPU)
  • Embedding: all-MiniLM-L6-v2 (384-dim)
  • Vector DB: FAISS (IndexFlatIP, cosine similarity)
  • GUI: tkinter (cross-platform)
Accuracy (Production Tested)
  • Nmap: 96.7%
  • BloodHound: 10/10
  • YARA: 9.84/10
  • NetExec: 9.2/10
  • Volatility: 8.13/10
  • Average: 9.27/10

Pro & Enterprise: Pre-Built Binaries

Skip the Python setup entirely. Pro and Enterprise ship as signed Windows EXEs. Download, verify the SHA256 hash, and run. All dependencies bundled.

  • Pro (£250/yr): 6 tools: Nmap, BloodHound, Volatility, YARA, NetExec, PCAP + 5-day support
  • Enterprise (£1,000/yr): Pro + Metasploit integration + 24hr priority support + report builder

Email: info@sydsec.co.uk

Next Steps

Watch Demo Videos

See Syd analysing Nmap, BloodHound, Volatility, YARA and NetExec output

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YouTube Channel

Subscribe for tutorials, demos and security research content

YouTube
GitHub Docs

Installation guide, quickstart and usage documentation

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Need Support?

Community support via GitHub or professional support via email

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