The single board computer (SBC) market is constantly evolving, driven by demand for compact, low-power devices capable of handling increasingly complex workloads. The Radxa Dragon Q6A represents a significant step forward, particularly in the realm of edge AI. Powered by Qualcomm’s QCS6490 processor (based on the Dragonwing architecture), this SBC promises to deliver impressive performance at a competitive price point. This blog post will provide an in-depth look at the Radxa Dragon Q6A, focusing on its key features, AI capabilities, the value proposition, and practical considerations for deployment. We’ll also address the downsides outlined by early adopters, offering a balanced perspective for those considering this platform.
This heterogeneous configuration gives you a total of eight CPU cores, allowing the board to handle mixed‑type workloads – from heavy inference tasks on the NPU to background Linux services.
GPU and Video Processing
The integrated Adreno 643 GPU supports OpenGL ES 3.2/2.0/1.1, Vulkan 1.1–1.3, OpenCL 2.2 and DirectX Feature Level 12. For video‑centric AI pipelines (e.g., object detection on live streams) the Adreno Video Processing Unit 633 can decode up to 4K 60 fps H.264/H.265/VP9 and encode up to 4K 30 fps, making it suitable for surveillance or multimedia edge devices.
eMMC/UFS storage: up to 512 GB UFS module or 64 GB eMMC
Connectivity and I/O
Interface
Details
Wi‑Fi / Bluetooth
IEEE 802.11a/b/g/n/ac/ax (Wi‑Fi 6) + BT 5.4, two external antenna connectors (note: driver support currently missing in the Windows preview)
Ethernet
1 × Gigabit RJ45 with optional PoE (requires separate PoE HAT)
USB
1 × USB 3.1 OTG Type‑A, 3 × USB 2.0 Host Type‑A
HDMI
HDMI 2.0 Type‑A, up to 3840 × 2160 (4K 30 fps)
M.2
Key‑M slot supporting PCIe Gen3 x2 for 2230 NVMe SSDs
Camera
1 × four‑lane CSI + 2 × two‑lane CSI, plus a four‑lane MIPI DSI display connector
GPIO
40‑pin header with UART, I²C, SPI, PWM, 5 V and 3.3 V power rails
AI Capabilities: A Surprisingly Strong Contender
The most compelling aspect of the Radxa Dragon Q6A is its potential for edge AI applications. Qualcomm’s software ecosystem – QAIRT SDK, QAI-APP-BUILDER and QAI-HUB model library – provides a robust foundation for developing and deploying AI models. Out of the box, these tools support major CV (Computer Vision), LVM (Language & Voice Models) and VLM (Vision Language Models).
Qualcomm’s AI acceleration is split between the Hexagon Vector Extensions (HVX) DSP and a dedicated Tensor Accelerator. The DSP handles low‑precision operations efficiently, while the Tensor Accelerator provides high throughput for matrix multiplication in modern LLMs and vision transformers. Together they form the backbone of the board’s AI performance.
Early testing indicates impressive performance even on the modest 4/6/8GB version. Reports show ~100 tokens/second in prompt processing and over 10 tokens/second in generation with a 4096 context length using Llama3.2-1b. These figures that are highly competitive for an SBC in this price range. This suggests the Dragon Q6A can handle real-time AI inference tasks, opening up possibilities for applications like:
Smart Home Applications: Voice control, personalized automation
The integrated Hexagon Tensor Accelerator is key to this performance. It’s designed specifically for accelerating machine learning workloads, enabling efficient execution of complex models without relying heavily on the CPU or GPU. This translates to lower power consumption and improved responsiveness – critical factors for edge deployments.
Software Support & Development Ecosystem
Radxa supports a variety of operating systems including RadxaOS, Ubuntu Linux, Deepin Linux, Armbian, ArchLinux, Qualcomm Linux (Yocto-based), and Windows on Arm. The availability of hardware access libraries for both Linux and Android platforms simplifies development and integration. However, it’s important to note that the software is still under active development and hasn’t reached a stable release state yet. This means users may encounter bugs or require recompiling the kernel and working with packages – potentially challenging for those unfamiliar with Linux subsystems.
Downsides & Practical Considerations
Despite its impressive capabilities, the Radxa Dragon Q6A isn’t without its drawbacks:
Limited Availability: Currently, shipments are primarily out of China, which can lead to difficulties and additional expenses for North American customers due to current trade conditions.
Thermal Management: The SBC runs hot when executing models, requiring a cooling solution. Radxa doesn’t offer official passive or active cooling systems, necessitating modification of existing solutions designed for other boards. This adds complexity and cost.
Software Maturity: As mentioned earlier, the software ecosystem is still evolving. Users should be prepared to debug issues, potentially recompile kernels, and work with Linux packages.
Comparison to Competing Edge AI (8GB) SBCs
Device
NPU / Accelerator
Approx. Price (USD)
Token Generation Speed*
Prompt Processing Speed
Radxa Dragon Q6A
Qualcomm Hexagon (QCS6490) + Tensor Accelerator
$85-100
9.7 tokens/s
110.3 tokens/s
Orange Pi 5
Rockchip 3588 NPU (Mali‑G610)
$150‑$180
5.8 tokens/s
14.8 tokens/s
Nvidia Jetson Orin Nano
CUDA GPU Core based on NVIDIA Ampere architecture
$249
38.6 tokens/s
8.8 tokens/s
Raspberry Pi 5 CPU + GPU
Broadcom BCM2712 + VideoCore VII
$85-100
6.5 tokens/s
4.3 tokens/.s
*Measured under similar quantization settings and batch size of 1. context of 4096, and using Llama 3.2-1B.
The Dragon Q6A’s advantage lies in its dedicated Tensor Accelerator that can sustain higher throughput for larger context windows, making it a compelling choice for on‑device LLM inference or multimodal tasks where latency matters.
Inference Pipeline LLM Prompt Execution Flow
The following Mermaid diagram visualizes the data flow from user input to NPU inference and back to the application:
The diagram highlights that the CPU handles tokenization and detokenization while the heavy matrix operations run on the NPU, keeping latency low and freeing CPU cycles for other tasks such as network handling or monitoring.
Conclusion
The Radxa Dragon Q6A represents an exciting development in the SBC landscape, offering a compelling combination of performance, AI capabilities, and affordability. Its Qualcomm Dragonwing processor and dedicated Hexagon Tensor Accelerator make it well-suited for edge AI applications. However, potential buyers should be aware of the downsides – limited availability, thermal management challenges, and software maturity issues. By carefully addressing these considerations, developers can unlock the full potential of this powerful SBC.
Next I look forward to putting the Dragonwing (including the Airbox Q900 once they continue shipping to the US) line to the test in a Hiwonder robotics application, where I believe it will outshine a traditional Raspberry Pi; at the same price point.
The dream was simple enough: an AI-powered Halloween skeleton, affectionately dubbed “Skelly,” greeting trick-or-treaters with personalized welcomes based on their costumes. The reality, as often happens in the world of rapid prototyping and ambitious side projects, proved… more complicated. This post details the lessons learned from a somewhat chaotic Halloween night deployment, focusing on the implications inherent in edge AI systems like Skelly, and outlining strategies for a more controlled – and successful – iteration next year. We’ll dive into the design choices, the unexpected challenges, and how leveraging local Vision Language Models (VLMs) can be a powerful tool for privacy-focused applications.
The Initial Vision: A Local AI Halloween Greeter
The core concept revolved around using a Radxa Zero 3W, a connected USB webcam, built-in speaker controlled by a MAX98357A mono amplifier, and the animatronics of a pre-built Halloween skeleton. The plan was to capture images, feed them into an offline VLM like those available through LM Studio (powered by AMD Strix Halo platform), analyze the costumes (with Google gemma-3-27B), and generate a custom greeting delivered via text-to-speech (TTS) using PiperTTS. The original inspiration came from Alex Volkov’s work on Weights & Biases, utilizing a similar setup with Google AI Studio, ElevenLabs, Cartesia, and ChatGPT.
I opted for a fully offline approach to prioritize privacy. Capturing images that include children requires careful consideration, and sending that data to external APIs introduces significant risks. Local processing eliminates those concerns, albeit at the cost of increased complexity in model management and resource requirements.
The Halloween Night Reality: Overwhelmed by the Que
The biggest issue wasn’t technical – it was human. We anticipated a trickle of small groups, perhaps one to three treaters approaching Skelly at a time, uttering a polite “trick or treat.” Instead, we were met with waves of ten-plus children lining up like attendees at a concert. The system simply couldn’t handle the rapid influx.
The manual trigger approach – snapping pictures on demand – quickly became unsustainable. We struggled to process images fast enough before the next wave arrived. Privacy concerns also escalated as we attempted manual intervention, leading us to abandon the effort and join our kids in traditional trick-or-treating. The lack of good reproducible artifacts was a direct consequence of these issues; we were too busy firefighting to collect meaningful data.
Security Considerations: A Deep Dive into Edge AI Risks
This experience highlighted several critical risk considerations for edge AI deployments, particularly those involving physical interaction and potentially sensitive data like images of children:
Data Capture & Storage: Even with offline processing, the captured images represent a potential privacy breach if compromised. Secure storage is paramount – encryption at rest and in transit (even locally) is essential. Consider minimizing image retention time or implementing automated deletion policies.
Model Integrity: The VLM itself could be targeted. A malicious actor gaining access to the system could potentially replace the model with one that generates inappropriate responses or exfiltrates data. Model signing and verification are crucial.
GPIO Control & Physical Access: The Radxa Zero 3W’s GPIO pins, controlling the animatronics, represent a physical attack vector. Unrestricted access to these pins or the network could allow an attacker to manipulate Skelly in unintended ways,
Network Exposure (Even Offline): While we aimed for complete offline operation, the system still had network connectivity for initial model downloads and updates. This creates a potential entry point for attackers.
Reimagining Skelly: Controlling the Chaos
Next year’s iteration will focus on mitigating these risks through a combination of controlled interactions, robust security measures, and optimized processing. Here’s the plan:
1. Photo Booth Mode: Abandoning the “ambush” approach in favor of a dedicated photo booth setup. A backdrop and clear visual cues will encourage people to interact with Skelly in a more predictable manner.
2. Motion-Triggered Capture: Replacing voice activation with a motion sensor. This provides a consistent trigger mechanism, allowing us to time image capture and processing effectively.
3. Timing & Rate Limiting: Implementing strict timing controls to prevent overwhelming the system. A delay between captures will allow sufficient time for processing and response generation.
4. Visual Indicators & Auditory Cues: Providing clear feedback to users – a flashing light indicating image capture, a cheerful phrase confirming costume recognition, and a countdown timer before the greeting is delivered. This enhances user experience and encourages cooperation.
5. Enhanced GPIO Controls: Restricting access to the GPIO pins using Linux capabilities or mount namespaces. As well as limiting physical access to Skelly is key to reduce tampering.
Leveraging Local VLMs: A Python Example
The power of local VLMs lies in their ability to understand images without relying on external APIs. Here’s a simplified example demonstrating how to capture an image from a USB webcam and prompt Ollama with a costume greeting request using Python:
import cv2
import requests
import json
# Configuration
OLLAMA_API_URL = "http://localhost:11434/api/generate" # Adjust if necessary
MODEL = "gemma-3-27B" # Or your preferred VLM model
PROMPT_TEMPLATE = "You are an AI assistant controlling a Halloween animatronic. The following is a base64‑encoded JPEG image of a person(s) in a costume.
Identify the costume in one short phrase and then respond with a friendly greeting that references the costume. Use a cheerful tone."
def capture_image(camera_index=0):
"""Captures an image from the specified webcam."""
cap = cv2.VideoCapture(camera_index)
if not cap.isOpened():
raise IOError("Cannot open webcam")
ret, frame = cap.read()
if not ret:
raise IOError("Failed to capture image")
_, img_encoded = cv2.imencode('.jpg', frame)
cap.release()
return img_encoded.tobytes()
def prompt_ollama(image_data):
"""Prompts Ollama with the image data and returns the response."""
headers = {
"Content-Type": "application/json"
}
payload = {
"model": MODEL,
"prompt": PROMPT_TEMPLATE,
"stream": False # Set to True for streaming responses
}
# Encode the image as base64 (Ollama requires this)
import base64
image_base64 = base64.b64encode(image_data).decode('utf-8')
payload["prompt"] += f"\n[Image: {image_base64}]"
response = requests.post(OLLAMA_API_URL, headers=headers, data=json.dumps(payload))
response.raise_for_status() # Raise an exception for bad status codes
return response.json()['response']
if __name__ == "__main__":
try:
image_data = capture_image()
greeting = prompt_ollama(image_data)
print("Generated Greeting:", greeting)
except Exception as e:
print("Error:", e)
Important Notes:
This is a simplified example and requires the cv2 (OpenCV) and requests libraries. Install them using pip install opencv-python requests.
Ensure Ollama is running and the specified model (gemma-3-27B) is downloaded.
The image data is encoded as base64 for compatibility with Ollama’s API. Adjust this if your VLM requires a different format.
Error handling is minimal; implement more robust error checking in a production environment.
System Flow Diagram: Whisper to Piper via Ollama
Here’s a flow diagram illustrating the complete system architecture:
This diagram highlights the key components and data flow: a motion sensor triggers image capture, which is then processed by Ollama to generate a costume description and greeting. Piper TTS converts the text into audio, delivered through Skelly’s speaker. Whisper processing detects the “trick or treat” wake word, initiating the process.
Conclusion: Building Secure & Engaging Edge AI Experiences
The Halloween night debacle served as a valuable learning experience. While the initial vision was ambitious, it lacked the necessary controls and security measures for a real-world deployment. By focusing on controlled interaction, robust security practices, and leveraging the power of local VLMs like those available through Ollama or LM Studio, we can create engaging and privacy-focused edge AI experiences that are both fun and secure. The key is to anticipate potential challenges, prioritize user safety, and build a system that’s resilient against both accidental mishaps and malicious attacks. The future of animatronics powered by local VLM is bright – let’s make sure it’s also safe!
In today’s distributed world the perimeter of an organization is no longer a single firewall at the edge of a data center. Users, services and devices connect from any location, over public clouds, through orchestrated container networks and via remote VPNs. This shift has driven security teams to adopt Zero Trust, a model that validates not only who you are but also where your device is located and what network it uses before granting access to critical resources.
One of the most effective ways to add an additional layer of verification is geofencing – the practice of allowing or denying traffic based on its geographic or network attributes such as country, city, or Autonomous System Number (ASN). When combined with strong device authentication (for example Device Trust certificates) geofencing can dramatically reduce the attack surface of your Device Trust access gateways.
This post explains how to harden access gateways with geofencing using Nginx and the ngx_http_geoip2_module. We will walk through obtaining free GeoIP data from MaxMind, configuring Nginx as a reverse proxy that blocks traffic by ASN, integrating geofence policies into modern identity providers like Authentik, and visualizing a example secure access flow. The examples are designed for Linux environments but can be adapted to any container or cloud platform.
Why Device Trust Matters in Modern Cloud Environments
Devices now connect from home offices, coffee shops, mobile networks and public clouds.
Attackers often use compromised devices or rented cloud instances that appear legitimate.
Traditional username/password/MFA checks do not verify the legitimacy of the device itself.
Adding a location check/monitoring makes it much harder for an adversary to reuse stolen credentials from an unexpected region.
When you combine device certificates, modern identity federation, and geofencing, you create a zero trust style gateway that only accepts traffic that meets all three criteria:
Valid client certificate issued by your private Device Trust CA.
Successful authentication with your Identity Provider (IdP).
Source IP (or X-Forward-For) belongs to an expected country, city or ASN.
If any of these checks fail, the request is dropped before it reaches downstream services.
The Role of Geofencing in Hardening Access Gateways
Geofencing works by mapping an incoming IP address to a set of attributes – usually:
Country code (ISO‑3166 two‑letter format).
City name, coordinates and accuracy radius.
Autonomous System Number (ASN) which identifies the ISP or network owner.
These mappings are provided by public databases such as MaxMind’s GeoLite2. Because the data is freely available, you can implement geofencing without paying for a commercial service. The key steps are:
Download and regularly update the GeoIP database.
Load the database into your reverse proxy (Nginx in this example).
Define rules that allow or deny traffic based on the mapped attributes.
(Optional) Combine those rules with device certificate validation and IdP User attributes .
Getting Started with GeoIP Data Sources
MaxMind offers three primary free databases:
GeoLite2‑Country – maps IP to country code.
GeoLite2‑ASN – maps IP to ASN number and organization name.
GeoLite2-City– maps IP to City name as well as latitude, longitude, and accuracy radius.
Note: There are other free and paid providers of MaxMind (.mmdb) geolocation databases, which also should integrate into the same tooling without issue. Some great options are ipinfo lite, iplocate free, and ip2location lite.
You can obtain them by creating a free MaxMind account, accepting the license, and downloading the .mmdb files. To keep the data fresh you should schedule regular updates (MaxMind releases new versions weekly). The open source tool geoipupdate automates this process:
# Install geoipupdate on Debian/Ubuntu
apt-get update
apt-get install -y geoipupdate
# Create /etc/GeoIP.conf with your account details
cat <<EOF | sudo tee /etc/GeoIP.conf
AccountID YOUR_ACCOUNT_ID
LicenseKey YOUR_LICENSE_KEY
EditionIDs GeoLite2-Country GeoLite2-City GeoLite2-ASN
EOF
# Run the update immediately and enable a daily cron job
sudo geoipupdate
The resulting files are typically stored in /var/lib/GeoIP/ as GeoLite2-Country.mmdb and GeoLite2-ASN.mmdb. Adjust the paths in your Nginx configuration accordingly.
Installing and Configuring ngx_http_geoip2_module
The ngx_http_geoip2_module is a third‑party module that provides fast lookups of GeoIP data inside Nginx. It works with both the open source and commercial versions of Nginx, but for most Linux distributions you will need to compile it as a dynamic module.
#Install Build Prerequisites
apt-get install -y build-essential libpcre3-dev zlib1g-dev libssl-dev libmaxminddb-dev nginx wget git vim
#Download Nginx Source and the GeoIP2 Module
NGINX_VERSION=$(nginx -v 2>&1 | cut -d'/' -f2| cut -d' ' -f1)
wget http://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz
tar xzf nginx-${NGINX_VERSION}.tar.gz
git clone https://github.com/leev/ngx_http_geoip2_module.git
#Compile the Module as a Dynamic Loadable Object
cd nginx-${NGINX_VERSION}
./configure --with-compat --add-dynamic-module=../ngx_http_geoip2_module
make modules
cp objs/ngx_http_geoip2_module.so /usr/share/nginx/modules/
echo "load_module modules/ngx_http_geoip2_module.so;" > /etc/nginx/modules-available/mod-http-geoip2.conf
ln -s /etc/nginx/modules-available/mod-http-geoip2.conf /etc/nginx/modules-enabled/60-mod-http-geoip2.conf
#Enable and configure the Module by adding the following to the http section
vim /etc/nginx/nginx.conf
'''
geoip2 /var/lib/GeoIP/GeoLite2-Country.mmdb {
auto_reload 60m;
$geoip2_metadata_country_build metadata build_epoch;
$geoip2_country_code country iso_code;
$geoip2_country_name country names en;
}
geoip2 /var/lib/GeoIP/GeoLite2-City.mmdb {
auto_reload 60m;
$geoip2_metadata_city_build metadata build_epoch;
$geoip2_city_name city names en;
}
fastcgi_param COUNTRY_CODE $geoip2_country_code;
fastcgi_param COUNTRY_NAME $geoip2_country_name;
fastcgi_param CITY_NAME $geoip2_city_name;
'''
Now you can use the GeoIP2 country or city variables or create custom directives inside your server blocks.
Blocking Traffic by Country with Nginx GeoIP2
A simple config that only allows traffic from the United States and Canada might look like this:
http {
map $geoip2_country_code $allowed_country {
default no;
US yes;
CA yes;
}
server {
listen 443 ssl;
server_name gateway.example.com;
# TLS configuration omitted for brevity
if ($allowed_country = no) {
return 403;
}
location / {
proxy_pass http://backend;
}
}
}
This configuration uses the $geoip2_country_code variable loaded from the geoip2 via the main nginx config. The map block creates a boolean variable $allowed_country that is later used in an if statement to reject disallowed traffic with HTTP 403.
ASN Based Geofence on an Nginx Reverse Proxy
Blocking by ASN provides finer granularity than country alone, especially when you want to restrict access to corporate ISP ranges or known cloud providers. Below is a more advanced configuration that:
Allows only devices originating from your corporate ASN (e.g., AS12345) or a trusted cloud provider (AS67890).
Requires a valid client certificate signed by your internal CA.
Sends the authenticated request to an internal API gateway.
http {
# Load both country and ASN databases
geoip2 /var/lib/GeoIP/GeoLite2-ASN.mmdb {
auto_reload 5m;
$geoip2_asn_number asn asn;
$geoip2_asn_org asn organization;
}
# Define the list of permitted ASNs
map $geoip2_asn_number $asn_allowed {
default no;
12345 yes; # Corporate ISP
67890 yes; # Trusted Cloud Provider
}
server {
listen 443 ssl;
server_name api-gateway.example.com;
# TLS configuration (certificate, key) omitted for brevity
# Enforce mutual TLS – reject if no cert or invalid cert
ssl_verify_client on;
ssl_client_certificate /etc/nginx/certs/ca.crt; # Your CA Chain
# If client certificate verification fails, Nginx returns 400 automatically.
# Add an explicit check for ASN after TLS handshake:
if ($asn_allowed = no) {
return 403;
}
location / {
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Client-Cert $ssl_client_cert;
proxy_set_header X-Client-DN $ssl_client_s_dn
proxy_pass http://internal-app;
}
}
}
Explanation of key parts
$geoip2_asn_number is populated by the GeoIP2 lookup; the map block translates the ASN into a simple yes/no flag.
The if ($asn_allowed = no) clause blocks any request that does not originate from an allowed ASN, even if the client certificate is valid.
You can extend this pattern to include city-level checks ($geoip2_city_name) or combine multiple criteria with logical operators.
Integrating GeoIP Policy with Authentik IDP
Authentik is a modern open source identity provider that supports OIDC, SAML and LDAP. It can enforce additional policies during the authentication flow, such as requiring a specific claim that matches your geofence rules.
Enable the GeoIP Policy within Authentik
Since Authentik version 2022.12 GeoIP is baked in and only requires mmdb files provided during startup for policies to be enabled.
Upload the same GeoLite2-City.mmdb & GeoLite2-ASN.mmdb files used for Nginx.
Provide the GeoIP (city mmdb) and ASN (ASN mmdb) file paths as env variables during startup
Setup schedule to update the files or configure geoipupdate plugin/container with your license key.
Now every authentication request will have an ASN value attached, which can be referenced in policies.
Maximum distance – The maximum distance allowed in Kilometers between logins
Distance tolerance – The allowable difference to account for data accuracy
Historical Login Count – The number of past logins to account for when evaluating
Check impossible travel – Whether to check logins/sessions for impossible travel >1k
Static Settings
Allowed ASNs – Comma separated list of all of the ASNs allowed to for the given policy
Allowed Countries – List of countries the policy allows connections from
(Optional) Create a Custom Policy in Authentik
In the Authentik admin UI:
Navigate to Customizations → Policies.
Add a new Expression Policy named “GeoIP ASN Allowlist”.
Use the following Jinja2 expression (replace the ASNs with your allowed values):
{% set allowed_asns = [12345, 67890] %}
{{ context["asn"]["asn"] in allowed_asns and context["geoip"]["continent"] == "NA" }}
The context[“asn”] attribute is automatically populated by Authentik when the GeoIP ASN database and context[“geoip”] is provided by GeoIP City database. Both are used in conjunction here to required connections from an approved ASN network and from North America.
Attach the Policy to Your OIDC Application
Open Applications → Your API Gateway.
Under Policy Binding, add the “GeoIP Default” policy.
(Optional) Under Policy Binding, add the “GeoIP ASN Allowlist” policy.
Save changes.
When a user authenticates via Authentik, the flow will evaluate the policy. If the source IP belongs to an unauthorized ASN, authentication fails and no token is issued. This adds a second line of defense: even if an attacker obtains valid client certificates, they cannot get a JWT unless they connected from an allowed network.
Enforce Token Claims at Nginx
You can configure Nginx to validate the JWT issued by Authentik and also verify that it contains the expected ip_asn claim. The ngx_http_auth_jwt_module (available in the open source version) can be used:
http {
# Load GeoIP2 as before
server {
listen 443 ssl;
server_name api-gateway.example.com;
# TLS settings omitted
auth_jwt "Protected API";
auth_jwt_key_file /etc/nginx/jwt-public.key; # Authentik public key
auth_jwt_claim_set $jwt_asn ip_asn;
# Reject if JWT claim does not match allowed ASN list
map $jwt_asn $jwt_asn_allowed {
default no;
12345 yes;
67890 yes;
}
if ($jwt_asn_allowed = no) {
return 403;
}
location / {
proxy_pass http://internal-api;
}
}
}
The flow now looks like this:
TLS handshake verifies client certificate.
Nginx extracts the source IP and performs GeoIP ASN lookup.
The request is redirected to Authentik for OIDC authentication.
Authentik checks the “GeoIP ASN Allowlist” policy; if it passes, a JWT containing ip_asn is returned.
Nginx validates the JWT and ensures the claim matches the allowed list before proxying to the backend.
This combination of device trust certificates, geofence enforcement and IdP policies creates a robust zero‑trust perimeter around your sensitive services.
Best Practices for Maintaining Geofencing Rules
Regularly update GeoIP databases – use geoipupdate with cron or systemd timers.
Keep an audit log of denied requests – configure Nginx error logs to capture $remote_addr, $geo_country_code, $geo_asn_number and the reason for denial.
Use a allowlist rather than a blocklist – allow only known good ASNs/countries; attackers can easily spoof or route through VPNs that belong to allowed regions.
Combine with rate limiting – even legitimate IP ranges may be abused; use limit_req_zone and limit_conn_zone.
Test changes in a staging environment – a mis‑configured ASN map could lock out all users out of an application, including administrators.
Monitor for anomalies – sudden spikes of traffic from an unexpected ASN can indicate compromised credentials.
Access Flow Diagram
Below is flowchart that visualizes the hardened access methodology. Only devices presenting a valid client Device Trust certificate and originating from an allowed location/ASN are permitted to obtain an authentication token and reach the protected service.
The diagram highlights three independent checks:
TLS client certificate – ensures the device holds a trusted private key.
GeoIP ASN validation at Nginx – blocks traffic from unknown networks before any authentication attempt.
Authentik policy enforcement and JWT claim verification – guarantees that the token itself reflects an allowed source network and travel distance is within tolerance.
Only when all three conditions succeed does the request reach the backend service.
Monitoring and Auditing Geofence Enforcement
A hardened gateway is only as good as its visibility. Implementing robust logging and alerting helps you detect misconfigurations or active attacks.
Nginx Log Format Extension
Add a custom log format that captures GeoIP variables:
Create dashboards that filter on status=403 and group by $geoip2_asn_number.
Set alerts for spikes in denied traffic from a single ASN.
Scaling Geofence Enforcement Across Multiple Gateways
In large environments you may have dozens of ingress controllers. To keep policies consistent:
Store the allowed ASN list in a central source (e.g., Consul KV, etcd, or a ConfigMap).
Use a templating engine like envsubst or Helm to generate Nginx configs on each node.
Automate database updates with a CI/CD pipeline that pulls the latest MaxMind files and pushes them to all pods.
By treating the geofence policy as code you can version it, review changes via pull requests, and roll back quickly if an error blocks legitimate traffic.
Conclusion
Geofencing is a powerful yet straightforward technique for hardening access gateways with geofencing. By leveraging free GeoIP data from MaxMind, the ngx_http_geoip2_module in Nginx, and modern identity providers such as Authentik, you can enforce policies that require:
A trusted device certificate.
An allowed source network identified by ASN.
Successful authentication with a policy‑aware IdP.
The layered approach dramatically reduces the attack surface for privileged services, makes credential theft less useful, and gives security teams clear visibility into who is trying to connect from where. Combined with automated updates, logging, and containerized deployment, geofence enforcement can scale across hybrid cloud environments without adding significant operational overhead.
Start by downloading the GeoLite2 databases, compile the GeoIP2 module for your Nginx instances, define a allowlist of ASNs that correspond to your corporate trust network and approved cloud providers, and integrate the policy into your IdP. From there monitor the logs, tune the allowlist as your network evolves, and you’ll have a robust zero‑trust perimeter protecting your most sensitive workloads.
Remember: Device trust plus geofencing equals stronger security – and with the tools described in this post you can implement it today on Linux, cloud, and container platforms.