October 23, 2025

Fighting Email Spam with Local LLMs on Your Mail Server

Learn how to use local LLMs on your mail server to effectively identify and combat email spam while ensuring privacy.

What you’ll build / learn

In this tutorial, you will learn how to implement local Large Language Models (LLMs) on your mail server to combat email spam effectively. By the end of this guide, you will have a functioning spam detection system that leverages the power of AI while ensuring your data remains private. We will cover the necessary tools, including Mailcow, Rspamd, and Ollama, and provide a step-by-step guide to set everything up.

You will gain insights into the architecture of the system, how the components interact, and the best practices for maintaining and optimising your spam detection setup. This tutorial is designed for those with a basic understanding of mail servers and an interest in applying AI technologies to enhance their email management.

Additionally, we will discuss common pitfalls and troubleshooting tips to help you navigate any challenges you may encounter during the implementation process. By following this guide, you will be well-equipped to tackle the issue of email spam using advanced AI techniques.

Why it matters

Email spam is not just an annoyance; it can pose serious risks to your security and productivity. Spam emails can contain phishing attempts, malware, and other malicious content that can compromise your data and privacy. Traditional spam filters often fall short, allowing unwanted emails to slip through while blocking legitimate messages.

By using local LLMs for spam detection, you can significantly enhance your email security. These models are trained to understand the nuances of language, allowing them to identify spam more accurately than conventional filters. This not only improves your email experience but also reduces the risk of falling victim to scams.

Moreover, maintaining control over your data is increasingly important in today’s digital landscape. By processing your emails on a local server, you ensure that sensitive information does not leave your premises, thereby protecting your privacy and complying with data protection regulations.

Prerequisites

Before diving into the setup process, there are several prerequisites you need to have in place. First, you should have a mail server running, preferably using Mailcow, which is an open-source mail server suite that simplifies email management. Familiarity with basic server administration and command-line interface (CLI) usage is also essential.

You will need to install Rspamd, an advanced spam filtering system that works seamlessly with Mailcow. Additionally, having Ollama installed will allow you to leverage local LLMs for enhanced spam detection. Ensure that your server has sufficient resources (CPU, RAM, and storage) to handle the processing demands of these tools.

Lastly, it’s beneficial to have a basic understanding of Python, as you may need to write some custom scripts to integrate the various components effectively. With these prerequisites met, you will be ready to embark on the setup process.

Step-by-step

Begin by setting up your Mailcow server. Follow the official documentation to install and configure Mailcow on your server. Ensure that your domain is properly configured to use Mailcow.
Once Mailcow is up and running, install Rspamd. You can do this by following the installation instructions provided in the Rspamd documentation. Make sure to configure Rspamd to work with Mailcow.
Next, install Ollama on your server. This will allow you to run local LLMs. Follow the setup instructions available on the Ollama website to ensure proper installation.
After installing Ollama, you will need to download and configure the LLM model you wish to use for spam detection. Choose a model that suits your needs and follow the guidelines for loading the model into Ollama.
Create a custom Python script that will act as a proxy between Rspamd and Ollama. This script will send emails to the LLM for analysis and receive the spam classification results.
Configure Rspamd to call your custom Python script when processing incoming emails. This involves modifying the Rspamd configuration files to include your script in the filtering process.
Test the integration by sending test emails to your server. Monitor the logs to ensure that Rspamd is correctly invoking your Python script and that the LLM is returning accurate spam classifications.
Once testing is complete, refine your spam detection parameters based on the results. Adjust the thresholds and settings in Rspamd to optimise spam filtering performance.
Regularly update your LLM model and Rspamd configuration to keep up with evolving spam tactics. Staying proactive will help maintain the effectiveness of your spam detection system.
Consider implementing additional security measures, such as rate limiting and IP whitelisting, to further protect your mail server from spam and abuse.
Document your setup process and any custom scripts you create for future reference. This will help you or others troubleshoot and maintain the system over time.
Finally, engage with the community for ongoing support and to share your experiences. Participating in forums and discussions can provide valuable insights and updates on best practices.

Best practices & security

When implementing local LLMs for spam detection, adhering to best practices is crucial for ensuring both effectiveness and security. First, always keep your software up to date. Regular updates for Mailcow, Rspamd, and Ollama will help protect against vulnerabilities and improve performance.

Next, ensure that your server is properly secured. Use strong passwords, enable two-factor authentication where possible, and limit access to your mail server. Regularly review logs to monitor for any suspicious activity, and consider implementing firewalls to block unwanted traffic.

It’s also important to fine-tune your spam detection settings. Regularly review the performance of your LLM and Rspamd configurations, adjusting parameters as necessary to improve accuracy. Additionally, consider using feedback loops where users can report false positives and negatives, helping to train your models over time.

Common pitfalls & troubleshooting

One common pitfall when setting up local LLMs for spam detection is misconfiguration. Ensure that all components are correctly linked and that Rspamd is properly configured to call your Python script. Double-check your server logs for any errors that may indicate configuration issues.

Another issue may arise from resource limitations. LLMs can be resource-intensive, so ensure your server has adequate CPU and RAM. If you notice slow performance, consider upgrading your server resources or optimising your LLM model.

If spam emails continue to slip through, it may be necessary to retrain your LLM with a more diverse dataset. Regularly updating the training data can significantly improve the model’s accuracy in identifying spam.

Alternatives & trade-offs

Alternative	Pros	Cons
Cloud-based spam filters	Easy to set up, no maintenance required	Less control over data, potential privacy issues
Traditional spam filters	Established technology, often included with email services	May not be as effective against sophisticated spam
Custom AI models	Highly tailored to specific needs	Requires significant expertise and resources

While local LLMs provide a powerful solution for spam detection, there are alternatives to consider. Cloud-based spam filters are convenient and require minimal setup, but they often come with privacy concerns as your data is processed off-site. Traditional spam filters are widely used and can be effective, but they may struggle against more sophisticated spam tactics.

Custom AI models offer a tailored approach but demand significant expertise and resources to develop and maintain. Weighing these options against your specific needs and capabilities will help you choose the right solution for your email management.

What the community says

Forums and online groups provide valuable resources for troubleshooting and sharing insights. Users frequently discuss common challenges, such as resource management and model training, and offer advice on how to overcome these hurdles.

Overall, the community is supportive and encourages experimentation, making it a great place to learn and grow your skills in email spam detection.

FAQ

What are local LLMs? Local LLMs are Large Language Models that are run on your own server, allowing you to leverage advanced AI capabilities without sending your data to external services. This ensures greater privacy and control over your data.

How do I know if my spam detection is working? Monitor your server logs and user feedback. If legitimate emails are being marked as spam or spam is getting through, you may need to adjust your settings or retrain your model.

Can I use other models besides Ollama? Yes, there are various LLMs available that can be used for spam detection. The choice of model may depend on your specific requirements and the resources available on your server.

Is this setup suitable for small businesses? Absolutely! This setup can be tailored to fit the needs of small businesses looking to improve their email security without relying on third-party services.

What if I encounter issues during setup? Refer to the documentation for each tool you are using, and consult community forums for troubleshooting advice. Many users have faced similar challenges and can offer solutions.

How often should I update my LLM model? Regular updates are recommended to keep your model effective against evolving spam tactics. Consider retraining your model every few months or whenever you notice a decline in performance.

Source

For more information, visit the source: Fighting Email Spam on Your Mail Server with LLMs.