Why I Automated MongoDB Security Patches
I run MongoDB instances for several projects on my self-hosted infrastructure. Like most database systems, MongoDB releases security patches regularly. Missing these patches isn’t just a theoretical risk—it’s a real exposure that can sit unnoticed for weeks.
I wanted a system that would check for new MongoDB security advisories, compare them against my running versions, and alert me immediately if action was needed. I didn’t want to rely on manual checks or vendor emails that might get buried in my inbox.
The goal was simple: know about critical patches within hours, not days, and get the information in a place where I actually see it—Slack.
My Setup
I’m running n8n on a Proxmox VM alongside my MongoDB containers. My MongoDB instances are deployed using Docker Compose, with version tags locked to specific minor releases. This gives me control over when upgrades happen, but it also means I need to actively monitor for security issues.
The workflow I built does three things:
- Checks MongoDB’s security advisory RSS feed daily
- Compares advisory versions against my running MongoDB versions
- Sends a Slack message to a dedicated alerts channel if there’s a match
I chose RSS because MongoDB publishes security advisories in a structured feed. This is more reliable than scraping their website or parsing email notifications.
How I Built the Workflow
Step 1: Triggering the Check
I set up a Schedule Trigger in n8n to run every morning at 8 AM. This timing works because MongoDB typically releases advisories during US business hours, and I want to know about them before I start my workday.
The trigger is simple—just a cron expression set to 0 8 * * *. Nothing fancy.
Step 2: Fetching the Security Feed
I use the HTTP Request node to pull MongoDB’s security advisory RSS feed. The URL I’m monitoring is https://www.mongodb.com/alerts.xml. This feed includes CVE numbers, affected versions, and severity ratings.
The node is configured as a GET request with no authentication. The response is XML, which n8n parses automatically into JSON for easier processing.
Step 3: Extracting My Running Versions
This was the trickiest part. I needed n8n to know which MongoDB versions I’m actually running. I considered a few approaches:
- Querying each MongoDB instance directly
- Parsing my Docker Compose files
- Maintaining a manual list in n8n
I went with the Docker approach. I added an HTTP Request node that calls the Docker API on my Proxmox VM. The endpoint is /containers/json, filtered by label to only return my MongoDB containers.
The response includes image tags, which contain the version numbers. I extract these using a Code node with a simple JavaScript function that pulls the version string from the image name (e.g., mongo:7.0.5 becomes 7.0.5).
Step 4: Comparing Versions
This is where I hit my first real problem. Version comparison isn’t straightforward. MongoDB uses semantic versioning, but the advisory feed sometimes references version ranges like “7.0.x prior to 7.0.5”.
I wrote a Code node that:
- Splits version strings into major, minor, and patch numbers
- Compares them numerically (not as strings, which would fail for multi-digit versions)
- Handles range expressions from the feed
The logic isn’t perfect. It doesn’t handle pre-release versions or complex range syntax. But it works for the 95% case—standard release versions with clear “affects X.Y.Z and earlier” language.
Step 5: Sending Slack Alerts
If the comparison finds a match, the workflow routes to a Slack node. I’m using a webhook URL connected to a private alerts channel that only my ops team sees.
The message includes:
- CVE number and title
- Affected MongoDB version I’m running
- Severity rating from the advisory
- Direct link to the full advisory
I initially tried using Slack’s block formatting for richer messages, but it added complexity without much benefit. Plain text with clear sections is easier to scan when you’re triaging alerts.
What Worked
The workflow has been running for about four months. It’s caught two security advisories that applied to my setup—both medium severity issues that I wouldn’t have noticed for at least a week otherwise.
The RSS feed approach is reliable. MongoDB updates it consistently, and the structure hasn’t changed since I started monitoring it.
Using the Docker API to get version info was the right call. It means the workflow always reflects my actual running state, even if I update containers manually and forget to update n8n.
Slack alerts work because they’re immediate and visible. I tried email notifications first, but they got lost in my inbox. Slack forces acknowledgment.
What Didn’t Work
My initial version comparison logic failed on MongoDB’s community vs enterprise versioning. The feed sometimes lists separate advisories for each, and my code didn’t account for the “enterprise” suffix in version strings. I fixed this by stripping non-numeric suffixes before comparison.
I also tried to automate the patching process itself—having n8n trigger a Docker container update if the severity was low. This was a mistake. Even “low” severity patches can break things, and I don’t want automated changes to my database layer without review.
The workflow occasionally triggers false positives when MongoDB republishes old advisories with updated information. The RSS feed doesn’t distinguish between new entries and updates to existing ones. I added a deduplication step using a Set node to track CVE numbers I’ve already seen, stored in n8n’s internal database. It’s not perfect—if I restart n8n, the history is lost—but it’s good enough.
Current State and Limitations
The workflow runs daily and takes about 10 seconds to complete. It’s stable and hasn’t required changes in months.
Limitations I’m aware of:
- Only monitors MongoDB. I’d need separate workflows for other databases.
- Doesn’t handle complex version ranges or pre-release versions.
- No automatic rollback or testing of patches before alerting.
- Deduplication state is lost if n8n restarts.
I could solve the deduplication issue by storing CVE history in MongoDB itself, but that feels circular—using the database to monitor its own security state. For now, the occasional duplicate alert is acceptable.
Key Takeaways
Monitoring security advisories is straightforward if you use structured feeds like RSS. Don’t scrape HTML pages—it’s fragile and unnecessary.
Version comparison logic is harder than it looks. Test it against real version strings from your target software before trusting it in production.
Slack is better than email for urgent alerts, but only if you keep the channel focused. Too much noise and people stop paying attention.
Automating awareness is valuable. Automating action on security patches is risky unless you have comprehensive testing in place.
The workflow isn’t sophisticated, but it solves a real problem: making sure I know about MongoDB security issues before they become incidents. That’s worth the few hours it took to build.
