What's the best way to audit our rules for security holes?

I'm an engineering lead at a mid-size fintech, managing our customer identity platform. We've run Auth0 in production for about three years, currently on a Business plan, with eight custom rules handling everything from step-up authentication to geographic IP analysis.

The best way to audit rules depends heavily on your resources, but here's a breakdown of the main approaches:

1. **Manual Code Review Process**: This is the baseline everyone should do. You need a peer review checklist. Key items for us are: validating any user-supplied input to the rule via `context.request.*`, avoiding secrets in rule code (use the key/value store), and ensuring all `callback` functions are invoked even on errors to prevent login hangs. For a team of two, expect to spend 2-4 hours initially and 30 minutes per new rule review. The cost is internal dev time, but the limitation is it's only as good as the reviewer's security knowledge.

2. **Static Analysis Tools (like Semgrep)**: You can run tools locally against your rule code. We created a small script to pull our rules via the Management API and run Semgrep with a custom rule set. This catches issues like using `eval()` or dangerous global functions. Setup took me an afternoon. The main hidden cost is maintaining the pattern definitions, and the clear win is catching "low-hanging fruit" automatically. It won't catch logical flaws in business logic, however.

3. **Dedicated External Security Audit**: We did this once during our SOC 2 prep. A third-party firm reviewed our Auth0 tenant, including rules. They charged a flat fee in the ballpark of $5-8k for the full identity module. Their report was invaluable, pointing out a potential token replay risk we missed. This is a high-cost option suited for compliance needs or pre-major-release, but not for ongoing checks.

4. **Auth0's Built-in Real-Time Monitoring & Logs**: This is your runtime safety net. You must enable and regularly check the "Auth0 Logs" for rule failures and anomalies. We set up dashboards in Datadog (via their Events API) to monitor failure rates. A rule with a high failure rate can indicate a logic flaw or unexpected input. The cost is the time to set up the monitoring, and the limitation is it's reactive - it only shows issues after they occur or during testing.

My recommendation is to start with a combination of #1 and #4 immediately, as they're within your control and budget. If you're building more than 2-3 new rules a month, invest the day to set up #2. To make a cleaner call, tell us your team size (solo vs. group) and if you're in a regulated industry like finance or healthcare.

Start with manual review. The biggest immediate risk isn't exotic logic flaws. It's rule secrets stored in plain code, or rules that fail silently and block login. Auth0's key/value store exists for a reason.

For a simple checklist:
* Any `context.request` parameter used? Validate it.
* Does every error path call `callback`?
* Any API keys or URLs hardcoded? Move them.
* Are you modifying ID tokens with user-provided data?

Automated tools are limited. You can copy the rule code into a local JS linter with a Node.js sandbox profile, but Auth0's context object is unique. Focus on your own code's patterns.

I've seen too many teams get burned because they treated rules like application code. The problem isn't just "checking" them once. You need to think about change control and cost. Every rule execution has a compute cost, and a poorly written or insecure one can be a hidden billing leak.

Manual review is fine, but what's your proof that it's effective? Where's your data on rule execution failures, latency spikes, or unexpected calls to external APIs from your logs? Security gaps and cost spikes often show up in the same places. Have you even looked at your Auth0 dashboard's logs section, or are you just hoping for the best?

Right, because the logs section of a vendor dashboard is famously comprehensive and never drops entries during peak load. It's a great single source of truth.

You're not wrong about the billing leak, but you're just shifting the hope from "hoping the code is fine" to "hoping the vendor's telemetry is fine." The real cost spike happens when you need to export and analyze those logs somewhere you can actually query them, which is another line item.

You're getting good tactical advice from the other replies, but you're missing the foundational step: quantifying your exposure. Before you even start a code review, you need to establish a cost baseline.

How many logins per day are hitting these rules? What's the average execution duration for each rule? Without these numbers, you can't measure the impact of a hypothetical security fix or the potential cost of an inefficient rule. Security gaps often manifest as unexpected external API calls or rule failures, and both show up as anomalies in your execution metrics and cost per login.

Pull the last 30 days of Auth0 log data, even just to a CSV. Calculate the average and 95th percentile execution time for each rule. That's your starting point for measuring risk. A rule that runs on 10 logins a day presents a different audit priority than one running on 10,000.