Spike in 403s After Tightening a WAF Rule
A retail API sits behind App & API Protector. Last week the security team switched a SQL-injection rule from alert to deny after reviewing alert logs for 30 days and seeing only scanner traffic. Within hours, checkout success dropped 18% and the API team reports a surge of 403s on POST /v2/orders originating from the native mobile app. The rule matches on a JSON body field notes. The mobile app sends free-text notes; the web client sends the same field. Alert-mode logs from the prior month show 4,000 hits, of which 3,850 came from a single IP range belonging to a vulnerability scanner. The remaining 150 hits were spread across many distinct client IPs, all with valid session cookies and successful prior GETs. The team wants to re-enable deny immediately but is unsure how to avoid breaking checkout again.
1. The team proposes adding an IP allowlist for the scanner range to exception the rule. Evaluate this proposal and recommend a better path.
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The proposal is backwards. The scanner range is the traffic you want blocked; exception-ing it would disable the control for the noisiest source and leave the 150 legitimate-looking hits still denied. The real signal is the 150 non-scanner hits: distinct IPs, valid sessions, successful prior navigation - that pattern is consistent with real users whose notes content happens to trip the pattern (e.g., punctuation or keywords that resemble SQL).
Better path:
- Keep the rule in deny for the scanner traffic but scope the exception to the legitimate traffic class, not the scanner. Use a targeted exception keyed on the request context that distinguishes the two - e.g., authenticated session cookie + endpoint
/v2/orders+ the specificnotesfield - rather than an IP allowlist. - Before re-enabling deny, pull the 150 hits and inspect the actual payloads to confirm they are benign free text. If they are, the rule's match condition is too broad for this field and the exception should be written at the parameter level.
- Consider a custom rule or a tuned match that excludes the
notesparameter from this signature while still evaluating the rest of the body, so the SQLi control remains active elsewhere. - Roll out in alert mode on the new exception first, confirm no false negatives on a known-bad test payload, then flip to deny.
2. What monitoring should accompany the change to catch a regression within the first hour?
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You need a signal that fires on business impact, not just security events, because the symptom was a checkout drop, not an alert storm. Minimum set:
- 403 rate on
POST /v2/orderssegmented by client type (native app vs web) and by rule ID, so a single rule's false positives are visible. - Checkout success rate / order conversion as a canary metric with a threshold tied to the pre-change baseline (e.g., alert if it drops more than a few percent within 15 minutes).
- Count of deny events per rule, compared to the alert-mode baseline, so an unexpected jump is caught immediately.
- A synthetic transaction that exercises the native app checkout path with representative
notescontent, run every few minutes.
Pair these with a fast rollback: keep the previous rule configuration exportable so deny can be reverted to alert in one action. The point is that the first 60 minutes is when you want to know, and the business metric is the earliest reliable indicator.