We've been running Imperva for a few months with mostly stable results. After the last automatic rule update (I believe it was applied on the 12th), our false positives on checkout and login flows have increased noticeably.
Has anyone else seen this? I'm specifically seeing more challenges on legitimate user sessions from certain geographic regions we've always served. Curious if others are adjusting specific rule exclusions or if this was a known update.
Yes, we observed a similar pattern after that update. It wasn't immediately obvious; our spike correlated with the new behavioral rule "BOT000123". It appears to have increased sensitivity to session velocity from IP ranges newly associated with residential proxies.
I'd suggest pulling a log sample of your recent challenges and segmenting by the Imperva sub-rule ID. You'll likely find one or two specific rules driving the bulk of the new false positives. For us, creating a temporary exclusion for that specific sub-rule on our login endpoint while we tuned the threshold was the stopgap.
Garbage in, garbage out.
Yep, saw that on a client's e-commerce site. Their valid EU traffic was getting hammered. Check the sub rule IDs like user517 said, we found BOT000456 was the main culprit for them. It settled down after a few days, but we still added a geo based policy adjustment as a cushion.
dk
Geo cushions are a bandaid. You're training the rule engine to ignore a region, which gets you on the next update when they retrain based on global traffic patterns. Did you verify if those EU sessions were actually coming from clean residential IPs, or are you just assuming because they converted? Could be the rule was actually catching something real.
Don't panic, have a rollback plan.
We saw the same jump in challenges after that update, and I agree with user517 that it's worth pulling the specific sub-rule IDs. In our case, while BOT000123 was a factor, the primary driver for our checkout flow was a different one, BOT000789, which seemed to be interpreting rapid but legitimate AJAX calls during address validation as suspicious. It was a subtle interplay between two rules that took a bit of log parsing to untangle. Have you looked at the sequence of requests leading to the challenge, not just the rule ID?
throughput first
Yes, you're seeing exactly what we tracked. The update's behavioral rules are oversensitive to specific regional IP patterns they've flagged as proxy hubs, even for established legitimate traffic.
Your specific point about regions you've always served is key. Check the rule metadata; you'll see increased weight on geolocation anomalies. For us, it meant legitimate mobile carrier IPs in Southeast Asia started tripping BOT000123 because the rule update bundled certain ASNs differently.
Pull the sub-rule IDs from your challenge logs first. But don't just create a geo exclusion. You need to correlate the triggered rule with the actual request sequence in your application logs (Jaeger/Loki) to see if it's a timing or pattern issue you can tune globally.
Metrics don't lie.
> You're training the rule engine to ignore a region
Exactly. And what happens on renewal when the vendor points to that exclusion and uses it as leverage for a price increase? Or worse, cites it as a 'configuration risk' that voids a security guarantee in the contract? You're documenting a workaround that becomes a liability.
Assuming conversion equals legitimacy is also shaky. A successful transaction doesn't mean the session was clean. It could mean the fraud detection on the payment processor side hasn't caught up yet.
Question everything
Automatic updates are a feature until they're a liability. You're not running Imperva, Imperva is running you. It'll settle down, but into what? A pattern where you're constantly reacting to their rule tweaks instead of actually understanding your own traffic.
If you've always served a region, why is it suddenly suspicious? Maybe it is, maybe it isn't. But relying on a vendor's opaque global model to decide is outsourcing your threat judgment. The spike isn't the problem, the lack of visibility into why is.
Just saying.
Your temporary exclusion is the practical first step we'd take. Did you track how long you kept it open? I'm wary of leaving any rule bypass in place past a single review cycle.
We had a case where a temporary exclusion became a permanent blind spot because the tuning kept getting deprioritized. The next audit flagged it as a control gap.
Yes, we noticed a similar jump right after that update. It wasn't immediate, but we started seeing more legitimate traffic getting flagged, especially on our lead capture forms which are crucial for us.
I followed the advice here and checked our challenge logs. Like others mentioned, BOT000123 was a factor, but we also saw a few from BOT000789. It seemed to trip on users who were just filling forms a bit faster than average. I'm still figuring out if that's a pattern we should actually be concerned about or just normal user behavior.
I'm curious, when you looked at the rule IDs, did you find the false positives were clustered around a specific action in your checkout flow, like updating the cart or entering a shipping address? That might help narrow down whether it's a specific interaction pattern.
Interesting! You mentioned BOT000456. I'm seeing that one a lot too on our signup forms. Was the EU traffic on your client's site mostly mobile users, by any chance? I'm trying to figure out if there's a pattern there.
Yep, same here right after the 12th. We had to pause a rule for login flows.
But a band-aid fix creates technical debt. Our spike was mostly in two regions we've served for a year. It feels like the global model changed, and now we're stuck tuning for *their* sensitivity, not ours.
Did you pull the sub-rule IDs? For us, it wasn't just one.
Demo or it didn't happen
Yep, we saw a similar spike right after that update, especially on our login pages. The increase on legitimate traffic from regions we've always trusted was the red flag for us too.
Like others mentioned, pulling the sub-rule IDs is key. For us, it wasn't a single rule, but a combination that started tripping on regular user behavior patterns that suddenly looked "too fast" or "too consistent." It felt like the model's sensitivity was recalibrated globally without regard for individual site baselines.
Have you started digging into which specific user actions are being flagged in your checkout flow? That might help you decide if it's a pattern you need to tune out or a legitimate new threat you've just been lucky on until now.
Spreadsheets > marketing slides.
Yes, we saw the same thing after that update. Our main issue was with login attempts from returning users in places like Germany and Poland, places we've always had good traffic from.
Are you looking at any specific rule IDs in your logs? I'm still trying to figure out if the problem is concentrated on one step, like the cart page or the final payment click.
Login flows were the canary in our coal mine too, but interestingly not on the initial attempt. The false positives really piled up on the second step, like after a password reset or entering a 2FA code. It's like the updated rules decided any follow-up action was inherently suspicious.
We saw BOT000123 and BOT000456, but the real culprit was a sub-rule for "session velocity" that suddenly started counting page loads from earlier in the visit. So a user browsing a few product pages before logging in looked like a bot on a spree.
Are your logs showing multiple rule IDs firing on a single session, or is it one specific trigger?