How do you handle a coordinated review attack?

A coordinated review attack, a sudden, organized flood of fake negatives, is handled as an incident with several simultaneous tracks, because no single response is fast or reliable enough on its own. Four tracks run at the same time and converge on the same goal: recovering the rating and checking whether the AI engines have already absorbed the attack.

The four simultaneous tracks

1. Mass platform reporting with pattern documentation. Report the reviews through each platform’s violation process, and document the coordination itself, the timing of the burst, repeated language, suspect account profiles. Review platforms provide a process to report content that breaks their rules (fake, competitor-originated, confidential information, slurs, false or defamatory content), and a documented report citing the specific rule a review breaks has a real chance of removal, though the timeline is unpredictable.
2. Legal escalation against the source. Where the originator can be identified, escalating legally against the source is one of the few things that can stop an active campaign at its root. This is a counsel decision rather than a default. It is strongest where the reviews carry false statements presented as fact (not opinion) against an identifiable target, and most major platforms separately prohibit coordinated inauthentic behavior, which reinforces the case.
3. Accelerate authentic reviews to dilute. The track most reliably within your control is generating genuine reviews from real, recent customers. This dilutes the attack’s weight in both the overall rating and, more importantly, the recent set, which is the slice that readers and the local algorithm tend to weight most.
4. Continuous wave-monitoring. Coordinated attacks arrive in waves rather than as a single event, so the affected platforms have to be watched continuously to catch each new burst as it lands rather than after it has already moved the rating.

Where the tracks converge

The four tracks converge on rating recovery plus an AI-summary check. We track the rating recovery and whether the AI engines have absorbed the attack into their summaries with AIQ, because the engines fold aggregated review content into their answers and can amplify a fake-review cluster well beyond the platform it started on. Containing the attack at the platform level is only half the job; the other half is making sure it does not harden into the model’s standing description of the business.