FTC fake reviews rule: paid and insider reviews

The rule

The Federal Trade Commission’s Rule on the Use of Consumer Reviews and Testimonials is codified at 16 CFR Part 465. The Commission announced it on 14 August 2024 by a 5-0 vote, it was published in the Federal Register on 22 August 2024, and it took effect on 21 October 2024. Each conduct section declares the conduct it covers “an unfair or deceptive act or practice and a violation of this part”. The rule is made under the FTC’s rulemaking power in Section 18 of the FTC Act (15 U.S.C. 57a), the route to civil penalties where the conduct is knowing.

The rule has exceptions that matter for day-to-day practice. Section 465.2(d) says paragraphs (b) and (c) do not apply to reviews that result from “generalized solicitations to purchasers”, or to a business “merely engaging in consumer review hosting”. The FTC’s questions and answers say a business that only hosts consumer reviews is not liable under the rule for reviews it did not write or buy, and that the rule “does not prohibit giving incentives for reviews, as long as there isn’t an express or implied requirement that the reviews have to express a particular sentiment”. The FTC also says the rule has no blanket ban on AI-generated avatars in marketing, though other deception can still arise under the FTC Act. Testimonials a business puts on its own pages are advertising messages and fall within the rule.

Penalties come from the rule, not from the FTC’s older Section 5 authority alone. The Commission’s announcement says the rule lets it seek civil penalties where the conduct is knowing. In its December 2025 business blog the FTC put the amount at up to $53,088 for each instance of conduct the FTC counts. That number is inflation-adjusted and changes. The FTC Act’s general ban on deceptive practices still applies to review conduct the rule does not reach. The Fashion Nova (2022) and Cure Encapsulations (2019) cases below predate the rule.

What PeekWell checks and how

US-01 reads the reviews and testimonials a visitor can see on a public page. It asks whether any look possibly fabricated, whether any carry signs of an insider author, and whether the page says how reviews are checked.

The scan finds the testimonial and review blocks. A classifier looks at the text for signs of machine-written wording, and the scan checks for duplicated text between reviews and for photos that match stock images. It also looks for signals on the page that a reviewer may be an insider whose connection is not stated. Code looks for a statement on how reviews are collected or verified on the pages it visited. All of this is reported as “AI-flagged as possibly fabricated”, with a confidence level, and the severity is written as high if confirmed. The scan never says a review is fake, and it never says a business has broken the rule.

The method is the same as EU-11 and UK-13. Only the rule the observations are read against differs.

The scan has limits, and the report says so. It sees the public pages it visited and nothing behind a sign-in. It cannot see whether a reviewer was paid, because incentives live in emails, contracts and invoices. It cannot see which reviews your review service held back, so review suppression under Section 465.7 is outside what a page visit can show. It does not contact reviewers or compare them with order records. A classifier gets some cases wrong. A Passed means the expected behaviour was observed on the pages scanned. It does not say the site complies.

Why it matters for a company

The FTC’s cases on reviews run from before the rule to after it, and show the kinds of practice the rule now names.

In January 2022 the FTC announced a settlement with Fashion Nova, LLC, under which the company agreed to pay $4.2 million. The agency alleged the retailer misrepresented that the reviews on its site reflected all those submitted, while a review tool held back lower-starred ones. This is the conduct Section 465.7(b) now addresses.

In February 2019 the FTC announced what it called its first case challenging fake paid reviews on an independent retail website, against Cure Encapsulations, Inc. and its owner. It alleged they paid a website to create and post Amazon reviews of their supplement. The settlement included a $12.8 million judgment, suspended on payment of $50,000 and unpaid taxes.

In July 2026 the FTC approved a final order against Vanilla Chip LLC, which sold TruHeight. The agency alleged reviews written by employees and vendors, or by consumers offered a free product or discount for a 5-star review, and bot-run social profiles. The order bars misrepresenting reviewer authenticity or experience and purchasing reviews tied to a sentiment, with a $4 million judgment of which $750,000 was payable.

On 22 December 2025 the FTC sent warning letters to ten companies flagging possible problems under the rule. It says the letters are not legal determinations. It described the practices as using fake reviews and providing money or incentives for only positive reviews.

What these matters share is that the conduct can look routine inside a company: an incentive for stars, a moderation setting, a brief to an agency. Those are the things the FTC has pointed to.

Smaller companies and larger companies

The rule applies to any business that sells products or services, with no size line. The FTC’s own cases include a supplement seller and its owner (Cure Encapsulations), and a children’s supplement brand (TruHeight), alongside an online fashion retailer (Fashion Nova).

In a small company the trouble tends to start with a good intention. The owner asks the team to leave a review “to get things going”. A discount code goes out on a card in the box that says “leave us 5 stars”. An agency is hired to build social proof and delivers a batch of testimonials with first names and stock photos. The TruHeight matter is the allegation in this shape: employees, vendors and consumers offered a free product or discount for a 5-star review.

In a larger company it arrives through systems. A review tool is set to publish only the higher ratings. Teams in different regions run their own incentive programmes. A brand runs its own review site and does not say it is the owner. In the Fashion Nova matter the FTC alleged that a review tool automatically posted high ratings and held lower ones, and that the held reviews were never approved over several years.

How to fix it

These steps are practical, not legal advice.

1. Find every review, rating and testimonial on your pages. Include product pages, landing pages, case studies and ad images. Write down who wrote each and how it got there.
2. Take down anything you cannot tie to a real customer. A quote or photo supplied by an agency, or written by staff, should not be shown as a customer’s experience.
3. Check your review requests. Ask all buyers for reviews in the same neutral way. If you offer a reward, it must not depend on the rating or on what the review says. Remove “5 stars for a discount” wording from cards, emails and messages.
4. Label insider content. Staff, owners and relatives who post about the business should say who they are, clearly and visibly. Brief them in writing.
5. Show the negative ones too. Check your review tool for rules that hold back or hide lower ratings. Hold back reviews only on rules applied to every review, such as abuse or content unrelated to the product.
6. Say how reviews are collected and checked. The rule does not require a verification statement as such. It makes a plain note of how reviews are collected and shown a sensible thing to have beside them, and the scan looks for one.
7. Brief agencies and vendors. Contracts and briefs should rule out fake reviews, bought followers and review gating.
8. Re-scan. Run a scan again. Flagged items should be gone or explained, and the note should be found.