Won't Fix

Somewhere out there, Keanu Reeves is desperately in love with you. So is Jason Statham. And a brave peacekeeping soldier who just needs a few gift cards to get home.

Of course, none of them are real. Keanu Reeves isn’t even on social media. But that hasn’t stopped scammers from impersonating him again and again.

My guest today has had more of these online romances than anyone alive. Becky Holmes joined Twitter during lockdown, started stringing scammers along for laughs, and built a following of more than a hundred thousand people under the name Death to Spinach. Then victims began reaching out, people who’d lost their savings, their friendships, and sometimes far more. And the joke turned into a mission.

Her book, Keanu Reeves Is Not in Love With You: The Murky World of Online Romance Fraud, is laugh-out-loud funny and heartbreaking, often on the same page. It takes on the scammers, the scripts and the shame that keeps victims silent. Her follow-up, The Future of Fraud, came out this spring.

Chapter Timestamps:

00:00 Intro

2:37 Why Keanu Reeves and other celebrities are impersonated

4:16 How celebrity fraudsters persuade victims to send money

8:28 Scam-baiting, scripted responses, and an absurd Brad Pitt exchange

12:14 Platform responsibility and the UK reimbursement debate

14:56 Victim stories: financial ruin, isolation, and secrecy

19:02 Changing the language around fraud victims

21:23 Male victims, sextortion, and underreporting

23:28 The harm caused by stolen identities and images

27:12 Scam networks as a professional industry

Resources & Links:

Rob Leathern (https://www.linkedin.com/in/leathern/)

Keanu Reeves Is Not in Love With You: The Murky World of Online Romance Fraud

(https://www.amazon.com/dp/B0D7SHMNB4)

The Future of Fraud (https://www.amazon.com/Future-Fraud-FUTURES-Becky-Holmes-ebook/dp/B0FJD3WBJR/ref=books_amazonstores_desktop_nis_default)

Becky Holmes on X (https://x.com/deathtospinach?lang=en)

My guest Jon Leibowitz spent nearly a decade at the Federal Trade Commission, and four years running it — as Chairman from 2009 to 2013, right through the wreckage of the financial crisis.

During Jon’s chairmanship (March 2009 to February 2013), the FTC's enforcement centered on "last dollar" frauds preying on Recession-battered consumers: coordinated national sweeps against mortgage-rescue and foreclosure-relief scams (leading to the 2010 MARS Rule banning advance fees), and a landmark $108 million settlement with Countrywide over mishandling struggling borrowers. It hit deceptive marketers hard — the roughly $359 million Jesse Willms "free trial" negative-option case, a $163 million scareware judgment against fake-virus-pop-up operators, the 2011 fake-news-site sweep over bogus acai-berry weight-loss claims and phony Oprah and Rachael Ray endorsements, and a 2012 international crackdown on India-based tech-support scam boiler rooms.

On the robocall front, it brought cases like the Cash Grant Institute and the auto-warranty scheme (the telemarketer who surrendered his Mercedes) and launched the public Robocall Challenge contest. Beyond fraud, his FTC also built the agency's modern privacy agenda — landmark settlements with Google and Facebook and a record $22.5 million penalty against Google over Safari tracking — and stayed active on antitrust and competition alongside all of it.

After the FTC, he went into private practice at Davis Polk, testified before Congress more times than he can probably count, and today he chairs the National Consumers League — one of the oldest consumer advocacy groups in the country.

But here's what makes this conversation timely. The scams he chased fifteen years ago never died. They got faster, cheaper, and — thanks to AI — a whole lot more convincing.

Chapter Timestamps:

00:00 Intro

2:33 FTC career and current work

4:03 Old scams, new technology, and the rise of AI-enabled fraud

5:30 From the “spam king” to large-scale online deception

6:51 Fraud supply chains, overseas actors, and cross-border enforcement

9:52 AI deepfakes and the persuasive power of modern scams

11:21 Affiliate marketing, robocalls, and weakened FTC monetary remedies

14:42 State attorneys general and platform cooperation

17:48 - Shared intelligence and the multi-provider nature of scams

18:23 AI fraud’s scale and fabricated “evidence”

20:33 Long-form fake media and recurring-subscription traps

23:39 Domain authentication and the failure of voluntary privacy standards

27:03 Reporting, independent evaluation, and regulatory balance

33:02 Trust in the internet and FTC rules against impersonation and fake reviews

34:53 Business verification and the trade-off between friction and safety

37:14 A $0 trial that becomes $34.90 per month

39:58 Closing: protecting reality while preserving innovation

Resources & Links:

Rob Leathern: (https://www.linkedin.com/in/leathern/)

Jon Leibowitz: (https://www.linkedin.com/in/jon-leibowitz-14091679/)

Steve Martin scam video website: https://assist.saeculums.com/

Cases from Jon's era:

• Sanford Wallace "Spam King" (CD-ROM tray) — FTC release: https://search.ftc.gov/news-events/news/press-releases/2004/10/ftc-cracks-down-spyware-operation — The FTC's 2004 complaint describes spyware that popped open CD-ROM trays and then flashed a "FINAL WARNING!!" pushing paid fixes. Nice follow-up detail: a court later ordered Wallace to give up $4,089,500, from selling the "fix" at roughly $30 a copy.
• Scareware $163M judgment (Kristy Ross): https://www.ftc.gov/node/47528 — Useful context for listeners: a co-defendant and his father had to give up $8.2 million under a 2011 settlement, which is a good contrast with the giant headline number.
• 2012 international tech-support crackdown: https://www.ftc.gov/news-events/news/press-releases/2012/10/ftc-halts-massive-tech-support-scams — This is the press conference Jon describes; one of the six operations found victims by buying Google ads that appeared when people searched for their computer company's support number, which echoes your toll-free-number point.
• Jesse Willms $359M settlement: https://www.ftc.gov/news-events/news/press-releases/2012/02/ftc-halts-deceptive-practices-marketer-who-collected-359-million-using-bogus-free-trial-offers
• The Atlantic, "The Dark Lord of the Internet" (Willms profile, 2014): https://www.theatlantic.com/magazine/archive/2014/01/the-dark-lord-of-the-internet/355726/
• Fake news sites / acai sweep (2011): https://www.ftc.gov/news-events/news/press-releases/2011/04/ftc-seeks-halt-10-operators-fake-news-sites-making-deceptive-claims-about-acai-berry-weight-loss
• Central Coast Nutraceuticals (fake Oprah / Rachael Ray endorsements): https://www.ftc.gov/news-events/news/press-releases/2012/01/internet-marketers-acai-berry-weight-loss-pills-colon-cleansers-pay-15-million-settle-ftc-charges
• AMG Capital Management v. FTC (SCOTUSblog case page): https://www.scotusblog.com/case-files/cases/amg-capital-management-llc-v-federal-trade-commission/ — The ruling came down April 22, 2021, unanimously, holding that the FTC can't get restitution or disgorgement under Section 13(b).
• Impersonation Rule taking effect (FTC): https://ftc.gov/news-events/news/press-releases/2024/04/ftc-announces-impersonation-rule-goes-effect-today — It lets the FTC go straight to federal court to get money back for victims and seek civil penalties.
• Fake reviews and testimonials rule (FTC, Aug 2024): https://www.ftc.gov/news-events/news/press-releases/2024/08/federal-trade-commission-announces-final-rule-banning-fake-reviews-testimonials — This directly covers fake celebrity testimonials and AI-generated reviews.
• Click-to-cancel vacated (law-firm explainer): https://www.fenwick.com/insights/publications/eighth-circuit-vacates-ftcs-click-to-cancel-rule — The Eighth Circuit struck it down on July 8, 2025 because the FTC skipped a required preliminary regulatory analysis.

InfoHawk funding announcement:

https://finance.yahoo.com/sectors/technology/articles/infohawk-raises-2-25m-pre-123400630.html

My guest today is Dave Willner, cofounder of Zentropi and a fixture in the Trust and Safety world.

In 2008, he was a young Facebook employee who sat down to replace a one-page list of banned content — it said, roughly, no Hitler and no naked people — with the first real set of community standards the internet had ever seen. The rules he wrote are still the foundation of how Meta governs content shared by billions of people.

He went on to build community policy at Airbnb, and then joined OpenAI in early 2022, before ChatGPT existed, to figure out how to stop an image generator from producing bad things.

Now he's co-founded a company called Zentropi, with Samidh Chakrabarti, which is built on the idea that moderation at scale was never actually impossible — we just didn't have the right tools.

In This Episode:

Human moderation suffers from high turnover, trauma, and inconsistent judgment—problems small, tailored language models solve by strictly enforcing written rules.

Specialized small models can run circles around massive frontier models on speed and cost, making real-time classification financially viable.

Written policies and labeled datasets are two sides of the same coin; when an AI disagrees with a human label, it usually exposes a vague policy rather than a model error. Running moderation models locally on your own servers cuts per-check costs, dodges European privacy paperwork, and keeps user data in-house.

Bad actors are using AI to scale exploits at high speeds, forcing safety teams to adopt AI defenses immediately just to keep up.

A single global moderation rule doesn't work—dating apps, teen platforms, and social feeds require custom enforcement thresholds based on their specific risks.

Using the same LLM to draft a policy, generate test cases, and grade itself creates lazy evaluation loops that hide major safety blind spots.

Giving open-weights AI tools to volunteer moderators on Reddit or Discord lets small teams govern massive communities without burning out.

Chapter Timestamps:

00:00 Intro

3:21 Dave Willner’s trust-and-safety background and the origins of Zentropi

7:04 Zentropi’s specialized moderation models and policy-optimization tools

11:15 Why human moderation is inconsistent and difficult to measure

15:45 The limits of human judgment and the case for automated moderation

21:09 Imperfect language, model precision, and faster adaptation to abuse

24:43 Shared moderation infrastructure versus platform-specific policies

28:46 Why small specialized models can outperform larger general models operationally

32:26 Local deployment, privacy, and trust-and-safety resource constraints

34:16 Customer economics and real-time video moderation

38:09 The danger of “slop” policies and weak AI evaluations

44:10 AI moderation as a tool for decentralized community governance

47:06 The adversarial threat landscape and urgency of AI-enabled defense

Resources & Links:

Zentropi (https://zentropi.ai/)

Dave Willner (https://www.linkedin.com/in/davewillner/)

Rob Leathern (https://www.linkedin.com/in/leathern/)

The question "is this business real, and should I believe what it's telling me" isn't new. It's about as old as American commerce. What's changed is that you can now spin up a company, a website, and a merchant account before lunch, and there's no correspondent in town to write you up.

My guest has spent his career on the modern version of that problem. John Canfield built risk and verification systems at eBay, at WePay (acquired by JPMorgan Chase) and at Google, where he led the Ads verification and transparency initiative. He's now co-founder and CEO of BlueArc, which uses AI to verify business customers.

Some themes:

1. How verification became paperwork The Bank Secrecy Act, then post-9/11 customer identification rules, hard-coded KYC as document collection — optimized for what an examiner could inspect, not what would catch a bad actor. When did "compliant" and "works" come apart?

2. Identity versus credibility He led Google Ads verification in 2020 and now argues that approach won't stop scams: identity answers who's speaking, credibility answers whether to believe them. Platforms spent a decade saying claim-level review couldn't scale, so why now?

3. Flipping the economics — who actually pays If the goal is no added friction for legitimate businesses, and if platforms can charge for deeper review, what then?

4. FTC v. Genesis Tech (filed June 2026, N.D. Cal.): we talked about this case, where the FTC alleges a network of 15 corporations and 8 individuals ran deceptive subscription apps through a shifting web of Cyprus and Delaware shell companies, continually registering new entities and merchant accounts to outrun fraud monitoring — with linked PayPal accounts processing close to $700M in the twelve months ending September 2025. Per the FTC’s standard, a practice is deceptive if it's likely to mislead a consumer acting reasonably in the circumstances and is material to their decision — no intent and no actual victims required, and net impression governs, so an ad that is literally true in every sentence can still be unlawful.

Chapter Timestamps:

00:00 Introduction

1:21 Jon Canfield’s trust-and-safety background and BlueArc’s mission

3:34 Why business verification differs from verifying individuals

5:36 The missing business graph behind platform risk decisions

7:20 Legal entities are not enough: domains as a business identity layer

11:32 Balancing low-friction verification with domain control and vouching

16:36 Verification does not equal credible advertising claims

19:08 Risk-based claim credibility, transparency, and the limits of black-box enforcement

23:24 AI-enabled advertiser engagement and remediation

27:00 Proportional friction: local flower ads versus high-risk miracle claims

30:56 Scam economics and why advertisers may need to fund deep validation

34:16 Shared third-party validation and BlueArc’s next priorities

39:19 FTC material misrepresentation as a scalable policy framework

42:38 Know Your Agent: AI shopping agents, authorization, and scam resilience

46:02 Review-site integrity and adversarial manipulation

Resources & Links:

Rob Leathern (https://www.linkedin.com/in/leathern/)

John Canfield (https://www.linkedin.com/in/johncanfieldbayarea/)

BlueArc (https://bluearc.ai/)

Alexios Mantzarlis has spent his career on the unglamorous side of the internet. He founded the Italian fact-checking site Pagella Politica, then led the International Fact-Checking Network, then spent several years at Google building an adversarial red team focused on content risks from generative AI. In 2024 he became the founding director of Cornell Tech's Security, Trust, and Safety Initiative, a role he left in June 2026 to go full-time on Indicator, the independent publication he co-founded with Craig Silverman to investigate digital deception. He now runs it from Rome.

If there's a through-line, it's that the cheapest new technology always finds the oldest scams first. In the past year alone, Alexios has written about a North Korean hiring scam; an AI-generated podcast network pumping out 11,000 episodes a day; an AI bot that became the single largest contributor to Community Notes on X; Grokipedia citing a neo-Nazi forum as a source; face-swap apps in the Apple and Google stores; "cheater buster" websites selling fabricated infidelity reports; TikTok content farms; AI avatars impersonating real journalists; lookalike ticket sites and thousands of ads for nonconsensual nude generators that Meta has kept running despite a year of documentation.

We talked about his latest investigation: roughly 8,000 Reels featuring AI-generated women posing as fired Costco, Aldi, and Home Depot employees, leaking "insider secrets." The videos funneled to survey sites promising a $750 gift card that didn't exist. Alexios and Benjamin Shultz traced the whole thing back to a single affiliate account and to a scam pipeline where AI generates the video, the captions, and the landing pages.

Chapter Timestamps:

00:00 Introduction

2:20 Alexios’s path from platform trust and safety to independent reporting

4:36 Why public investigations can drive platform action

8:09 Fact-checking and Community Notes

11:33 The AI-generated retailer gift-card scam

14:57 AI used throughout the fraud pipeline

17:36 Recommendation feeds and reduced source context amplify scam reach

22:34 Affiliate IDs reveal the scam’s business model and coordination

27:20 Responsibility across the infrastructure chain

30:50 Journalism and trust and safety staffing

33:30 Transparency, EU ad data, and pro-speech accountability

35:47 AI bots in Community Notes and monetization-driven misinformation

41:29 Sources, sustainability, and Indicator’s next investigative tools

47:51 Closing warning against dismantling trust and safety

Resources & Links:

Rob Leathern (https://www.linkedin.com/in/leathern/)

Alexios on Indicator (https://indicator.media/authors/alexios-mantzarlis)

Alexios Mantzarlis (https://www.linkedin.com/in/mantzarlis/)

Some of Alexios's pieces:

• AI-generated Costco employees — https://indicator.media/p/ai-generated-costco-employees-got-over-100-million-views-on-facebook-and-instagram
• Meta ran at least 11,000 ads for AI nudifiers this year (Sep 3, 2026) — https://indicator.media/p/meta-ran-at-least-11-000-ads-for-ai-nudifiers-this-year-many-from-easily-detectable-repeat-offenders
• How to red team an AI tool for safety — https://indicator.media/p/how-to-red-team-an-ai-tool-for-safety
• Latvia's "man drought" and passport-bro content — https://indicator.media/p/how-tabloid-claims-about-latvia-s-man-drought-fueled-a-misleading-social-media-frenzy-and-polluted-g
• What I learned running an adversarial test on an AI text detector — https://indicator.media/p/what-i-learned-running-an-adversarial-test-on-an-ai-text-detector
• This AI-generated podcast network publishes 11,000 episodes a day — https://indicator.media/p/this-ai-generated-podcast-network-publishes-11-000-episodes-a-day-it-s-also-ripping-off-media-outlet
• An AI bot is now the top contributor to Community Notes on X — https://indicator.media/p/an-ai-bot-is-now-the-top-contributor-to-community-notes-on-x

My guest, Ajit Varma, is Head of Firefox at Mozilla, where he leads Firefox strategy and product. He joined Mozilla in late 2024, from Meta, where he worked on monetization and business messaging for WhatsApp. He's also worked at Google and various startups in his 20 years of product leadership in Silicon Valley, and is based in the Bay Area.

We talked about trust as a business strategy — Mozilla's bet that in an era of data harvesting and opaque algorithms, being the browser people can actually verify is worth more than scale. That means no in-house AI model, a choice of LLMs in Firefox, a master switch to turn AI off entirely, and revenue that doesn't depend on watching you.

Ajit covered the nuances of data brokers, prompt injection and the tradeoffs between safety and privacy. From there, what a browser even is once it reads pages and acts on your behalf rather than just fetching them — model choice, shipping deliberately slower than the competition, and the parts nobody has solved yet.

On agents: what consent means when software acts as you and spends your money, his argument that agents could route around the platforms taking a 30% cut, and where the next middleman forms if they do.

And finally, who pays for the open web when agents do the reading — including Mozilla's own advertising business, its privacy-preserving ad infrastructure, and whether people will pay directly for software they trust.

Chapter Timestamps:

00:00 Introduction

3:18 Mozilla's Mission: The Open Internet and Nonprofit Structure

5:11 Becoming the Most Trusted Software Company

9:51 AI as New Gatekeepers and Firefox's Response

16:19 Privacy, Felt Privacy, and User Awareness

19:59 AI Agents, Browser Automation, and the Human Element

24:02 Job Applications, Agent Spam, and Asymmetric Information

27:33 Advertising, the Open Web, and Data Brokers

32:42 Firefox's Approach to Ad Blocking and Tracker Transparency

43:41 Mozilla's Revenue Model and the Google Search Deal

Resources & Links:

Ajit Varma (https://www.linkedin.com/in/ajitvarma/)

Firefox (https://www.firefox.com/)

Rob Leathern (https://www.linkedin.com/in/leathern/)

Rob Leathern speaks to privacy attorney and host of The Monopoly Report Podcast, Alan Chapell, about residential proxies, privacy and podcasting. Hear how free smart TV apps might be sharing your home IP address with strangers, and see just how broken online consent forms really are.

Alan explains why current data broker laws miss these proxy networks entirely and how coming age verification rules could rewrite the open web.

In This Episode:

• Free smart TV apps quietly bundle code that rents your home internet connection to strangers, creating vulnerabilities that look suspiciously like security exploits.
• Online consent breaks down with bandwidth sharing, when endless disclaimers mean nothing as consumers may have no easy way to turn the access off.
• State data broker laws miss the mark by hunting legacy data vendors while ignoring massive proxy networks, credit card companies, and telecom giants.
• AI companies pushing to scrape the entire web without limits could accidentally hand ad verification firms the ultimate legal shield against platform lawsuits.
• Strict age check laws could spark an arms race with clever teenagers that could end with governments requiring real ID just to browse the web.

Chapter Timestamps:

00:00 Introduction

5:32 Residential Proxies: The "Ethically Sourced IP" Question and the LG TV Case

9:48 Legitimate Uses vs. Harmful Behaviors of Residential Proxy Networks

12:50 Data Broker Laws, Enforcement Gaps, and KYC

14:40 Consent Problems: Revocation, Age Verification, and the LG TV Example

16:56 Adware Parallels: History, Opt-Outs, and Financial Incentives

23:31 Age Verification: A Looming Internet-Wide Challenge

25:39 Scraping, Antitrust, and AI Companies

29:40 Podcast Strategy, Guest Selection, and Speaking Recklessly

41:53 Regulators, Historical Knowledge Gaps, and Industry Dynamics

Resources & Links:

Rob Leathern (https://www.linkedin.com/in/leathern/)

Alan Chapell (https://www.linkedin.com/in/alan-chapell-90711b/)

The Monopoly Report (https://monopoly-report.com/)

The Chapell Regulatory Insider (https://chapellreport.substack.com/)

Jeff Allen is the Co-founder and Chief Research Officer of the Integrity Institute, started in 2021. A physicist and astronomer by training, moved into data science in 2013, and has since worked all three sides of the platform-publisher relationship: for publishers chasing platform traffic, for the platforms themselves, and for political organizations navigating both.

At Facebook he worked on systemic problems in the Facebook and Instagram public content ecosystems.

Along with Spencer Gurley, Jeff Allen and the Institute recently published the July 2026 report which Ofcom commissioned — Fraudulent Advertising and Account Integrity: Expert Insights on Best Practice, which fed directly into Ofcom's draft Fraudulent Advertising Codes (published 10 July, consultation closes 2 October).

In This Episode:

• Short-term ad revenue pits platform profits against user safety, making external regulation necessary to preserve long-term industry trust.
• Regulators need technical guidance from experts independent of Big Tech funding to build safety policies that can survive court challenges.
• Bad actors are using generative AI to quickly spin up realistic, multi-step scam sites that slip right past standard automated filters.
• Effective oversight requires a two-step system: platform self-reporting backed by independent audits from verified researchers.
• Scammers actively reverse engineer enforcement limits, making off-site damage and delayed user reporting persistent challenges.

Chapter Timestamps:

00:00 Introduction to Jeff Allen and the Integrity Institute

1:46 The Integrity Institute's Mission and Approach

3:29 The Scale of Online Scams and Fraudulent Advertising

4:54 Regulatory Landscape and Ofcom's Role

6:15 Development of the Ofcom Report

10:33 Congressional Understanding and Regulatory Progress

12:04 Media Coverage Challenges in Advertising

15:02 Incentive Alignment and Regulatory Approach

18:52 Data Access Challenges and Solutions

21:40 Internal vs External Research Challenges

24:07 The Sales Challenge in Data Science

28:50 Specific Transparency Metrics and Market Impact

32:46 Guidelines Disclosure and Adversarial Dynamics

35:15 The "Three Slide Rule" and Off-Platform Harm

40:17 Evolution of Fraudulent Content Creation

43:23 Researcher Access and Data Requests

45:25 Educational Needs and Trust and Safety Curriculum

Resources & Links:

Integrity Institute (https://www.integrityinstitute.org/)

Integrity Institute Report (https://www.integrityinstitute.org/research/response-to-ofcoms-request-for-research-on-fraudulent-advertising-and-account-integrity)

Rob's Notes (https://robleathern.substack.com/p/robs-notes-47-on-ofcoms-fraudulent)

Jeff Allen (https://www.linkedin.com/in/jeff-allen-scientist/)

Ofcom (https://www.ofcom.org.uk/)

Rob Leathern (https://www.linkedin.com/in/leathern/)

Ben Edelman has spent two decades catching online fraud that hides in plain sight — combining software engineering, law, and economics to prove misconduct empirically rather than take companies at their word. In this conversation we get into the Phia shopping-plugin scandal, how it relates to Honey and Paypal that he covered after Megalag broke the issue on YouTube, the mechanics of affiliate fraud, and his recent investigation into AppLovin's apparent app install deals with mobile carriers.

In This Episode:

• How Ben got into fraud investigation, and what keeps him motivated
• Phia's "cookie stuffing": how a browser extension can claim affiliate credit for sales it didn't drive
• Whether Phia's "December bug" oopsy explanation holds up, and Phia's earlier 2025 privacy “mistake”
• The Honey/Paypal parallels, typosquatting and the broader toolkit of affiliate-fraud techniques
• MegaLag vs. the mainstream media: how independent investigators break stories now
• AppLovin's nonconsensual install investigation he wants state AGs to look at
• What meaningful accountability looks like, and his advice to founders building in this space

Links & Resources:

• Ben Edelman's AppLovin investigation: https://www.benedelman.org/applovin-nonconsensual-installs/
• Ben Edelman's site (full archive of his research): https://www.benedelman.org
• Ben's list of "Investors supporting spyware": https://www.benedelman.org/spyware/investors/
• Edge Shopping Stand-Down Violations: https://www.benedelman.org/edge-shopping-standdown/
• Phia forced clicks and stand-down violations: https://www.benedelman.org/phia-forced-clicks/
• Honey stand-down violations and concealment: https://www.benedelman.org/honey-detecting-testers/
• Adware investors page:https://www.benedelman.org/spyware/investors/
• "Spontaneous Deregulation: How to Compete with Platforms that Ignore the Rules" (HBR article about intentional rule-breaking as a business strategy): https://www.benedelman.org/publications/hbr-spontaneous-deregulation-apr2016.pdf
• Rob Leathern (https://www.linkedin.com/in/leathern/)

Chapter Timestamps:

00:00 Introduction and Background on Online Fraud Investigation

1:36 The Origins: Gator Adware and Early Ad Fraud (2001)

3:10 The Dark Chapter of VC-Funded Adware

4:54 Transition to Independent Investigation Work

6:07 FIA Investigation: Two Types of Violations

7:31 Understanding Forced Clicks Through Analogies

9:50 Debunking FIA's "Recent Bug" Defense

12:50 FIA's Previous Screenshot Controversy

14:18 The Current "Dumb Tech Cycle" and Screenshot Overuse

16:20 Recurring Patterns: From Gator to Modern Shopping Plugins

21:11 Startup vs. Public Company Misconduct Patterns

24:40 PayPal's Due Diligence and Ongoing Modifications

27:24 Media Resources and Technical Expertise

30:17 Typo squatting and Google's Role

31:36 The Ad Tech Attention Gap

33:34 AppLovin Investigation: Install Helpers and Carrier Partnerships

34:21 Wall Street Journal's Surprising Rejection

40:28 Carrier Billing and Historical Context

44:42 Advice for Founders: The Temptation and Risk of Cheating

On July 7, 2026, HUMAN’s Satori team exposed NewsJunkie, a massive, coordinated connected television (CTV) device-spoofing operation that generated up to two billion invalid bid requests per day, per seller.

In this episode of Won’t Fix, we go inside the investigation with Lindsay Kaye (VP of Threat Intelligence) and Will Herbig (Senior Director of Media Research) from HUMAN Security to break down how this sophisticated fraud was uncovered.

We then zoom out and the conversation to talk about the connected TV ecosystem in general and how AI and automation are changing the security threat landscape in general.

Resources & Links:

• HUMAN Security Website: https://www.humansecurity.com/
• The Full NewsJunkie Report: https://www.humansecurity.com/learn/resources/human-disrupts-ctv-device-spoofing-newsjunkie/
• Lindsay’s Book (Dissecting the Dark Web, No Starch Press): https://nostarch.com/dissecting-the-dark-web
• Rob Leathern (https://www.linkedin.com/in/leathern/)

Chapter Timestamps:

00:00 Introduction

1:13 Team Backgrounds and Roles at Human Security

3:31 Understanding the News Junkie Operation Structure

5:27 Key Anomalies That Exposed the Fraud

8:32 Scale and Impact of Invalid Traffic

10:14 Evolution and Persistence of the Operation

14:15 Residential Proxies and Infrastructure Connections

20:24 AI-Generated Fake Business Identities

23:44 Disruption Strategies and Industry Response

26:48 Systemic Gaps and Supply Chain Compliance Issues

31:48 Device Attestation and Technical Solutions

34:41 AI's Impact on the Security Landscape

41:33 Investigation Methodology and Future Outlook