Do Tinder Bots Dream of Electric Toys

### Deconstructing Dating App Bots and Algorithmic Profiles At 00:00, Inbar Raz (former Principal Researcher at PerimeterX) opens an investigative presentation examining the proliferation of automated bots on the popular dating application Tinder. Raz outlines how commercial bot operators exploit Tinder's mobile API and image-heavy interface to engage with millions of users at scale.

### Anatomy of Fake Profiles and Social Engineering At 02:01 and 05:00, Raz documents his field research tracking fake profiles across international conferences, from Copenhagen to Las Vegas. He demonstrates how bot operators systematically scrape high-resolution glamour photos, social media models, and adult entertainer images to construct persuasive profiles designed to maximize right-swipes and initiate conversations.

### Dissecting Automated Chat Scripts and Off-Platform Redirections At 08:00 and 15:00, the presentation details the conversational mechanics employed by Tinder bots. Using pre-scripted conversational trees, bots initiate flirtatious dialogue before delivering shortened URLs or messaging app handles. Raz analyzes the redirect chains behind these links, showing how users are funneled through affiliate networks, fraudulent webcam services, and recurring subscription billing traps.

### Infrastructure Attribution: Affiliate Networks and Exploits At 18:00 and 25:00, Raz investigates the backend infrastructure supporting bot campaigns. By capturing network traffic and dissecting HTTP request headers, he traces affiliate referral codes, advertising networks, and hosting servers. He emphasizes the serious security risks posed by unvetted off-platform links, including mobile exploit kits, credential harvesting pages, and drive-by malware infections.

### Corporate Platform Responsibility and Defense Concluding at 28:02, Raz addresses the broader legal and security ramifications for dating platforms. He warns that failing to curb malicious bot ecosystems exposes users to financial fraud and mobile compromise while placing platform operators at severe risk of brand degradation and legal liability.

(AI summary — Gemini Flash)