AI Forensics: in 16 Italian and Spanish Telegram groups, 24K+ men are sharing nonconsensual images of women and girls, buying spyware, and engaging in doxing
In Telegram groups, men are sharing thousands of nonconsensual images of women and girls, buying spyware, and engaging in doxing and sexual abuse.
Context & Ripple Effects
This is part of a persistent Telegram abuse ecosystem rather than an isolated discovery. Italian authorities had already examined Telegram bots used to generate fake nudes from women’s photos, while a later review found communities promoting numerous bots for explicit nonconsensual content at scale across Telegram communities.
The newly documented groups combine image-based abuse with doxing and spyware purchases, connecting content distribution to tools that can enable offline or ongoing harassment. That overlap resembles the targeting documented in Telegram groups harassing women through doxxing and coordinated abuse.
First-order effects
- Women and girls depicted or identified in the groups face immediate privacy, reputational, and safety harm; doxing and spyware offerings can extend exposure beyond the images themselves.
- AI Forensics’ findings give Telegram and relevant investigators a more concrete set of groups, behaviors, and associated services to assess for enforcement action.
Second-order effects
- Moderation cannot be limited to removing individual images: the linkage among groups, bot-enabled sexual content, doxing, and spyware pushes platforms to address discovery channels and repeat-offender networks.
- Providers of abuse-enabling tools face greater scrutiny when investigations document their use alongside nonconsensual-image sharing, while victim-support and reporting systems must handle harms that cross platforms.
Third-order effects
- If these patterns continue, platform-safety policy will increasingly treat synthetic sexual imagery, privacy intrusion, and coordinated harassment as a connected enforcement surface rather than separate content categories.
- The durable challenge is enforcement across semi-private groups and tool ecosystems; effective governance will depend on whether platforms and authorities can disrupt networks without relying solely on individual victim reports.
The trend: This is one data point in the shift from isolated deepfake-image abuse toward networked, multi-tool digital harassment that demands broader platform-safety enforcement.