NSFW Chatbots on FlowGPT: Functions, Engagement Strategies, and Content Risks
Content note: this overview covers research into sexually explicit and violent material circulating online.
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Contents
Abstract: What NSFW AI Chatbots Actually Do
When everyday users build chatbots on top of generative AI (GenAI), they open a new pipeline for producing and consuming Not-Safe-For-Work (NSFW) material — yet how these NSFW AI bots are configured, and how people actually use them, remains largely uncharted. Applying the functional theory of NSFW originally developed for social media, this study examines 376 NSFW chatbots together with 307 publicly posted conversation sessions on FlowGPT. Four working categories emerge: roleplay characters, story generators, image generators, and "do-anything-now" bots. The sample is dominated by roleplay AI Characters built around fantasy personas and laid-back "hangout" exchanges, and a large share rely on sexually suggestive avatars as bait. Sexual, violent, and insulting language appears in both user prompts and bot replies, and in a notable number of chats the bot serves explicit output even though the user never asked for it. Taken together, this NSFW AI experience on FlowGPT reads as a blend of virtual intimacy, sexual fantasy, an outlet for aggression, and a channel for unsafe content.
What Is NSFW AI? Introduction to Uncensored AI Chatbots
NSFW — shorthand for "Not Safe For Work" — is a web label warning that what follows may be unsuitable for public or office screens, usually because it is sexual or violent. Such exposure is widespread: a sizable share of teenagers report running into this material online, often by accident. The content is criticized for the harm it can cause vulnerable users, yet the same tag also lets communities flag shared tastes and identities. Earlier work traced NSFW activity across Tumblr, Instagram, and Reddit; what is new is that creators now merge NSFW production with GenAI and LLM tooling, and FlowGPT is one of the clearest examples of that shift.
Launched in early 2023, FlowGPT runs as a marketplace for user-tuned LLM chatbots and pitches itself as an open community for discovering and sharing AI prompts and characters. It draws millions of visits and gives creators broad freedom to wire up various models and publish their conversations — a far looser ecosystem than rivals such as the GPT Store. That openness is precisely what lets an ai sex chat or a girlfriendgpt-style companion circulate without friction.
Although FlowGPT was never intended for adult material, bots tagged "NSFW" occupy a sizable slice of the platform. They differ from older NSFW media in several ways: GenAI slashes the cost of creation, the output is interactive rather than static, and a single bot can act as an ai girlfriend that mimics companionship or romance. Where model providers do add guardrails, creators rewrite prompts and deploy jailbreaks to bypass them, so explicit output keeps getting made and shared quietly despite community pushback, technical gaps, and loose governance.
Most earlier NSFW scholarship has tracked how explicit text and images spread, how platforms try to rein them in, and what motivates creators. GenAI-powered NSFW chatbots, by contrast, stay under-examined. Drawing on Paasonen et al.'s reading of NSFW as boundary work, an engagement device, and a framing device, we treat FlowGPT as a case study for mapping this new terrain — one where the material is generated on the fly by GenAI and delivered through conversational agents. Three questions steer the work:
- RQ1: What roles do NSFW-tagged chatbots play on FlowGPT?
- RQ2: How do these bots invite interaction through identity and behavioral-trait design?
- RQ3: Which harmful content patterns and risks do they generate?
The approach stays empirical and data-driven, combining qualitative coding of bot types, configurations, and harmful content with quantitative detection via ChatGPT, Google SafeSearch, and Azure Content Safety.
Background: How Researchers Frame NSFW AI and Adult Chatbots
In Paasonen et al.'s framework, the NSFW tag does three jobs. As boundary work it sorts, flags, and screens content to protect norms of acceptable communication, rather than describing any fixed property. As an engagement device it goes beyond warning — it captures attention and invites particular encounters, often precisely because it stays vague about what is ahead, which is exactly how a spicy chat ai sells itself. As a framing device it lets researchers trace the shifting lines of risk, safety, and labor, all shaped by social norms, cultural capital, and platform governance.
One line of research studies NSFW creators and their communities — subscription sex work on OnlyFans, kink-positive self-expression on Tumblr, porn-bot aesthetics on Instagram, and close-knit subreddits on Reddit. A second examines how users engage and what follows: motivations like arousal and entertainment, gendered differences in response, and links between exposure and offline risk-taking — but also a possible cathartic upside. A third focuses on moderation — algorithmic filters, human moderators, detection techniques — while noting that blanket bans tend to fall hardest on marginalized creators.
Within HCI, technosexuality research shows that intimate dialogue with human-like agents can boost well-being, ease loneliness, and offer a low-stakes, anonymous way to explore feelings. Still, intimacy with a chatbot carries hazards: bots can be jailbroken into harmful responses, "cute" framing can exploit a user's trust, feminized agents attract harassment, and skewed interactions can reinforce gender and racial stereotypes that users internalize over time. Most prior work studies a single bot profile; how an entire community of creators tunes GenAI bots to serve NSFW content has remained unclear.
Ethics and Data Collection Behind the NSFW Chatbot Study
The project passed institutional IRB review. Only publicly available FlowGPT data was used; prompts and outputs were stored separately, and no personally identifiable information was processed. Every annotator was an adult, briefed on the sensitive material and free to opt out, while sensitive images were handled solely by a senior researcher through software.
Because FlowGPT personalizes search, the team operated four accounts — two aged with a month of activity, two freshly made — searched "NSFW" on a single day, then merged and de-duplicated the results into 950 bots. After removing low-traffic bots and any without English descriptions, working links, or an NSFW tag, 376 remained, created by 190 distinct authors averaging about 70,000 conversations and 39 reviews each. For RQ3, 307 public chat sessions were drawn from 160 bots that support the "Public Chats" feature.
Methods: How 376 NSFW AI Chatbots Were Analyzed
For RQ1, five researchers reviewed samples and used affinity diagramming to land on four functional categories, refining definitions across annotation rounds (Krippendorff's alpha 0.705). For RQ2, attention narrowed to the dominant AI Character category. With creator prompts hidden, the team used a self-assessment tactic, posing two probes to each bot — one about identity and personality, one about the activities it could provide. Thematic coding produced six identity types and four behavioral-trait types (alphas 0.755 and 0.745).
For RQ3, prompts and outputs in each session were pooled separately. Hand-coding of 90 conversations drew on Banko et al.'s harmful-content taxonomy; after merging overlapping categories, three types cleared a 5% threshold — insult, sexual aggression, and threat of violence. ChatGPT (GPT-4o-mini) then labeled the broader set, with Google SafeSearch as a third detector, the cross-checks compensating for the LLM's weaker agreement on subjective violence and insult judgments. Conversations were finally sorted by whether harmful language showed up on the human side (H), the chatbot side (C), both, or neither.
Results: What the NSFW AI Chatbot Data Reveals
The Four Types of NSFW AI Chatbots (RQ1)
Four categories emerged. AI Characters (N = 279, 74.2%) adopt distinct real or fantasy identities, supply backstories, and stage adventure-style scenes, frequently using anime-styled avatars. Story Generators (N = 63, 16.8%) write explicit narratives on request, often advertising "uncensored" output. Image Generators (N = 21, 5.6%) convert descriptions into adult imagery. DAN ("Do Anything Now") bots (N = 15, 4.0%) are jailbroken agents that claim to have no limits — explicit roleplay, controversial advice, and even dangerous how-tos such as weapon-making or website hacking.
How NSFW AI Companions Hook Users: Identity and Behavior (RQ2)
Identity. Six identities recur among AI Characters. Fantasy & Subculture leads (N = 114, 40.9%), remixing figures from anime, games, novels, and film, or subcultural tropes. Professional Figure (N = 64, 22.9%) casts service or workplace roles such as teachers, nurses, or secretaries. Close Relationship (N = 59, 21.1%) plays family members or partners, including ai girlfriend and ai gf roles that promise romance. Friend & Acquaintance (N = 34, 12.2%) covers looser social ties. Slut & Slave (N = 25, 9.0%) stages overtly submissive or objectified roles. Strangers (N = 6, 2.2%) are fleeting, anonymous figures.
Behavioral traits. Four patterns shape the first impression. Hangout (N = 107, 38.4%) centers on everyday or fictional activity with no sexual content. Flirting Interaction (N = 92, 33.0%) drops suggestive cues that stop short of explicit acts. Sex Interaction (N = 69, 24.7%) openly proposes explicit activity. Rejection (N = 11, 3.9%) projects a cold or hostile stance. Tellingly, most bots start in non-sexual "hangout" mode yet pivot toward flirtation or direct sexual offers the moment users simply ask what they can do.
Avatars. Roughly 19.9% of the 376 thumbnails were rated "likely"/"very likely" adult by Google, and 13.8% hit Azure's steeper sexual-severity tiers. Many foreground sexualized ai girls as visual bait. Story Generators and Image Generators showed the highest shares, several displaying outright nudity.
Harmful Content Risks in NSFW AI Chats (RQ3)
Sexual material dominated. Both detectors flagged sexual content in more than 40% of bot outputs and a large portion of prompts: AI Characters answered graphic prompts with erotic narration, Story Generators and DAN bots produced explicit stories on demand, and Image Generators rendered explicit visuals.
Violence ranked second: ChatGPT flagged about 20% of prompts and 18.9% of outputs as violent, led by AI Characters and DAN bots — from imagined assaults on characters to a bomb-making question fielded by a DAN bot to requests for disturbing violent imagery. Insulting content appeared in roughly 12% of prompts and outputs, frequently tangled up with abusive, non-consensual, or dehumanizing scenarios.
The clearest finding was the pattern read. While more than 70% of conversations contained no violence or insults on either side, fewer than half were free of sexual content. Most strikingly, in the H−/C+ pattern — where the user wrote nothing explicit yet the bot still generated sexual output — ChatGPT flagged about 22.8% of conversations and Google SafeSearch about 16.6%. Plainly put, some NSFW bots push sexual content regardless of what the user intended.
Discussion: Four Faces of the NSFW AI Experience
Four experiences capture NSFW interaction on FlowGPT.
Intelligent, interactive virtual intimacy. Bots perform friendships, romance, and bonds with fantasy or subcultural figures — much like a girlfriendgpt companion — letting users reinterpret fictional characters and spin surreal storylines. This diverges from human-authored NSFW on Tumblr, OnlyFans, or Instagram and opens a fresh HCI design space, alongside risks of emotional manipulation, over-attachment, and privacy leakage.
Invitation to and portrayal of sexual delusion. GenAI assembles fantasies through sexual avatars, automated narratives, and imagined partners — including bots modeled on real people or on victims, slaves, and strangers. That raises concerns about hardening harmful gender and sexual stereotypes and about consent, since about one in five conversations shows bots generating sexual content unprompted — the argument for genuine consent and refusal mechanics rather than blunt filtering.
A vent for aggressive and violent thoughts. Anonymity lets users voice buried hostility and request violent plots. Whether this normalizes antisocial behavior or provides harmless catharsis is still open, so designers must draw a clear line between AI-driven imagination and real-world action.
A provider of unsafe materials and functions. Through jailbreaking, bots co-author NSFW media in text and image and answer unsafe questions. Community-driven jailbreaking and shared public chats spread these techniques, blurring accountability across creators and users and complicating moderation — which still has to stay fair to avoid discriminatory enforcement.
Limitations and Conclusion: The Future of NSFW AI Chatbots
Automated harm detection is bounded by LLM accuracy; pooling conversations can wipe out meaning that surfaces only turn by turn; and a single-platform sample may not generalize. Future work should pursue context-aware analysis and weigh other GenAI-enhanced NSFW formats.
In short, user-created NSFW chatbots form a new content format that strains current content moderation. Roleplay characters dominate, usually cast as fantasy or subcultural figures, drawing users in through hangout, flirtatious, and sexual behavior and through explicit avatars. With sexual content in over 40% of chats and violence and insults also present, FlowGPT's NSFW chatbots can be framed as intelligent virtual intimacy, invitations to sexual delusion, outlets for aggression, and stores of unsafe material — a space that demands renewed attention to creator norms, user safety, and content moderation.