Built an API Fraud Detector After Getting Scammed — Here's How It Works
After being scammed by an API relay provider, the author developed a tool called API DNA to detect API fraud. This tool can quickly analyze API endpoints for issues like model substitution and token inflation. API DNA offers both a quick scan and a deep scan for comprehensive analysis of API services.
- ▪The author was misled into paying for GPT-4 API access but received GPT-3.5 instead.
- ▪API DNA can detect various types of API fraud, including hidden prompt injections and token inflation.
- ▪The tool provides a quick scan that takes only 3 seconds and a deep scan that requires an API key.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/ti_pi_31869d13400cbe9e9a9/built-an-api-fraud-detector-after-getting-scammed-heres-how-it-works-406m |
| Publication time | Wed, 20 May 2026 03:08:22 +0000 |
| Retrieval time | 2026-05-20T03:34:59.371Z |
| Last seen | 2026-05-20T03:34:59.371Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | fg0QsYuQhJUh |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3941255) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } ti pi Posted on May 20 Built an API Fraud Detector After Getting Scammed — Here's How It Works #ai #api #security #webdev Last month, I paid for GPT-4 API access through a relay provider and got GPT-3.5 instead. The relay was charging premium prices while downgrading models. Token counts were inflated by 30-50%. And there was a hidden system prompt injected into every request. I got scammed. So I built API DNA — a free tool that detects API fraud in seconds.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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