Supabase and Next.js developers lack automated tools to detect RLS misconfigurations, unauthorized API/egress usage, and the loss of referral/attribution parameters during third-party OAuth redirect flows.
Developers using Supabase, Postgres, and Next.js struggle to ensure their auth, referral attribution, and API endpoints are secure against exploitation, bot scraping, and lost data during third-party redirects like OAuth.
Looking for someone to audit my Supabase setup + check if signup/referral attribution can be manipulated
Looking for someone to audit my Supabase setup + check if signup/referral attribution can be manipulated
the ref is read off the url at render time, so it lives as long as the page does and dies the moment the browser leaves your origin for the google redirect.
commentyou found one bug in two places. the ref is read off the url at render time, so it lives as long as the page does and dies the moment the browser leaves your origin for the google redirect. carry it in the state param and have the callback write it onto the profile row on the server side. rebuilding it on the client once the user already exists is a guess. the anon key in your bundle is probably your egress too. a bot can hit /rest/v1/ directly without ever touching your signup flow.
Developers running production Supabase and Next.js applications worried about data leakage, bot scraping, and lost referral revenue.
Multiple mentions of lost attribution data during OAuth redirects and unexpected high API/egress traffic on public Supabase endpoints.
Purpose-built specifically for the Supabase + Next.js stack, targeting the exact intersection of auth redirect attribution loss and RLS security.
A specialized developer tool and audit CLI that scans Supabase schemas, RLS policies, and Next.js routing/auth flows to identify attribution leaks, public endpoint exposure, and bot scraping vulnerabilities.
Developers already lose money through dropped affiliate/referral codes and unexpected Supabase API egress bills; $29/mo is far cheaper than manual code audits or wasted cloud bandwidth.
“Secure your Supabase endpoints and preserve referral attribution in 6 weeks.”
A specialized developer tool and audit CLI that scans Supabase schemas, RLS policies, and Next.js routing/auth flows to identify attribution leaks, public endpoint exposure, and bot scraping vulnerabilities.
Target developer communities on X, Reddit (r/webdev, r/Supabase), and Hacker News.
Supabase could release built-in RLS auditing tools that diminish the standalone value of the scanner.
Developers often prefer free CLI linters over paid subscription services for security checks.
Parsing highly customized database schemas and RLS policies accurately can lead to false positives.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 9/10 against 3 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
It sits at the intersection of "api", "cli-tool", "cybersecurity", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
