PAIN TRIGGERS
Common misconfigurations leading to anonymous data leaks across popular backends
Developers won't proactively run security scanning CLIs
Passive/metadata-based scanners lack trust compared to active proof of leaks
SaaS· SaaS founders with production appsPain 8.00/10WTP 8.0/10Market 7.0/10Validation 9.0Confidence 88%May 10, 2026
LeakProbe: Active Proof Scanner for Supabase/Firebase Leaks
Developers ship leaky RLS/rules allowing anonymous read access to user data/tables; existing scanners are passive, untrusted, and fail to drive fixes.
automationbackendcybersecuritydevelopersdevtoolsfirebaseindie-hackerssaassecurity-scanningsupabase
1
STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
SaaS developers and founders using backend services like Supabase, Firebase, PocketBase frequently ship with publicly leaky tables and rules (e.g. anon-readable user data), and neither they nor existing scanners effectively catch or convince them to fix it.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
I shipped 5 open-source backend security auditors after finding 17 leaky tables in my own SaaS. Here's what 100+ random projects taught me.
SaaS15
I shipped 5 open-source backend security auditors after finding 17 leaky tables in my own SaaS. Here's what 100+ random projects taught me.
SaaS15
I shipped 5 open-source backend security auditors after finding 17 leaky tables in my own SaaS. Here's what 100+ random projects taught me.
SaaS15
the only thing that made founders care was exactly what you’re doing: show them a real anon 200 with redacted user rows
commentI went through a similar “oh shit” moment with Supabase and Firebase, and the only thing that made founders care was exactly what you’re doing: show them a real anon 200 with redacted user rows, not a vague “RLS might be off.” Once they see that, they’ll happily jump on a call or share creds for a deeper pass. What worked for me on the “people won’t run a CLI” problem was treating it like outbound: I’d hunt projects where I could already see public config, run the scan myself, then send a super focused Loom walking through 2–3 concrete leaks and fixes, not the whole report. That tiny taste led to way more “ok, do the full thing” than just dropping a GitHub link. On the discovery side, I tried F5 Bot, then HuggingFace Spaces scrapers, and ended up on Pulse for Reddit plus GitHub code search alerts to catch “help, my rules aren’t working” threads right when devs are panicking and open to someone running a quick audit for them.
2
STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
SaaS founders with production appsIndie Saa S Founders & Backend Engineers
Solo-to-small-team founders shipping production apps on Supabase/Firebase who need to verify and fix RLS/rules security before anonymous data exposure.
Context
Identify and fix real security leaks in production backend databases (RLS/rules) before data exposure, with high trust and minimal manual effort.
Building custom active-probe auditors and open-sourcing them after personal incident
Manually hunting vulnerable public projects via GitHub configs, running scans, and sending personalized Loom videos with concrete leaks
Current Workarounds
Building and open-sourcing custom active-probe scripts after personal leaks
Manually scanning GitHub projects and sending personalized Loom videos of real anon data access
Relying on passive metadata scanners that lack credibility
Using GitHub alerts and Reddit discovery to hunt vulnerable peers
3
STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Existing scanners rely on policy metadata inference instead of active probing
No strong forcing function or personalized outreach to get devs to act on scans
Default rules in Firebase/Supabase encourage insecure patterns that persist in production
OPPORTUNITY & VALUE
Why Now
Repeated across multiple complaints: passive scanners ignored, active proof drives action, common anon leaks in popular backends (22% in scans).
Value Proposition
Active proof-of-leak (real 200 responses) vs passive metadata inference; personalized forcing-function reports that actually get founders to fix issues.
Product Direction
Automated active probing scanner that demonstrates real anonymous leaks with redacted proof, then provides one-click rule fix suggestions and monitoring.
4
STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$29/moUnlimited scans for 3 projects
Model
SaaS subscription
WILLINGNESS TO PAY
Founders describe mortifying personal leaks and invest hours in custom tools/Looms; $29 is trivial vs breach risk or time spent manually proving issues to themselves/peers.
5
STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“See your real anon data leaks in one click and ship secure rules today.”
Automated active probing scanner that demonstrates real anonymous leaks with redacted proof, then provides one-click rule fix suggestions and monitoring.
Core Features
Active probe engine for Supabase/Firebase public endpoints
Redacted leak report with exact vulnerable tables/rows
One-click RLS/rules patch generator
GitHub repo integration for scheduled scans
Weekly Roadmap
1
W1-W2
Core active probe works end-to-end for Supabase.
- •Implement anon key probe engine against Supabase tables
- •Generate redacted leak report JSON
- •Basic web dashboard for scan results
2
W3-W4
Firebase support + rule fix suggestions complete.
- •Add Firebase rules probing
- •Build RLS patch generator from leak findings
- •GitHub repo OAuth import
3
W5
Polish, internal dogfooding, and 10 beta users.
- •UI report polishing with Loom-style summaries
- •Scheduled scan cron jobs
- •Recruit beta users from r/supabase
4
W6
Public launch and first paying customers.
- •Stripe billing integration
- •Launch post on Indie Hackers and relevant subs
- •Track conversions from free scan to paid
Launch Strategy
Launch on r/supabase, r/Firebase, Indie Hackers, and targeted GitHub issue outreach to leaky repos
RISKS & ASSUMPTIONS
Top Risks
Probe reliability across backends
Active probing must work consistently against Supabase/Firebase/PocketBase without false negatives or rate limits.
Low conversion from free scans
Founders may acknowledge leaks but delay paid fixes if they believe they can patch manually.
API changes breaking scanner
Backend providers updating auth/RLS endpoints could require frequent maintenance.
Ethical/legal concerns of scanning public projects
Demonstrating leaks on others' projects risks complaints even if data is public.
6
STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
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 memoWhat this score means
This opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 9/10 against 4 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.
Why this matters for SaaS founders
It sits at the intersection of "automation", "backend", "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
Frequently asked questions
Is "LeakProbe: Active Proof Scanner for Supabase/Firebase Leaks" a real validated startup idea or just an AI-generated suggestion?
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.
How recent is the underlying data for automation?
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.
What's the difference between "overall score" and "validation score"?
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.