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LLM Prompt Injection Security Scanner
A developer tool that scans AI product flows for prompt injection, excess context exposure, and exfiltration paths before release. It would combine static checks, simulated attacks, and policy suggestions to help teams ship AI features more safely.
Why this matters
You ship an AI feature that reads user-generated content and suddenly realize the model can also see private account data it never truly needed. The hard part is not knowing in theory that prompt injection exists; it is proving where your product is exposed, what data is reachable, and whether the model can leak it through links, formatting, or clever output. Existing guidance is scattered across papers and opinions, while your team is under pressure to launch. You need something that acts like a security test harness for AI workflows, not another abstract warning.
- · Built for Application security teams, AI product engineers, and startups embedding LLM features into user-facing SaaS products.
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You ship an AI feature that reads user-generated content and suddenly realize the model can also see private account data it never truly needed. The hard part is not knowing in theory that prompt injection exists; it is proving where your product is exposed, what data is reachable, and whether the model can leak it through links, formatting, or clever output. Existing guidance is scattered across papers and opinions, while your team is under pressure to launch. You need something that acts like a security test harness for AI workflows, not another abstract warning.
Score Breakdown
Market Signal
Go-to-Market
Seed-to-Series B SaaS companies with 2-20 engineers actively shipping customer-facing AI assistants, summarizers, or agents
~30K-50K teams globally
Hacker News launch
$99/month
20 teams connect at least one AI workflow and 5 convert to paid within 30 days
MVP Scope · 1–2 weeks
- Build a CLI that ingests prompt templates and context payload samples
- Create 25 prompt-injection test cases covering instruction override, data extraction, and link-based exfiltration
- Implement a rules engine that flags sensitive tokens and over-broad context access
- Generate a simple HTML report with severity levels and remediation notes
- Set up a landing page with waitlist and one sample report
- Add GitHub Action support so scans run on pull requests
- Integrate one LLM provider to replay prompts against live models safely
- Implement policy checks for output restrictions such as links and markdown
- Add diff-based reporting to show newly introduced risk between commits
- Interview 10 AI product teams and refine top three remediation recommendations
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Teams may view prompt injection as unsolved in principle and decide tooling cannot materially reduce risk enough to justify spend.
- 2If the product cannot demonstrate concrete exploit reproduction on real workflows, it may be dismissed as another compliance-style scanner.
- 3Rapid changes in model providers and app architectures could make connectors and policies expensive to maintain for a small team.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
The discussion repeatedly centered on the idea that AI features processing untrusted content can expose private data if models have broad access and any output channel for exfiltration. Roughly a dozen comments described the issue as structurally similar to prior injection classes, while several specifically questioned why a summarization feature needed sensitive identifiers at all. Multiple participants also pointed to architectural mitigations, suggesting demand for productized tooling rather than theory.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
LLM Prompt Injection Security Scanner
Sub-headline
A developer tool that scans AI product flows for prompt injection, excess context exposure, and exfiltration paths before release. It would combine static checks, simulated attacks, and policy suggestions to help teams ship AI features more safely.
Who It's For
For Application security teams, AI product engineers, and startups embedding LLM features into user-facing SaaS products
Feature List
✓ Prompt injection attack simulator for common AI workflows ✓ Least-privilege context audit showing what sensitive data reaches each model call ✓ CI integration with pass/fail policies and remediation guidance
Where to Validate
Share your landing page in r/HN · front_page — that's exactly where these pain points were discovered.
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