Regex-based filters miss obfuscated PII and context-aware prompt injections, forcing developers to choose between fragile pattern matching and unsecured LLM calls. InputArmor intercepts traffic pre-model with semantic analysis to block sensitive data and jailbreak attempts, delivering reliable security without the latency or maintenance overhead of brittle rule sets.
Quantitative Score Breakdown
complaint frequency
3
growth rate
10
competition density
10.5
monetization potential
14.25
technical feasibility
7.5
search interest
5.6
Evidence Signal (2)
Raw Complaint Log
reddit • r/promptengineering
How are you catching PII / prompt-injection before it hits the model? Sharing my regex+Luhn approach and where it falls down.
How are you catching PII / prompt-injection before it hits the model? Sharing my regex+Luhn approach and where it falls down.
Recommended execution roadmap for "InputArmor: Semantic PII & Injection Interceptor"
1
Analyze Complaint Signals
Examine the 2 harvested raw posts to map specific feature complaints, workflow workarounds, and user friction points.
2
Scope Core MVP
Build a minimalist solution focused exclusively on solving "Regex-based filters miss obfuscated PII and context-aware prompt injections, forcing developers to choose betw..." without feature bloat.
3
Engage Early Adopters
Directly engage users in subreddits and developer forums who expressed frustration to offer early access beta invites.
Regex-based filters miss obfuscated PII and context-aware prompt injections, forcing developers to choose between fragile pattern matching and unsecured LLM calls. InputArmor intercepts traffic pre-model with semantic analysis to block sensitive data and jailbreak attempts, delivering reliable security without the latency or maintenance overhead of brittle rule sets.
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