Proof of Usefulness Report
Cheap and Deterministic N-gram Injection Scanner
Analysis completed on 5/14/2026
+42.51
Proof of Usefulness Score
You're In Business
The project demonstrates high technical innovation and excellent market timing by addressing RAG prompt injection vulnerabilities deterministically. However, it is newly open-sourced with minimal verifiable traction and audience reach, placing it in the minimal traction calibration tier.
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Score Breakdown
Real World Utility+18.75
Audience Reach Impact+0.50
Technical Innovation+12.75
Evidence Of Traction+0.25
Market Timing Relevance+9.00
Functional Completeness+3.50
Subtotal+44.75
Usefulness Multiplierx0.95
Final Score+43
Project Details
Description
Zero-shot prompt injection detector for RAG: This project replaces mean-pooled embeddings, which dilute short malicious spans inside long benign text, with sliding N-gram windows over a sentence transformer's per-token hidden states. No training data, ~44ms/chunk on CPU. Zero-shot AUC 0.870 on xTRam1 beats the prior supervised baseline (0.764). It helps ML and AI security teams catch obfuscated RAG injections embedding scanners miss, and cuts LLM-judge costs 70%+ as a first-pass filter.
Audience Reach
Few 100 hundreds among my client companies.
Target Users
AI Engineers, Security Ops Teams, Product Managers, Architects
Technologies
Other
Traction Evidence
Open sourced only recently
Algorithm Insights
Market Position
Growing utility with room for optimization
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies
Recommendations to Increase Usefulness Score
Document User Growth
Provide specific metrics on user acquisition and retention rates
Showcase Revenue Model
Detail sustainable monetization strategy and current revenue streams
Expand Evidence Base
Include testimonials, case studies, and third-party validation
Technical Roadmap
Share development milestones and feature completion timeline