Complex text analysis for spoof detection, secret hunting, near-duplicates, and CSV/TSV dialect discovery.
It analyzes text for risk signals and similarity, with explainable results and hard processing limits.
Finds lookalike and mixed-script abuse patterns:
- Unicode normalization checks
- confusable skeleton generation (versioned compact dataset)
- mixed-script detection
- invisible / control-character findings
- explainable risk findings
Does not claim that a heuristic result proves malicious intent.
Scans large text for likely secrets:
- high-entropy tokens
- configurable patterns
- context-aware confidence scores and false-positive controls
- streaming support with line/column reporting
- redacted findings by default
Indexes documents with MinHash / LSH-style techniques:
- configurable similarity thresholds
- incremental indexing
- deterministic seeds
- candidate retrieval plus exact similarity verification
- documented memory / accuracy trade-offs
Infers delimiter, quote, escape, newline, and header likelihood from samples. Returns ranked candidates with confidence and evidence — never silently claims certainty. Sample size and complexity are limited.
- Homograph / phishing username checks
- Pre-commit or CI secret scanning of text blobs
- Deduplicating support tickets, docs, or logs
- Auto-detecting messy CSV dialects before parsing
- Full OCR or NLP pipelines
- Guaranteeing zero secret false positives
dotnet add package FunctionFoundry.TextNone beyond the .NET shared framework.