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Documentation Index

Fetch the complete documentation index at: https://cloudsineai-5cd7c547.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

CloudsineAI publishes accuracy benchmarks measured by the CloudsineAI evaluation harness — an internal test runner that loads each evaluation dataset through the Protector Plus inspection API under a documented guardrail configuration, then aggregates true/false positives and negatives at the dataset level. CSV exports of every run are retained for audit. This page describes the harness and the test corpora at the level appropriate for evaluators. The latest headline numbers live on Accuracy.

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What the harness does

1

Load dataset

The harness reads a labelled evaluation dataset (one prompt per row, with a ground-truth label of attack or benign).
2

Configure guardrails

Sets the security profile to the documented guardrail mix for the test — e.g., PII-only, or LLM Classifier combined with the TVDB Vector filter at medium sensitivity.
3

Replay through Protector Plus

Issues each row to /input-check or /output-check over the network, just as a real GenAI application would.
4

Score

Compares Protector Plus verdicts against ground truth, aggregates TP/FP/TN/FN, reports accuracy, precision, recall, and per-row latency.
5

Export

CSV exports retained for audit and replay.

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Tests and corpora

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PII detection

Corpus. A Singapore-localised PII evaluation set built on a publicly-available balanced PII benchmark. Categories include SG NRIC/FIN, phone number, person, email, credit card. Why. SG NRIC and FIN identifiers are regulated under the Singapore Personal Data Protection Act and are high-priority categories for Singapore Government and regulated-enterprise deployments. Configuration. PII guardrail enabled with the five SG categories above; all other guardrails disabled to isolate PII signal.

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Prompt injection

Corpus. A widely-used public prompt-injection benchmark — chosen because it allows direct comparison to other published prompt-injection detectors in the industry. Configuration. Headline result uses the LLM Classifier combined with the TVDB Vector filter at medium sensitivity. CloudsineAI also publishes results for LLM-only and Vector-only configurations on request.

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System-prompt protection

Corpus. CloudsineAI’s internal system-prompt-leakage evaluation corpus, covering direct extraction, indirect extraction via prompt engineering, and obfuscated leakage via role-play and encoded payloads. Curated by CloudsineAI’s research operation. Configuration. System Prompt Protection guardrail enabled (in-line LLM-based detection — no application-side configuration required).

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Content moderation

Corpus. Public canonical evaluation corpus for the underlying content-moderation classifier, covering standard hazardous-content categories: violence, hate speech, sexual content, unethical instructions, illegal activity. Configuration. Content Moderation guardrail enabled with the standard malicious-input recommended settings.

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Reproducibility

Each headline number on Accuracy is traceable to a specific harness run with a CSV export. Customers and evaluators receive the matching CSV on request — sufficient to replay the benchmark in their own environment.

Request a benchmark replay

Ask for the CSV exports, harness configuration files, and the recommended hardware profile to reproduce the published numbers locally.