Upload your ML model and get a full vulnerability report - adversarial attacks, privacy risks, model theft, and regulatory compliance mapping.
Four steps from model to audit report.
Upload any sklearn, XGBoost, LightGBM or Keras model file. No code required - just drag and drop.
Adversarial attacks, privacy analysis, model theft simulation, and data integrity - all automated in under 60 seconds.
Download a full audit report with risk scores, findings in plain English, and regulatory compliance mapping.
Each finding includes actionable recommendations your engineering team can implement immediately.
Every audit runs all 8 checks automatically - no configuration needed.
Tests whether adding small noise to inputs causes the model to misclassify. Simulates a real attacker crafting adversarial inputs.
Probes the decision boundary to find the minimum change needed to flip a prediction. Measures how exploitable your model boundary is.
Checks if an attacker can determine whether specific data was in your training set. Relevant to GDPR Article 35 compliance.
Attempts to reconstruct what training data looks like from the model's predictions. Measures training data exposure risk.
Simulates an attacker cloning your model by querying the API repeatedly. Measures how much of your model logic can be replicated.
Detects statistical anomalies in input data that may indicate poisoning attempts - suspicious distributions, label flipping, boundary clustering.
Identifies over-reliance on single features that creates fragility. Relevant to EU AI Act Article 13 explainability requirements.
Documents ROC-AUC and accuracy before any attacks. Provides the performance benchmark all other checks are measured against.
Two layers - plain English for CTOs and compliance teams, full technical findings for engineers.
Plain English findings, immediate actions, and regulatory flags - written for non-technical decision makers.
Full metrics, AUC scores, attack results, and feature analysis - everything your engineering team needs.
Each finding mapped to EU AI Act, GDPR, ISO 42001, and DPDP Act - so your legal team knows exactly what applies.
Built for Indian startups. Not enterprise contracts.
Every finding mapped to the regulations your legal team is asking about.
Maps findings to articles most relevant to high-risk AI systems - risk management, data governance, transparency, and monitoring.
Privacy vulnerabilities like membership inference and model inversion mapped to GDPR obligations helping assess DPIA requirements.
Documented evidence for ISO 42001 clauses covering risk identification, impact assessment, and ongoing monitoring obligations.
India's Digital Personal Data Protection Act obligations mapped to privacy scan findings, critical for RBI and SEBI regulated entities.
OrbTech was built because Indian ML teams deploying models in fintech and healthtech had no affordable way to audit their AI for adversarial vulnerabilities or regulatory compliance.
Targeting Indian fintech and healthtech - the market where SEBI, RBI, and the DPDP Act are creating real compliance urgency around AI systems.
ML engineer and security researcher. I built OrbTech to solve a gap I kept running into: ML models reaching production with zero security testing.
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