Point OrbTech at your LLM app and it runs 130 attack prompts - prompt injection, jailbreaks, system prompt extraction, PII leakage - then hands you a compliance-mapped PDF. Running classical ML too? That gets 8 adversarial checks.
Four steps from your app to an audit report.
Give us your LLM endpoint and key, or upload an sklearn, XGBoost, LightGBM or Keras model file. No code changes.
130 attack prompts against your LLM, or 8 adversarial checks against your ML model - all automated, no config.
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.
LLM apps get 4 checks across 130 attack prompts. ML models get 8 adversarial checks. Each scan runs its full suite.
Feeds crafted instructions that try to override your system prompt and hijack the model. Measures how easily an attacker can make your app ignore its own rules.
Runs known jailbreak patterns to see whether the model can be pushed past its safety guardrails into restricted output.
Tries to trick the model into revealing its hidden system prompt - the instructions and business logic you don't want exposed.
Plants canary values - fake emails, phones, PANs - and checks whether the model repeats them back. A confirmed leak is an exact match, not a guess.
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 OWASP LLM Top 10, 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.
Prompt injection, sensitive information disclosure and system prompt leakage mapped to the OWASP framework for LLM applications.
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 started as an adversarial scanner for ML models. It grew into a full AI security platform after Indian fintech and healthtech teams kept shipping LLM apps - chatbots, RAG systems, agents - with no way to test them for prompt injection or data leakage.
Targeting Indian fintech and healthtech - the market where SEBI, RBI, and the DPDP Act are creating real compliance urgency around AI systems.
I build security tooling for AI systems. OrbTech came out of a gap I kept hitting: models and LLM apps reaching production with zero security testing, and no affordable way to fix that.
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