Point OrbTech at your LLM endpoint and get a compliance-mapped security report in minutes. Running classical ML models too? Those get adversarial testing in the same scan.
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 evolved into an AI security platform focused on helping teams test the AI systems they are putting into production - from LLM applications and chatbots to RAG systems and ML models.
Starting with Indian fintech and healthtech - sectors where data protection, security, and regulatory requirements make AI risk particularly important.
I build security tooling for AI systems. OrbTech came from a gap I kept seeing: AI systems reaching production without enough practical security testing.
Tried OrbTech, or have thoughts on where it should go? Send it over - it lands straight in my inbox.
Bugs, feature requests, or just a reaction - all of it helps.