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Model Poisoning Detection

Detect Tampered Models Before Deployment

Advanced detection of backdoors, data poisoning, and malicious modifications in pre-trained AI models

Types of Model Poisoning We Detect

Backdoor Attacks

Critical

Hidden triggers that cause models to behave maliciously on specific inputs

Data Poisoning

High

Maliciously corrupted training data that degrades model performance

Weight Tampering

Critical

Direct modifications to model weights to alter behavior

Architecture Injection

High

Hidden layers or neurons added to compromise model integrity

Our Detection Approach

1

Statistical Analysis

Anomaly detection in weight distributions

2

Behavioral Testing

Automated testing with adversarial inputs

3

Provenance Verification

Chain-of-custody tracking from source

4

Signature Scanning

Known backdoor pattern recognition

Protect Your AI Pipeline

Don't deploy compromised models. Verify integrity before production.