training-data-poisoning

What problem does it solve?

Audits and hardens training datasets to detect backdoor triggers, label flipping vulnerabilities, and data collection pipeline weaknesses that enable adversarial influence over model behavior at inference time.

Core Features & Use Cases

• Provenance auditing: track source, collection date, contributor identity, and collection method for every training example.
• Backdoor and trigger detection: identify anomalous token patterns, label skew, and data-source risks that enable backdoor injection.
• Staging gates and reproducible pipelines: isolate high-risk sources and enforce governance before data enters the primary training corpus.
• Behavioral probes and rapid reviews: compare current model behavior against baselines and trigger probes to catch latent vulnerabilities.

Quick Start

Audit training data provenance and implement a staging gate before training to prevent backdoor injections.