OWASP ML Top 10 (Explained)

The OWASP ML Top 10 crystallises the attack surface for machine learning workloads. Translating that list into action requires more than patchwork mitigation - it calls for a defence-in-depth posture that spans pipelines and runtime.

Build Security into Data Pipelines

Data is the choke point. Establish guardrails well before models reach inference.

• Automate dataset provenance checks to detect untrusted contributions and tampering.
• Embed differential privacy or anonymisation steps where personal data is involved.
• Validate labels and reject corrupted batches with statistical and semantic tests.

Harden Models and APIs

Adversaries target gradients, weights, and inference interfaces.

• Enable adversarial training for high-value models and rotate perturbation strategies.
• Throttle inference, enforce authentication, and monitor for scraping patterns.
• Use canary models to detect drift, poisoning, or model extraction attempts.

Monitor in Real Time

Visibility turns unknown unknowns into manageable incidents.

• Stream detections into your SIEM and correlate with traditional security telemetry.
• Trigger automated quarantine workflows when responses breach policy.
• Capture forensic artefacts to support retrospective analysis and regulator inquiries.

ModelGuard from GenShield AI ships with OWASP-aligned playbooks, red-team prompts, and reporting tailored for risk owners and auditors.

Back to blog ↑