ai-ml-security

To build an ML deployment threat model, assess vulnerabilities across the model supply chain, including provenance, dependencies, and deployment risks. This skill generates a defensive playbook to identify and mitigate these specific system threats.

Defend autonomous agents against prompt injection by applying targeted safety playbooks that evaluate agent interaction boundaries. This skill provides remediation guidance and testing strategies to secure autonomous agents from malicious manipulation.

Preventing model extraction and data privacy breaches in MLaaS requires privacy-preserving techniques and robust security controls. This playbook assesses these specific risks and crafts prioritized mitigations for deployment scenarios.

Test for adversarial robustness and model poisoning by applying structured testing strategies derived from a comprehensive threat assessment. This skill identifies these vulnerabilities and provides governance steps for remediation.

Yes, you can assess security risks across various model formats and provenance because the threat modeling covers supply chain integrity and data handling. It evaluates vulnerabilities regardless of the specific model architecture.