ai-ml-security

To test ML systems for model extraction and membership inference, you map gradient leakage scenarios and query APIs to determine if training data privacy is compromised. The skill requires structured analysis of model files and gradients to identify these data exposure risks.

Securing LLM agents against prompt injection involves reviewing tool-access boundaries and cross-agent trust workflows. The skill analyzes autonomous agent misuse paths to map risks and mitigations for hijacking attempts.

Assessing ML pipelines for pickle-based loading risks involves evaluating unsafe remote code paths and dependency confusion. The skill reviews model supply chain attacks to identify compromised integrity from poisoned weights.

Adversarial example testing works by mapping inputs designed to deceive ML models into making incorrect predictions. The skill assesses these vulnerabilities by analyzing model files and APIs to identify and mitigate adversarial manipulation scenarios.

Yes, you can analyze model inversion and training-data poisoning scenarios. The skill maps these attack vectors by assessing AI systems to expose how malicious data manipulation compromises model integrity and training data privacy.