ProtectAI

Scans language model applications for prompt injection vulnerabilities by testing inputs against known attack patterns and injection techniques. Identifies weaknesses in prompt design and input validation that could allow adversarial manipulation of model behavior.

Analyzes training datasets and model behavior to identify signs of data poisoning attacks where malicious data has been injected to corrupt model outputs or introduce backdoors. Detects anomalous patterns in training data and model responses.

Evaluates how well machine learning models resist adversarial examples and perturbations designed to fool the model. Tests model stability against small input modifications that shouldn't change predictions.

Automated scanning of ML systems and codebases to identify common security misconfigurations, insecure dependencies, and unsafe model deployment patterns. Detects issues like unvalidated model inputs, exposed model artifacts, and insecure API configurations.

Generates automated compliance reports and safety assessment documentation for AI models to meet regulatory requirements like the EU AI Act and SEC guidance. Creates audit trails and evidence of security testing performed.

Tests deployed models against expected behavior specifications to ensure outputs remain safe, accurate, and aligned with intended use. Validates that model behavior hasn't drifted or been compromised post-deployment.

Evaluates the security of ML supply chains including model sources, training data provenance, and third-party dependencies. Identifies risks from using untrusted models or data sources.

Analyzes models for demographic bias, fairness issues, and discriminatory outputs across different population groups. Identifies disparities in model performance and behavior across protected attributes.

Detects and prevents model extraction attacks where adversaries attempt to steal or reverse-engineer proprietary models through repeated queries. Identifies suspicious query patterns indicative of extraction attempts.

Validates that model explanations and interpretability outputs are accurate and trustworthy. Ensures that explanation methods don't themselves introduce vulnerabilities or mislead users about model behavior.