Assess security of AI/ML models and their infrastructure. Identify vulnerabilities in prompt handling, data exposure, model integrity, and system configurations.
Test for direct and indirect prompt injections, jailbreaking techniques, and system prompt leakage. Assess multimodal attack vectors.
Assess leakage of training data, PII, proprietary algorithms, or confidential business information through model outputs.
Test for manipulated training data, backdoors, and compromised model integrity that could alter AI behavior.
Test vector databases, embedding stores, and RAG systems for information leakage, cross-context data exposure, and embedding inversion attacks.
Test how AI-generated content is processed downstream. Identify code injection, XSS, and other vulnerabilities in application integration.
Assess AI deployment infrastructure, API endpoints, authentication mechanisms, container security, and orchestration configurations.
Test for direct and indirect prompt manipulation techniques including jailbreaking, multimodal attacks, and system prompt extraction vulnerabilities.
Assess exposure of training data, PII, and proprietary information. Test improper handling of AI-generated content leading to XSS or code injection.
Evaluate security of RAG systems, vector databases, embedding inversion attacks, and cross-context information leaks in multi-tenant environments.
Test for over-privileged AI systems, unauthorized actions, resource exhaustion attacks, and model extraction vulnerabilities.
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