AI/ML & LLM Security Testing – Secure AI Models & Large Language Models

As AI adoption accelerates, threat actors exploit vulnerabilities in machine learning models, APIs, and AI-powered applications. Our AI/ML & LLM Security Testing service helps organizations uncover security flaws, data risks, and adversarial weaknesses to strengthen trust, compliance, and resilience.

• Adversarial Machine Learning – Detect manipulation risks using adversarial attacks & evasion techniques.
• Prompt Injection & Data Poisoning – Prevent malicious inputs from corrupting LLM-generated outputs.
• Model Inference & API Abuse – Secure AI APIs against unauthorized access & misuse.
• Bias & Privacy Risks – Identify data leakage, unintended bias, and regulatory compliance gaps.
• Model Theft & Reverse Engineering – Assess risks of model extraction and intellectual property theft.

• Protect AI & LLM Deployments from manipulation & abuse.
• Secure AI APIs & ML Pipelines against exploitation.
• Ensure Compliance with GDPR, SOC 2, ISO 27001, and AI risk frameworks.
• Identify & Mitigate Adversarial Risks before attackers do.
• Tailored AI Security Testing aligned with NIST AI Risk Management Framework & OWASP Top 10 for LLMs.

Key AWS Service Integrations for AI/ML & LLM Security Testing

• Amazon SageMaker Security Testing: Identify adversarial attacks, model inversion risks, and data poisoning threats.
• AWS Bedrock Security Review: Assess LLM security risks, prompt injection vulnerabilities, and unauthorized access issues.
• Amazon API Gateway for AI Security: Secure AI model APIs from OWASP API Security Top 10 vulnerabilities.
• Amazon Macie for AI Data Protection: Detect and secure sensitive data used in AI/ML model training.
• AWS Lambda & Serverless AI Security: Test AI/ML workflows for security gaps in serverless environments.