Security testing for AI and LLM-powered applications, from prompt injection to model theft.
We conduct comprehensive security assessments of AI and LLM-powered applications to identify potential risks such as prompt injection, jailbreak vulnerabilities, insecure plugin or tool utilization, data leakage, training data poisoning, excessive agency, and model theft. Our certified security professionals employ industry-standard methodologies to deliver detailed findings, proof-of-concept attack scenarios, and actionable remediation guidance customized to your specific environment.
Simulate adversarial inputs and attack techniques to evaluate how AI models behave under hostile conditions.
Identify risks where malicious data can influence training datasets and compromise model integrity.
Assess vulnerabilities related to model theft, reverse engineering, and unauthorized inference through APIs.
Evaluate risks of sensitive data exposure through model outputs and training pipelines.
Analyze the entire ML lifecycle including data ingestion, training, and deployment for security gaps.
Ensure alignment with security, privacy, and ethical AI standards for safe and compliant deployments.
A structured, repeatable methodology that delivers measurable outcomes — every engagement follows the same rigorous process.
Identify AI models, datasets, pipelines, and deployment environments to define the testing scope and objectives.
Analyze potential attack vectors and adversarial risks targeting AI/LLM systems.
Identify weaknesses across models, datasets, and infrastructure components.
Perform controlled attacks to evaluate model resilience against real-world threats.
Assess impact on model performance, data integrity, and overall system security.
Provide detailed findings along with mitigation strategies and best practices for securing AI systems.
Can't find what you're looking for? Reach out directly — our team responds within one business day.
It is the process of identifying vulnerabilities and risks in machine learning models, large language models, datasets, and pipelines.
Adversarial attacks involve manipulating inputs to mislead AI models into producing incorrect outputs.
Yes, models can be attacked through data poisoning, evasion, prompt injection, and extraction techniques.
Yes, we assess the full lifecycle including data ingestion, model training, and deployment.
Yes, we provide actionable recommendations to secure AI systems effectively.
It is becoming essential for organizations adopting AI to ensure secure and responsible usage.
Talk to our specialists today. We'll identify your biggest risks and build a roadmap tailored to your business.
