AI Penetration testing
We’re among the few firms with offensive security expertise for AI systems, not just traditional apps.
Stay ahead of AI-specific threats like data leakage, prompt injection, and model manipulation, before they erode trust or compliance
At GRSee, we simulate realistic adversarial attacks on your models and surrounding infrastructure to reveal these AI-specific risks—while helping your team mitigate them before they become real-world incidents.
AI penetration testing assesses the security of AI-powered systems, models, and infrastructure by simulating adversarial attacks to identify vulnerabilities in how they process, store, and interact with data. Whether you’re using machine learning (ML), large language models (LLMs), recommendation engines, or custom AI integrations, our AI pentesting approach goes beyond traditional application security. We examine how models can be manipulated, how data can leak, and where attackers can gain unintended access or behavior.
Pentesting AI systems requires a specialized focus on unique vulnerabilities, such as: Prompt injection (in LLMs), Model inversion (reconstructing training data), Data poisoning (influencing model behavior during training), Adversarial examples (inputs designed to fool models), Over-permissive plugin behavior (in AI assistants).
AI penetration testing assesses the security of AI-powered systems, models, and infrastructure by simulating adversarial attacks to identify vulnerabilities in how they process, store, and interact with data. Whether you’re using machine learning (ML), large language models (LLMs), recommendation engines, or custom AI integrations, our AI pentesting approach goes beyond traditional application security. We examine how models can be manipulated, how data can leak, and where attackers can gain unintended access or behavior.
Pentesting AI systems requires a specialized focus on unique vulnerabilities, such as: Prompt injection (in LLMs), Model inversion (reconstructing training data), Data poisoning (influencing model behavior during training), Adversarial examples (inputs designed to fool models), Over-permissive plugin behavior (in AI assistants).
AI PT Benefits
Identify AI-Specific Vulnerabilities
Discover threats unique to AI systems that traditional pen testing would miss.
Prevent Data Leakage
Ensure models don’t inadvertently expose training data, sensitive prompts, or user inputs.
Simulate Real-World AI Threats
Test how attackers can manipulate outputs or bypass business logic protections.
Support Compliance with Emerging Standards
Align with frameworks like NIST AI RMF, ISO 42001, and AI-related GDPR requirements.
What sets us apart
Simplify the Complex.
Deliver with Care.
Deliver with Care.
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