LLM & AI Application Testing

  • Home
  • Services
  • AI & LLM Testing
Abstract image representing Artificial Intelligence security

Securing the Next Generation of Applications

The rapid adoption of Large Language Models (LLMs) and Artificial Intelligence has introduced a new frontier of complex security challenges. Standard application testing methodologies are not sufficient to address the unique vulnerabilities inherent in these systems. At Hackyde, we offer a bespoke testing service designed to assess the security and integrity of your AI-powered applications, protecting you from data leakage, manipulation, and reputational damage.

Our experts understand the nuances of AI security, moving beyond traditional vulnerabilities to evaluate the specific risks associated with LLMs, such as prompt injection, model manipulation, and data poisoning. We help you innovate with confidence, ensuring your AI integrations are robust, reliable, and secure.

Why Test Your AI?

  • Prevent Sensitive Data Leakage
  • Protect Against Model Manipulation
  • Ensure Ethical & Unbiased Outputs
  • Safeguard Against Service Disruption
  • Build Trust with Your Users

Our AI Security Testing Focus Areas

We assess your AI systems against emerging threats, focusing on the OWASP Top 10 for Large Language Model Applications.

Prompt Injection & Manipulation

We test for vulnerabilities where attackers can hijack the LLM's output by crafting malicious inputs, causing it to ignore its original instructions or perform unintended actions.

Insecure Output Handling

We assess whether the application properly sanitizes and handles model outputs, preventing downstream vulnerabilities like Cross-Site Scripting (XSS) or remote code execution.

Sensitive Information Disclosure

Our team attempts to trick the model into revealing sensitive data from its training set, such as personal information, intellectual property, or proprietary algorithms.

Insecure Plugin Design

For LLMs that use external plugins, we test for insecure handling of inputs and insufficient access controls that could lead to widespread system compromise.

Model Denial of Service

We test the model's resilience against resource-intensive queries that could lead to a denial of service, impacting availability and incurring high operational costs.

Training Data Poisoning

We evaluate the controls in place to protect your model's training data from being maliciously manipulated, which could introduce hidden vulnerabilities or biased outputs.