AI and LLM Application Penetration Testing

We test the security of AI applications, chatbots, AI agents, and RAG systems by simulating real-world attacks. We assess the resilience of large language models (LLMs) against prompt injection, jailbreak attacks, sensitive data leakage, model manipulation, and abuse of external tools and connectors based on the OWASP Top 10 for LLM Applications.

Request a Penetration Test

AI and LLM Application Penetration Testing

Why Test AI Applications?

Organizations are increasingly deploying AI chatbots, AI agents, Copilot solutions, and RAG systems that work with internal documents, sensitive data, and external tools. These applications introduce new security risks that traditional penetration testing may not always detect.

Prompt injection, jailbreak attacks, sensitive data leakage, tool abuse, or manipulation of model responses can lead to unauthorized access to corporate information, unintended actions, or the compromise of an entire AI solution.

AI application penetration testing simulates real-world attack scenarios against large language models (LLMs) and assesses their resilience against both known and emerging attack techniques. Our ethical hackers combine manual testing with proprietary security research and methodologies based on the OWASP Top 10 for LLM Applications.

AI security, however, extends beyond testing individual applications. Learn how artificial intelligence is changing cyberattacks, how we use AI during penetration testing, and how we approach the secure deployment of AI solutions.

Learn more about security in the era of AI transformation

Protection of Sensitive Data

We assess whether an AI application can expose internal documents, personal data, system prompts, or other sensitive information.

Secure Use of AI in Your Organization

We help organizations securely deploy AI chatbots, AI agents, and Copilot solutions while minimizing unnecessary security risks.

Compliance Support

Testing results support cybersecurity risk management and the secure deployment of AI solutions in line with internal policies and requirements such as NIS2 and the AI Act.

Greater User Trust

A secure AI application minimizes the risk of data leakage, incorrect responses, and model abuse, strengthening trust among customers and employees.

What We Test

Request a Penetration Test

Chatbots

We test public and internal AI chatbots, including system prompt protection, context handling, and user input processing.

AI Agents

We assess the security of autonomous AI agents, including their decision-making logic, permissions, and communication with external tools.

RAG Systems

We test the security of Retrieval-Augmented Generation systems, including their interaction with internal documents, databases, and vector stores.

LLM Applications

We assess applications built on OpenAI, Claude, Gemini, Mistral, Llama, and other language models.

AI APIs and Connectors

We assess the security of communication between AI applications, APIs, and external services.

Request a Penetration Test

What Can an AI Pentest Reveal?

An AI Pentest does not only uncover technical vulnerabilities in an application. It also assesses how large language models (LLMs) process inputs, handle context, communicate with external tools, and access sensitive data. We test AI applications against attacks specific to generative AI and determine whether they can be exploited to gain unauthorized access, leak data, or perform unintended actions. Typical findings include:

Attackers may use specially crafted inputs to manipulate instructions given to a language model, bypass the application's intended rules, and influence its behavior.

Malicious instructions can be embedded in external sources, documents, or web content processed by the AI application, influencing the model's behavior without direct attacker input.

We test whether the model's security restrictions and safeguards can be bypassed to make it generate content or perform actions that should be restricted.

We assess whether the AI application can expose personal data, internal information, system prompts, credentials, or other sensitive information.

AI agents and applications may have access to external tools, APIs, or internal systems. We test whether these capabilities can be abused to perform unauthorized or unintended actions.

We assess whether manipulating documents, knowledge bases, or vector stores can influence the information provided by a RAG system to the language model.

We test whether an AI agent has overly broad permissions or can independently perform sensitive operations without sufficient controls or approval.

We assess whether the model's behavior, instructions, or decision-making can be manipulated to produce unexpected or security-sensitive outcomes.

We test whether AI-generated outputs can be manipulated in ways that affect downstream systems, applications, or users and trigger unintended actions.

We assess whether the AI application can leak internal documents, user data, contextual information, tokens, or other sensitive information.

Free Report

Discover What Hundreds of Real-World Penetration Tests Reveal

AI applications, chatbots, AI agents, and LLM systems create new opportunities, but also introduce new security risks. Download our free Ethical Hacking Report 2025 and discover what we found across 628 penetration tests and an analysis of 3,293 identified vulnerabilities in Citadelo projects.

The most common vulnerabilities found in real-world security projects
How vulnerabilities have evolved compared to 2024
Analysis of AI applications, web applications, APIs, cloud environments, and infrastructure
Practical recommendations from experienced ethical hackers

Download the Free Report

Choose Your Penetration Testing Type

Penetration Testing

Penetration testing (pentesting) simulates real-world cyberattacks to identify vulnerabilities before attackers can exploit them. Learn how penetration testing works, what types of pentests we perform, what you receive after the test, and how we help you reduce cybersecurity risks.

Learn about pentesting

Web Applications

We test web applications against attacks such as SQL Injection, Cross-Site Scripting (XSS), authentication flaws, and privilege escalation. Our web application penetration testing follows the OWASP Web Security Testing Guide and OWASP Top 10, with every finding manually verified.

Learn about web testing

Mobile Applications

Mobile application penetration testing for Android and iOS assesses the security of sensitive data, APIs, server communication, local storage, and the application's resilience against reverse engineering, rooting, jailbreaking, and other attack techniques.

Learn about mobile apps

Network Infrastructure and Internal Systems

We simulate attacks against internal and external infrastructure, including firewalls, VPNs, Active Directory, servers, and databases. Network infrastructure penetration testing assesses network configuration, segmentation, privilege escalation, and an attacker's ability to move laterally across systems.

Learn about infrastructure

Cloud Environments

We assess the security of AWS, Microsoft Azure, and Google Cloud Platform (GCP) environments by simulating real-world attacks. Cloud penetration testing covers IAM, cloud storage, Kubernetes, network rules, and exposed services.

Learn about cloud testing

APIs and Microservices

API and microservices penetration testing assesses the security of REST, GraphQL, and other APIs, including communication between microservices. We test OAuth, JWT, API gateways, authorization, input validation, and API resilience based on the OWASP API Security Top 10.

Learn about APIs