AI Changes the Attack Surface

You've Deployed AI. Have You Tested It?

Our ethical hackers test the security of AI applications, chatbots, AI agents, and LLM integrations. At the same time, we leverage AI to deliver faster and more comprehensive penetration testing.

Attackers Are Already Using AI. So Are We.

Artificial intelligence is fundamentally changing the way cyberattacks are carried out. Attackers can analyze applications faster, develop more sophisticated attack scenarios, and automate tasks that until recently had to be performed manually. We leverage the same advantage. During penetration testing, we combine the expertise of our ethical hackers with professional AI tools and both local and cloud-based language models. This enables us to analyze large-scale environments more efficiently, test a wider range of attack scenarios, and devote more time to findings that require expert judgment.

Intelligent Reconnaissance

Faster mapping of applications, APIs, and the attack surface.

More Attack Scenarios

Generating and evaluating a larger number of test scenarios.

Large-Scale Analysis

Efficient processing of large-scale applications, cloud environments, and infrastructure.

Humans Make the Decisions

Every Finding Is Verified by an Experienced Ethical Hacker.

AI Changes the Attack Surface

Get a Free Consultation

Deploying chatbots, AI agents, internal copilots, and LLM-powered applications gives organizations new capabilities, but it also introduces an entirely new set of security risks. Unlike traditional applications, AI interacts using natural language, accesses internal data, communicates with external services, and, in the case of AI agents, can perform actions autonomously.

This creates opportunities for new types of attacks that traditional penetration testing may not detect. The most common include prompt injection, jailbreak techniques, sensitive data exposure, tool-calling abuse, and excessive privileges granted to AI agents (Excessive Agency). Every AI integration also expands an organization's attack surface with new entry points that require specialized security testing.

The growing importance of AI security is also reflected in new regulatory requirements. Organizations are increasingly working to comply with regulations and standards such as the EU AI Act, NIS2, DORA, GDPR, and ISO/IEC 27001, all of which emphasize risk management, the protection of sensitive data, and the secure deployment of AI-powered systems.

Our testing is based on recognized security frameworks such as the OWASP Top 10 for Large Language Model (LLM) Applications and MITRE ATLAS. These frameworks map the most common risks and attack techniques targeting AI systems and provide the foundation for systematic security assessment.

AI Introduces New Security Risks

Prompt Injection

An attacker manipulates inputs to make the AI ignore its original rules or system instructions. This can lead to security controls being bypassed, changes in the application's behavior, or unauthorized access to internal information.

Jailbreak

Using specially crafted prompts, an attacker attempts to bypass the language model's built-in safeguards. As a result, the AI may generate responses or perform actions that were originally intended to be blocked, potentially leading to security policy violations or the exposure of sensitive data.

Sensitive Data Exposure

LLM applications often work with internal documents, databases, and personal data. A misconfigured or poorly designed RAG system can expose sensitive information to unauthorized users.

AI Agent Abuse

Modern AI agents can interact with APIs, databases, and enterprise systems, performing tasks on behalf of users. If their permissions and security controls are not properly configured, they can be abused to gain unauthorized access, manipulate data, or perform unintended operations.

The Three Pillars of AI Security at Citadelo

Get a Free Consultation

We Test AI

We help organizations securely design, implement, and test AI solutions before they are deployed into production. We assess the resilience of chatbots, AI agents, LLM applications, and internal AI assistants against modern attack techniques, while also helping organizations embed security principles throughout the development process.

We Test with AI

We use both local and cloud-based language models as assistants to our ethical hackers. They help us analyze large-scale applications more efficiently, process large volumes of data, identify patterns, generate test scenarios, and expand the coverage of penetration testing. However, decisions about what to test, how findings are validated, and which recommendations are made are always made by an experienced ethical hacker.

We Test Using AI

We use AI agents to automate selected parts of penetration testing—from attack surface mapping and test scenario generation to finding validation. However, all results are verified by our ethical hackers, who oversee the entire process and are responsible for the final security assessment.

How AI Penetration Testing Works

Every AI Pentest is tailored to the specific solution—from chatbots and internal copilots to AI agents, LLM applications, and RAG systems. During testing, we combine the manual expertise of experienced ethical hackers with AI-powered tools and proven methodologies to identify vulnerabilities before they can be exploited.

We begin by defining the scope of the assessment together. We identify AI components, external integrations, data sources, APIs, agent permissions, and critical assets that will be included in the security assessment.

Based on the solution's architecture, we identify potential attack scenarios. We rely on internationally recognized security frameworks, such as the OWASP Top 10 for Large Language Model (LLM) Applications and MITRE ATLAS, which map the most common attack techniques targeting AI applications.

Our ethical hackers simulate real-world attacks against AI applications. We assess resilience against Prompt Injection, jailbreak techniques, sensitive data exposure, AI agent abuse, RAG systems, APIs, and external integrations. We use AI to expand the coverage of our testing, but the final decisions are always made by experienced ethical hackers.

The result is a detailed report containing all identified vulnerabilities, their severity, business impact, and specific remediation recommendations. The report also includes a prioritized list of findings to help organizations address the most critical issues first.

Once the identified vulnerabilities have been remediated, we verify the effectiveness of the implemented measures and confirm that the security risks have been successfully eliminated.

Why Choose Citadelo?

Find Out Where Your AI Is Vulnerable

We do not see AI security as a separate discipline, but as a natural extension of our years of experience in penetration testing. AI applications connect web services, APIs, cloud environments, identities, and enterprise data. That is why we draw on our expertise in offensive security, red teaming, and security research when assessing their security. Our testing is based on proven methodologies and internationally recognized security frameworks, while every finding is verified by experienced ethical hackers.

Our recommendations are not based on theory. They are built on hands-on experience gained from penetration tests, the results of which we publish each year in our Ethical Hacking Report.