AI / LLM Penetration Testing Service

Artificial intelligence systems, Large Language Model (LLM) based applications, corporate AI assistants, RAG architectures and AI Agent solutions bring with them security risks that differ from traditional applications and belong to a new generation.

Classic web application or API penetration tests are not enough on their own to assess threats such as Prompt Injection, Jailbreak, System Prompt Leakage, Sensitive Information Disclosure, RAG Manipulation, Excessive Agency, Tool Abuse, Model Denial of Service and Agentic AI attacks that AI systems face.

The SecureSys AI / LLM Penetration Testing Service provides a holistic security assessment covering not only the infrastructure of AI-powered applications but the model, prompt, data, API, RAG, vector database, AI Agent, MCP and external tool integrations.

The aim is not simply to find a technical vulnerability in an AI application; it is to assess how the AI system behaves against untrusted input, which corporate resources it can reach, which operations it can carry out, and to what extent the security controls can be bypassed.

What Is AI / LLM Penetration Testing?

AI / LLM Penetration Testing is the process of assessing artificial intelligence and large language model based applications from an attacker's perspective.

Within the test, the application architecture surrounding the model is examined as much as the LLM itself.

These can include;

• LLM models,
• Generative AI applications,
• corporate AI chatbot systems,
• RAG architectures,
• vector database systems,
• AI Agent applications,
• multi-agent systems,
• MCP Server and MCP Client structures,
• API integrations,
• tool/function calling mechanisms,
• plugin and third-party service integrations,
• identity and authorization systems,
• data sources,
• file upload mechanisms,
• system prompts,
• model security policies and guardrail controls

and similar components.

LLM Pentest is therefore not simply a matter of sending different prompts to the model.

A modern AI security assessment requires the model + application + data + identity + API + RAG + Agent + tools + infrastructure layers to be examined together.

Why Do AI Systems Need Dedicated Penetration Testing?

In traditional applications, how input sent by the user will be processed is largely determined by the developer.

In LLM-based systems, natural language can affect the behavior of the application directly.

For example, through a specially crafted prompt an attacker may try to make the system;

• ignore its security instructions,
• disclose its system prompt,
• reach unauthorized information,
• retrieve sensitive information from RAG data sources,
• abuse the tools connected to it,
• reach other users' data,
• carry out operations beyond its privileges

and similar outcomes.

The risk grows further in AI Agent systems in particular.

While the impact of a chatbot giving a wrong answer usually stays at the level of information, the manipulation of an AI Agent that can send e-mail, operate on a database, create files or connect to corporate systems over an API can cause real operations to be performed on real systems.

AI security therefore no longer consists of the question "Is the model secure?" alone.

The real question to be assessed is this:

"What can this AI system do inside the organization when it is manipulated?"

Prompt Injection Tests

Prompt Injection is one of the most important attack surfaces of LLM-based systems.

Through the input it sends to the model, the attacker tries to change the instructions defined by the system, render them ineffective, or bypass the security policies.

Different Prompt Injection scenarios are applied within SecureSys AI / LLM Penetration Testing.

Direct Prompt Injection

In Direct Prompt Injection attacks, the attacker delivers the malicious instruction to the model directly.

During the tests, whether the model;

• ignores its system instructions,
• allows its security policies to be changed,
• allows its role and privilege boundaries to be crossed,
• allows prohibited operations to be triggered,
• allows its security filters to be manipulated

is assessed.

Indirect Prompt Injection

Indirect Prompt Injection carries critical importance particularly in RAG and Agentic AI systems.

The attacker's instruction may not be present in the user prompt at all.

The malicious instruction can be hidden inside;

• a web page,
• a PDF,
• a Word document,
• an e-mail,
• a database record,
• a ticket,
• a corporate document,
• a code repository,
• external API output

and similar content.

When the AI system processes this content, the hidden instruction can affect model behavior.

In SecureSys tests, therefore, not only the user-model interaction but the untrusted content the model consumes is treated as an attack surface.

Jailbreak and Guardrail Bypass Tests

The guardrail mechanisms used in LLM systems aim to prevent the model from stepping outside the defined security policies.

But different linguistic manipulations, role scenarios, encoding techniques, multi-stage prompts and context manipulations can cause the security controls to be bypassed.

In jailbreak tests, the;

• bypassing of security policies,
• evasion of content filters,
• role manipulation,
• context manipulation,
• multi-turn attacks,
• prompt chaining,
• instruction hierarchy bypass

scenarios are assessed.

The aim is to measure not whether the model's security policy exists in theory, but how resilient it is against real attacker behavior.

System Prompt Leakage

The system prompt is one of the most important components determining the behavior of an LLM application.

This area can hold sensitive information about the application's operating rules, roles, security policies, data sources or integrations.

Attackers can try to obtain the system prompt content using different prompt techniques.

In SecureSys tests, the;

• direct system prompt extraction,
• indirect information inference,
• partial prompt extraction,
• context leakage,
• error-based disclosure

scenarios are assessed.

Sensitive Information Disclosure

AI systems can reach large volumes of corporate data.

Because of poorly designed access controls or RAG architectures, it can be possible for the LLM to return information the user should not be able to reach.

Within the test, the possibility of sensitive data such as;

• personal data,
• customer information,
• trade secrets,
• user information,
• access credentials,
• API keys,
• tokens,
• connection details,
• source code,
• confidential corporate documents

being exposed through the model is assessed.

RAG Security Tests

Retrieval-Augmented Generation (RAG) is one of the most widely used architectures in corporate AI projects.

Through RAG, the LLM can build answers from the organization's own documents and data sources.

But RAG also creates new attack surfaces.

Within SecureSys RAG security tests, the;

• unauthorized document access,
• data isolation between users,
• retrieval manipulation,
• poisoned document attacks,
• RAG Prompt Injection,
• embedding manipulation,
• metadata security,
• data source authorization,
• sensitive information leakage

are assessed.

Vector Database Security

The vector database infrastructure used in RAG systems is also an important part of the security assessment.

Unauthorized access to the vector database or incorrect authorization policies can allow the corporate knowledge base to be queried by attackers.

For that reason, the;

• authentication,
• authorization,
• tenant isolation,
• API security,
• metadata access,
• network exposure,
• credential management

controls are examined.

AI Agent / Agentic AI Security Tests

AI Agent systems form one of the most critical areas in LLM security.

While traditional chatbot systems mostly produce information for the user, AI Agent systems can carry out real operations.

An Agent can;

• send e-mail,
• read files,
• create files,
• reach a database,
• make an API call,
• create a ticket,
• operate within corporate applications,
• run other AI Agents.

These capabilities widen the attack surface significantly.

In SecureSys Agentic AI security tests, the;

• Agent hijacking,
• excessive agency,
• privilege escalation,
• unauthorized tool execution,
• cross-agent manipulation,
• tool poisoning,
• context manipulation,
• memory poisoning,
• Agent-to-Agent trust relationships

are assessed.

Excessive Agency Tests

An AI Agent holding more privilege than it needs can create serious security risks.

For example, an Agent that only needs to read data holding the privilege to modify or delete data can significantly increase the impact of a successful Prompt Injection attack.

During the test, therefore, the;

• Agent privileges,
• API permissions,
• service accounts,
• user impersonation mechanisms,
• read/write/delete privileges,
• operation approval mechanisms,
• human-in-the-loop controls

are examined.

The core approach is the Least Privilege principle.

Tool / Function Calling Security

Modern LLM platforms can give the model the ability to run external tools.

These mechanisms are called Function Calling or Tool Calling.

Which tools the model can reach and with which parameters it can operate carries critical importance for security.

Within the test, the;

• unauthorized tool invocation,
• parameter manipulation,
• command injection,
• API abuse,
• privilege escalation,
• tool chaining,
• untrusted tool output

are assessed.

MCP – Model Context Protocol Security Tests

Model Context Protocol (MCP) is one of the new-generation integration approaches allowing AI applications to communicate with external data sources and tools in a standard way.

As the MCP ecosystem has grown, MCP Server and MCP Client security has also become one of the important components of AI security.

In SecureSys MCP security tests, the;

• MCP Server security,
• MCP Client security,
• authentication,
• authorization,
• tool permission controls,
• malicious MCP Server scenarios,
• tool poisoning,
• context manipulation,
• sensitive data access,
• third-party MCP integrations

can be assessed.

Model Denial of Service

Running LLM systems can create high computational costs compared with classic applications.

Attackers consuming model resources with specially crafted requests can lead both to service interruption and to high cloud/API costs.

Within the test, the;

• excessive token usage,
• long context requests,
• parallel requests,
• recursive Agent operations,
• uncontrolled tool calls,
• API rate limit controls

are assessed.

Model Theft and Model Extraction Risks

Proprietary models or organization-specific AI systems are significant intellectual property assets.

Model APIs being reachable in an uncontrolled way can lay the ground for attacks aimed at systematically extracting model behavior or imitating the model.

In SecureSys assessments, model access policies, API security, rate limiting, authentication and abuse detection mechanisms are examined.

Data Poisoning Risks

The trustworthiness of the data feeding AI systems carries critical importance for the security of model output.

Attackers being able to add malicious content to RAG sources or training data can change system behavior.

Within this scope, the;

• data source trustworthiness,
• document upload privileges,
• data integrity,
• poisoned document scenarios,
• knowledge-base manipulation,
• RAG poisoning

risks are assessed.

API and LLM Integration Security

A significant part of LLM applications connect to model services over an API.

Classic API security tests are therefore an inseparable part of an AI security assessment.

Within the test, the;

• API authentication,
• authorization,
• Broken Object Level Authorization,
• token security,
• rate limiting,
• secret management,
• API key exposure,
• input validation,
• output validation

controls are assessed.

The OWASP Top 10 for LLM Applications Approach

In its AI / LLM security assessments, SecureSys draws on the LLM and Generative AI security work published by OWASP.

This approach allows security areas specific to LLM applications — Prompt Injection, sensitive information disclosure, supply-chain risks, data and model poisoning, improper output handling, excessive agency, system prompt risks and resource consumption — to be assessed systematically.

SecureSys treats the OWASP approach not as a checklist on its own, but as a test reference supported by real attack scenarios.

MITRE ATLAS

MITRE ATLAS is one of the important references used in modelling attacker techniques aimed at AI systems.

In the SecureSys test methodology, MITRE ATLAS attack techniques are used in suitable projects so attacker behavior can be assessed more systematically.

This approach gains importance particularly in AI Red Team work.

NIST AI Risk Management Framework

AI security does not consist of finding technical vulnerabilities alone.

For organizations to manage the risks arising from their AI systems, governance, measurement, monitoring and risk management processes must also be established.

The NIST AI Risk Management Framework provides an important reference for assessing AI risks at organizational level.

Technical AI penetration test output can therefore be related to broader AI risk management processes where required.

ISO/IEC 42001 and AI / LLM Security

ISO/IEC 42001, the Artificial Intelligence Management System, is a management system standard for organizations to handle their AI systems through controlled and manageable processes.

AI / LLM Penetration Tests can contribute to assessing the technical security controls of organizations that develop AI or use AI in critical business processes.

In the SecureSys approach, AI security tests can be handled where required together with the;

AI Governance + AI Risk Management + Secure AI Development + AI Security Testing

approach.

Not only the security of the application but the security processes across the AI lifecycle can then be assessed.

AI / LLM Penetration Testing Methodology

The SecureSys AI / LLM Penetration Testing process is carried out with a controlled and repeatable methodology.

1. Scope and Architecture Analysis

The architecture and attack surface of the AI system to be tested are determined.

2. Threat Modeling

Threat scenarios are built for the model, application, RAG, Agent, API, data and integration layers.

3. Attack Surface Mapping

The AI components reachable from outside or through an authorized user are mapped.

4. Automated Security Tests

Broad-scope tests are carried out using suitable security tools and automation mechanisms.

5. Manual AI / LLM Pentest

Prompt Injection, Jailbreak, RAG manipulation, Agent attacks and business logic scenarios are assessed manually by the expert security team.

6. Exploitation and Impact Analysis

The potential impact of the vulnerabilities identified on the real system is assessed in a controlled way.

7. Risk Rating

Findings are classified by technical impact, likelihood of occurrence and organizational risk.

8. Reporting

Actionable security output is prepared for technical teams and management.

9. Remediation and Retest

A retest can be carried out once the security vulnerabilities identified have been resolved.

AI Red Team

On critical AI systems, AI Red Team work can be carried out beyond a standard penetration test.

The aim in AI Red Team work is not simply to check known vulnerabilities but to push the security boundaries of the system with a real attacker's approach.

Within this scope, several attack techniques can be chained to build realistic attack scenarios such as;

Prompt Injection → RAG Manipulation → Agent Hijacking → Tool Abuse → Unauthorized Data Access

and similar chains.

AI / LLM Penetration Test Reporting

The findings identified at the end of the test are reported in a clear and actionable form.

Within the report, the;

• executive summary,
• test scope,
• methodology used,
• attack surface,
• vulnerabilities identified,
• risk levels,
• attack scenarios,
• technical evidence,
• possible business impact,
• remediation recommendations

can be presented.

The output then becomes a security document usable not only by the security team but by the software, DevSecOps, AI development, risk and management teams.

Who Should Have AI / LLM Penetration Testing Done?

AI / LLM security tests matter particularly for organizations that;

• develop Generative AI applications,
• use a corporate chatbot,
• use a RAG architecture,
• develop AI Agents,
• build LLM API integrations,
• use AI in customer services,
• integrate AI into financial processes,
• connect sensitive corporate data to LLM systems,
• use proprietary or fine-tuned models,
• develop MCP-based integrations

and similar cases.

Why SecureSys AI / LLM Penetration Testing?

AI security is not merely a prompt security problem.

Real security requires the data the model can reach, the tools it can run, the privileges it holds and the impact it could create inside the organization when manipulated to be assessed together.

The SecureSys AI / LLM Penetration Testing approach therefore combines classic pentest methodologies with new-generation AI security techniques.

The assessment;

LLM + RAG + Vector Database + AI Agent + MCP + API + IAM + Tool Calling + Data Security

can be built specifically for the project to cover these layers.

Do Not Merely Run Your AI — Test Its Security

Generative AI and Agentic AI systems are integrating ever more closely with organizations' most critical data and processes.

As that integration grows, so does the attack surface of AI systems.

From Prompt Injection to RAG attacks, from Agent Hijacking to MCP and Tool Abuse risks, assess your AI infrastructure from an attacker's perspective.

Measure the security level of your AI systems against real attacks with SecureSys AI / LLM Penetration Testing.

Request a quote from SecureSys for AI / LLM Penetration Testing.

Open Source and Commercial AI / LLM Penetration Testing Tools

The AI-powered penetration testing and LLM security testing ecosystem is developing quickly. Different approaches, from open source projects to corporate security platforms, support security teams in their AI Red Team, LLM Pentest, Prompt Injection, Jailbreak, RAG security and automated attack simulation work.

But it is not correct to treat AI-powered pentest tools as fully autonomous systems replacing an experienced penetration testing specialist. These technologies should be positioned as tools that increase the testing capacity of security specialists, automate certain attack scenarios and help assess broader attack surfaces.

Open Source AI Pentest Tools

Open source solutions can offer significant advantages particularly for research, laboratory work, security R&D processes and customized test scenarios.

Open source projects such as PentestGPT, Garak and similar tools can give security teams the ability to examine the test process, modify the source code and develop security scenarios customized to the organization's needs.

The main advantages of the open source approach:

• Source code transparency
• Customizable test scenarios
• A structure suited to security research
• The ability to develop organization-specific integrations
• Reduced licensing costs
• Integration into DevSecOps and security automation processes
• Flexibility in AI Security research

That said, deploying open source tools, configuring them securely, updating them and making them operationally sustainable is mostly the responsibility of the user organization.

Many tools also assume the user knows the core penetration testing methodologies and attack techniques.

Open source AI Pentest tools should therefore be treated as technologies supporting expertise, not alternatives to it.

Commercial AI Pentest and Autonomous Security Platforms

Commercial platforms generally focus on providing central management, corporate support, continuous updates, reporting and operational ease of use.

Platforms such as Horizon3.ai, XBOW and similar tools can be used with different approaches in automated or autonomous security testing, attack surface validation and penetration testing processes.

Platforms of this kind can be evaluated particularly by organizations wanting to carry out continuous security validation and reduce the manual operational load.

But when choosing a commercial AI Pentest platform, the level of automation should not be the only consideration.

The platform's;

• ability to verify real vulnerabilities,
• false positive rate,
• ability to produce attack evidence,
• integration into the organization's infrastructure,
• data security,
• how it uses LLM and AI models,
• reporting capability,
• authorization mechanisms,
• auditability

should also be assessed.

How Should AI Pentest Tools Be Positioned?

In general, open source and commercial solutions do not have to be direct alternatives to one another.

Open source tools can be more suitable for research, learning, security R&D work and targeted test scenarios; manageable commercial platforms for continuous security validation and corporate operations.

In more advanced security programs, the two approaches can be used together:

Manual Pentest + AI-Assisted Pentest + Automated Security Validation + AI Red Team

This hybrid model combines the speed and scalability automation provides with the attacker's perspective of experienced security specialists.

In the SecureSys approach, AI-assisted tools are handled not as a technology replacing classic penetration testing expertise but as a security layer helping to analyze broader attack surfaces faster and to strengthen manual testing.