Penetration Testing of LLM Integrations and AI

THEY TRUST US

What is AI/LLM integrations penetration testing and why is it important?

AI and LLM integrations penetration testing verifies the resilience of applications to prompt injection, jailbreaks, data leaks, and authorization bypass. We focus on models, the RAG pipeline, agents, and security guardrails.

The result is a prioritized report with recommendations that help minimize risks associated with deploying AI to production.

Experience

Hands-on experience with AI security and LLM integration testing.

Transparency

Clear test goals and ongoing communication throughout the project.

Collaboration

Close coordination with your teams and clear deliverables.

Professionalism

Ethical approach, thorough documentation, and safe procedures.

Testing process

How AI and LLM integrations penetration testing works

We rely on the OWASP Top 10 for LLM methodology and combine threat modeling with practical testing.

Workshop and scope definition

We map architecture, data, and integration points.

Prompt, API, and integration testing

We evaluate inputs, authorization, and security controls.

Adversarial scenario simulation

We verify resilience to jailbreaks, data leakage, and abuse cases.

Report, recommendations, and retest

We deliver PoCs, mitigations, and verify fixes.

Scope

What we test in AI and LLM integrations

Coverage includes models, data flows, tools, and security controls.

LLM integrations

OpenAI, Azure OpenAI, Anthropic, Mistral, and local models.

RAG pipeline

Extraction, indexing, retrieval, and vector databases.

AI agents and tools

Tool use, function calling, plugins, and workflow orchestration.

APIs and authentication

OAuth2/OIDC, API keys, rate limiting, and webhooks.

Prompts and guardrails

System instructions, filters, moderation, and policy rules.

Monitoring and audit

Logging, alerting, and abuse detection.

Service comparison

AI/LLM penetration testing vs. classic application penetration testing

AI tests address specific threats that standard application pentests do not cover.

Aspect	AI/LLM penetration test	Classic application penetration test
Focus	Prompts, agents, RAG, and model logic.	Web, mobile, and backend layers.
Typical threats	Prompt injection, jailbreaks, data leakage.	SQLi, XSS, CSRF, auth bypass.
Methodology	Threat modeling + adversarial scenarios.	OWASP testing and standard exploits.
Output	AI-specific mitigation and retest.	Standard vulnerability report.

Need help choosing? Contact us.

TESTIMONIALS

What Our Clients Say About Us

Ultima Payments

"The decision to collaborate with Haxoris for the penetration testing of our web application was an excellent choice. Thanks to their professional approach, thorough testing, and outstanding communication, we were able to identify and eliminate multiple vulnerabilities. Their detailed final report provided us with a clear overview of our application's security status and specific recommendations for its protection. I can wholeheartedly recommend Haxoris to any company - their services are of a high professional standard, and the results exceeded our expectations."

Digital Systems

"Professional services, client-oriented approach, we would order again :)"

Amerge

"The team has approached penetration testing professionally and impartially, while keeping an open line with our DevOps, frontend and backend developers to ensure proper isolation of production environments. The level of testing and subject-matter expertise was impressive, even compared to other world-class companies in the industry we've worked with. The depth of vulnerability testing and the findings clearly outlined our strengths and areas for improvement, helping us increase our security standards even further."

Piano

"We chose Haxoris to perform penetration testing of our web applications in accordance with ISO 27001 and PCI DSS standards. Their approach was focused on our business needs, and we also appreciate their client-oriented attitude and flexibility."

Frequently asked questions (FAQ)

01 What is an LLM integrations penetration test?

It is security testing of integrations with LLMs, agents, and RAG that verifies resilience to prompt injection, jailbreaks, data leakage, and tool abuse.

02 Which threats do AI integration penetration tests focus on?

Prompt injection, jailbreaks, prompt leakage, data exfiltration, authorization bypass via agents, data poisoning in RAG, and DoS/denial-of-wallet.

03 Which platforms do you test for LLM penetration testing?

OpenAI and Azure OpenAI, Anthropic, Google Vertex AI, Mistral, Llama, and local models; frameworks LangChain/LlamaIndex, RAG, and vector databases.

04 What deliverables do we receive and how does the retest work?

A technical report with evidence and recommendations, an executive summary, and after remediation we perform a retest that confirms removal of critical risks.

Let's test the security of your AI application or LLM integration in your product

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