Can you test systems that use third-party LLMs (OpenAI, Azure OpenAI, Anthropic, etc.)?

Yes. We test your application layer, prompts, retrieval, tools, and agents around those providers, and we respect their rate limits and terms.

Will testing impact production?

We prefer a staging or sandbox environment. Production testing is possible with written rules of engagement, allow-listed IPs, and non-destructive methods.

What access do you need to start?

Test accounts, test data, API endpoints, and any documentation on prompts, tools, RAG, and agents. Optional but helpful: read-only code or config access, and a diagram of your architecture.

How long does an engagement take?

Typical scope is 2 - 4 weeks depending on complexity: number of apps, tools, data sources, tenants, and whether code access is provided.

What exactly do we get at the end?

AI & LLM pentest report with impact and clear repro steps; threat model tailored to your system; completed checklists (OWASP LLM Top 10, WSTG, MASTG); evidence (prompts, payloads, transcripts, and screenshots); retest results and, when Critical/High issues are fixed, a Letter of Attestation.

Do you include a retest?

Yes. We retest once after you implement fixes to confirm closure of Critical and High issues.

How do you handle sensitive data (PII/PHI)?

We minimize test data, use sanitized artifacts, and delete on request under an agreed policy. If regulated data is involved, we align with your controls.

Do you use autonomous agents in testing?

Yes, in addition to manual testing and optional code review. We run autonomous pentest agents (e.g., CAI) under strict boundaries to extend coverage without risking unsafe actions.

Can you attempt model extraction?

Only with explicit consent and agreed rate limits. Many clients choose to exclude it; when included, we run it safely and document the approach.

How do you test multi-tenant isolation in RAG?

We validate metadata and namespace isolation, attempt cross-tenant retrieval under controlled conditions, and verify application-layer filters.

How is pricing determined?

By scope and complexity: number of applications, tools, connectors, tenants, and environments; whether code review is included; and any optional modules (e.g., model extraction).

Can you work with our internal security and compliance teams?

Yes. We align on rules of engagement up front, provide regular updates, and map results to your internal processes to speed remediation.

SERVICE

AI & LLM Penetration Testing Service

Securing the next generation of AI applications

Beyond Standards

Extensive Reports

In-Depth Coverage

Talk to An Expert

AI & LLM Penetration Testing Overview

Book a discovery call

We don’t do green lights or paper reports.
We show what’s actually exploitable
and how to fix it.

For AI applications, we combine manual verification, source code analysis (when available), and runs of autonomous pentest agents such as CAI to increase coverage.

Industries We Protect

As AI becomes part of critical systems, our pentesting is built for high-risk industries, enterprise SaaS, and teams launching AI copilots, agents, and RAG features who need security they can rely on

Biotech, FinTech, and Digital Health

SaaS and enterprise platforms

Teams rolling out AI copilots,
agents, and RAG features

What We Test

We test the parts of your AI stack that break in the real world.

LLM applications

- Conversation flows, safety controls, abuse handling

- Authentication, sessions, rate limiting

RAG pipelines

- Retriever setup, chunking and metadata hygiene
- Vector database boundaries and query isolation

Tools, plug-ins, and APIs

- Keys and secrets in context

- Unsafe action invocation and lateral movement

AI agents (planner, memory, tools)

- Permission misuse and goal hijacking
- Task queue manipulation and cross-agent escalation

Model interfaces

- Prompt injection and jailbreaks

- Model extraction via crafted queries (when in scope)

Cloud and secrets surface

- Buckets, logs, and telemetry that leak data
- Prompt and context redaction for PII/PHI where relevant

Our Approach

We build trust in your technology. The goal is simple: reduce unknown vulnerabilities, protect valuable data, and keep your product reliable and safe.

Checklist Assurance

Recognizing the possibility of human error, we counteract it by providing detailed AI security checklists covering OWASP LLM Top 10, MITRE ATLAS techniques, and AI-specific attack vectors.

Comprehensive Coverage

Each detection method excels at identifying particular types of vulnerabilities. We combine manual testing, targeted code review, and autonomous pentest agents (CAI, XBow) to cover the full AI and LLM attack surface.

Personalized Testing

Before testing, we conduct AI-specific threat modeling to pinpoint risks in your model integration, data flows, and agent or RAG pipelines. This ensures scope is realistic and high-impact.

Developer DNA

Code-informed testing stands out as the prime risk-reduction strategy, and we're masters at it. Many of our team have a developer background, enabling deeper analysis of AI workflows and custom integrations.

Business-Oriented

Guided by your business context and risk management priorities, we provide AI security solutions tailored to protect your data, reputation, and compliance posture.

Transparent

Scope decomposition, regular updates, and a dedicated manager keep you fully informed throughout the AI pentest process.

Unbiased

By having at least two security engineers on each AI pentest project, we ensure findings are reviewed from multiple perspectives, reducing false positives and missed issues.

Seamless Integration

Our dedicated manager coordinates with your engineering teams, making the AI pentest process feel like an extension of your development workflow.

Learn More About How Generative AI Can Be Used in Cybersecurity

GenAI in Security

Methodologies

We don’t just name-drop frameworks — we apply them in every AI pentest. Our work is guided by proven security standards and adapted to the unique risks of AI systems. Every engagement ends with a clear checklist and threat model so you know exactly what was tested and why it matters.

For AI and LLM systems, our process includes:

OWASP LLM Top 10 — full checklist coverage for AI-specific vulnerabilities

OWASP AI Testing Guide — comprehensive testing framework for AI system security

NIST AI RMF — aligning outcomes to recognized AI risk management principles

PTES — comprehensive pentest execution framework

OWASP ASVS & WSTG — for supporting application security layers in AI stacks

How It Works

Cybersecurity is complex. Your path to enterprise readiness doesn’t have to be.

Intro & Planning

Schedule a call, and we will:

Understand your AI application, architecture, and business context
Define the scope: LLM apps, RAG pipelines, agents, tools, and integrations
Agree on testing rules and objectives
Provide a tailored proposal and estimate

Rules of Engagement & Data Handling

Operate under strict & transparent controls:

Written authorization and allow-listed IPs
Staging or sandbox preferred; production testing only under strict controls
Non-destructive methods with clear boundaries for tool execution
Minimized test data, sanitized artifacts, and deletion on request

Security Testing

Our security engineers will:

Map attack surfaces across prompts, data flows, and model integrations
Test for AI-specific threats: prompt injection, jailbreaks, RAG poisoning, tool misuse, agent hijacking, and model extraction (if in scope)
Review source code where provided
Run autonomous pentest agents (CAI, XBow) alongside manual testing to maximize coverage
Document all tests in a detailed checklist

Reporting & Insights

Upon completion, our team will:

Deliver a clear report on each finding, its risk, and real-world impact
Provide evidence: prompts, payloads, transcripts, and screenshots
Walk your team through results for full understanding
Give actionable remediation steps your engineers can apply immediately

Support & Retesting

Post-assessment, we're still with you:

Retest after fixes to confirm all critical and high-risk issues are resolved
Issue a Letter of Attestation once verification is complete
If any questions come up, our team will be there to help

From Findings to Peace of Mind

You get a report that engineers can act on and leaders can trust.

AI & LLM Penetration Testing Report

A dual-focused document combining an executive summary for decision-makers with in-depth technical findings for your engineers. Includes real-world impact, reproduction steps, and prioritized fixes.

Threat Model Document

A structured representation of the threat landscape tailored to your environment, highlighting potential threats and their prioritized mitigation

Testing Checklist

A comprehensive list enumerating every test we conducted, ensuring transparency and thoroughness in our approach.

Letter of Attestation

A formal statement confirming all critical and high-risk issues have been remediated and verified, providing independent validation of your system’s security posture.

Case Studies

Pentesting for AI-HealthTech Compliance

Learn more →

Enterprise-Grade Security in Finance & AI

Learn more →

Representative Findings (anonymized)

Real examples of issues we’ve identified and helped clients fix.

Each one shows the kinds of vulnerabilities that can slip through without focused AI/LLM security testing.

/01

Agent Tool Misuse

Unauthorized Data Access

What we tested:

A support copilot with search and file retrieval tools.

What we did:

Steered the agent into triggering a high-permission tool without checks.

What we found:

Access to invoices and configuration files containing environment variables.

Why it mattered:

Allowed sensitive data exfiltration via “helpful” tool misuse.

Fix implemented:

Least privilege for tools
Pre-execution guardrails
Output sanitization
Abuse simulations in testing

/02

RAG Namespace Escape

Cross-Tenant Data Leakage

What we tested:

Multi-tenant knowledge assistant using a shared vector database.

What we did:

Crafted queries exploiting missing tenant filters.

What we found:

Snippets from another tenant’s documents in responses.

Why it mattered:

Violated data isolation, risking regulatory breaches and trust.

Fix implemented:

Strict metadata and namespace isolation
Per-tenant database collections
Application-layer filter enforcement
Filter validation in CI/CD

Why Teams Choose Sekurno

Our clients trust us because we go beyond surface checks — we focus on finding what’s truly exploitable and delivering solutions that matter.

Specialized in AI Security

We test the parts that make AI systems fail in production: prompts, RAG pipelines, tools, agents, and data boundaries.

Senior Engineer Expertise

Every engagement is led by seasoned security engineers with real-world experience.

Actionable, Not Noisy

Reports are clear, evidence-based, and prioritized so your team can act fast.

End-to-End Partnership

From testing to retesting, we stay engaged to verify fixes and ensure closure.

100+

Critical Issues Found

$100M+

Saved for our Clients

5/5

Client Satisfaction Rate

90%

Clients return

What Our Clients Say

Sekurno delivered everything on time, discovered unexpected vulnerabilities, and increased our peace of mind regarding security. Their clear documentation ensured transparency, and they were excellent at project management. The team was independent, efficient, and highly knowledgeable.

Rodrigo Azevedo, Software Developer, Coreway

Our Certifications

Frequently Asked Questions

Still have questions?

Next Steps

To strengthen your security posture, contact Sekurno for a security consultation and learn how proactive cybersecurity measures can protect your business.

Contact us

Cybersecurity Beyond Compliance

AI & LLM Penetration Testing Service

AI & LLM Penetration Testing Overview

We don’t do green lights or paper reports. We show what’s actually exploitable and how to fix it.​​

Industries We Protect

Biotech, FinTech, and Digital Health

SaaS and enterprise platforms

Teams rolling out AI copilots, agents, and RAG features

What We Test

LLM applications

RAG pipelines

Tools, plug-ins, and APIs

AI agents (planner, memory, tools)

Model interfaces

Cloud and secrets surface

Our Approach

Checklist Assurance

Comprehensive Coverage

Personalized Testing

Developer DNA

Business-Oriented

Transparent

Unbiased

Seamless Integration

Learn More About How Generative AI Can Be Used in Cybersecurity

Methodologies

How It Works

Intro & Planning

Rules of Engagement & Data Handling

Security Testing

Reporting & Insights

Support & Retesting

From Findings to Peace of Mind

AI & LLM Penetration Testing Report

Threat Model Document

Testing Checklist

Letter of Attestation

Case Studies

Pentesting for AI-HealthTech Compliance

Enterprise-Grade Security in Finance & AI

Representative Findings (anonymized)

/01

Agent Tool Misuse

Unauthorized Data Access

What we tested:

What we did:

What we found:

Why it mattered:

Fix implemented:

/02

RAG Namespace Escape

Cross-Tenant Data Leakage

What we tested:

What we did:

What we found:

Why it mattered:

Fix implemented:

Why Teams Choose Sekurno

100+

$100M+

5/5

90%

What Our Clients Say

Rodrigo Azevedo, Software Developer, Coreway

Our Certifications

Can you test systems that use third-party LLMs (OpenAI, Azure OpenAI, Anthropic, etc.)?

Will testing impact production?

What access do you need to start?

How long does an engagement take?

What exactly do we get at the end?

Do you include a retest?

How do you handle sensitive data (PII/PHI)?

Do you use autonomous agents in testing?

Can you attempt model extraction?

How do you test multi-tenant isolation in RAG?

How is pricing determined?

Can you work with our internal security and compliance teams?

Next Steps

Cybersecurity Beyond Compliance

Security Services

We don’t do green lights or paper reports.
We show what’s actually exploitable
and how to fix it.

Teams rolling out AI copilots,
agents, and RAG features