AI Security/AI & LLM Penetration Testing

AI & LLM Penetration Testing

AI & LLM penetration testing that proves what is exploitable

Hands-on testing of your chatbots, RAG pipelines, model APIs and AI features for prompt injection, data leakage, broken authorization and unsafe output. Every finding comes with a reproducible proof of concept and a fix your engineers can ship.

Direct and indirect prompt injection, jailbreaks and system prompt extraction
RAG, vector store and cross-tenant data leakage
Every OWASP Top 10 for LLM Applications (2025) risk that applies to your architecture
Model layer and the web app and APIs around it, in one engagement

Get my AI pentest quote See how we test ↓

The credentials behind every engagement

2026 CyberSecurity Breakthrough Awards winner

Penetration Testing Solution of the Year2026 CyberSecurity Breakthrough Awards

Trusted by Fortune 100, financial services, insurance and SaaS security teams

The short answer

What is AI penetration testing?

AI penetration testing (AI pentesting) is a hands-on security test of an application that uses a large language model or other AI component. Testers attack the prompts, the data the model can reach, its APIs and the tools it can call, and prove each exploitable weakness with a reproducible proof of concept.

An LLM penetration test covers the model layer that a standard web pentest leaves out, together with the web app and APIs around it. Findings are mapped to the OWASP Top 10 for LLM Applications 2025 and MITRE ATLAS, so your engineers, auditors and enterprise customers read the same evidence.

You need an AI pentest if

You are launching a chatbot, copilot or RAG search feature
An enterprise customer or auditor asked for an AI pentest report
Your AI can read internal documents or customer data
You changed models, prompts or retrieval sources since the last test

Where we attack

Ten OWASP LLM risks. We test every one that applies to you.

Prompt injection gets the headlines, but most damage happens where the model meets your data, tools and users. This is where each OWASP LLM risk shows up in a typical architecture.

Architecture diagram of an LLM application: user, web app and API, orchestrator with system prompt and guardrails, model API, RAG vector store, tools and downstream systems, and the training pipeline. Ten numbered markers place each OWASP Top 10 for LLM Applications 2025 risk where it is tested. — **Source:** OWASP Top 10 for LLM Applications 2025 (OWASP GenAI Security Project); ioSENTRIX test methodology. Your architecture and scope will differ.

What we test

Four layers, tested by hand

Scope follows your architecture. Most AI penetration tests cover these four areas.

Layer 1

Prompt & model

Attacks on how the model interprets instructions and untrusted input.

Direct & indirect prompt injection
Jailbreaks & guardrail bypass
System prompt extraction
Insecure output handling

Layer 2

RAG & data

What the model can retrieve, and who it will show it to.

Cross-user & cross-tenant leakage
Vector store access controls
Document poisoning paths
Sensitive data in responses

Layer 3

APIs, plugins & integrations

The application around the model, tested like any other attack surface.

Model API authN & authZ
Rate limits & cost abuse
Plugin & tool abuse
Excessive agency

Layer 4

Training & model pipeline

For teams that build or fine-tune models.

Training data poisoning paths
Model & artifact access
Pipeline secrets
Model & library supply chain

Methodology

How we test: example test cases

A sample of the manual test cases in a typical AI penetration test. Your scope decides the full list.

LLM01

Indirect prompt injection through retrieved content

We plant instructions in a document, page or ticket the system ingests, then check whether the model follows them when a different user asks a question.

LLM02 · LLM08

Cross-tenant retrieval

Signed in as one customer, we try to make the model retrieve and quote another customer’s documents from the vector store.

LLM07

System prompt extraction

We try to recover hidden instructions, business rules or credentials embedded in the system prompt.

LLM05

Model output reaching a sink

We craft input that makes the model emit markup, code or queries, and test whether the browser, database or downstream tool runs them.

LLM06

Tool and function abuse

We test whether the model can be talked into calling functions or APIs beyond what the signed-in user is allowed to do.

LLM10

Cost and resource abuse

We test rate limits and input sizes for requests that run up token spend or degrade the service.

Sample finding

What a finding looks like in our report

An illustrative example in our report format. Not client data.

Finding 01 · IllustrativeHigh

Instructions hidden in an uploaded PDF make the assistant reveal other users’ support tickets

OWASP LLM01 Prompt Injection · LLM02 Sensitive Information Disclosure

What we found

A user uploads a PDF containing hidden instructions. When another user later asks the support assistant a question, the retrieved PDF tells the model to search all tickets and include them in its answer. The assistant’s retrieval tool ran with service-level access, so it returned tickets the second user was never allowed to see.

Impact

Any user able to upload a document can read other customers’ support history.

How to fix it

Enforce document-level access control at retrieval time using the end user’s identity, not in the prompt. Treat retrieved text as untrusted data, and limit the tools the assistant can call while answering from retrieved content.

What you get

What you get from every AI pentest

Not a model scan. Proven AI attacks and fixes your engineers can act on.

Findings with proof, ranked by exploitability

Each finding — prompt injection, data leakage, broken authorization — has a severity, a reproducible proof of concept and remediation guidance.

Mapped to OWASP LLM Top 10 and MITRE ATLAS

A report your auditors and enterprise customers can read, with every finding mapped to the standards they ask about.

Walkthrough, then retesting

We walk your engineers through the findings, and retesting is available once fixes land.

ioSENTRIX sample penetration test report cover

See how we write findings before you talk to us

A redacted sample penetration test report (web application) — finding write-ups, proof-of-concept evidence and remediation. No form required.

Download the sample report (PDF) →

Verified reviews

What security leaders say about working with us

★★★★★

“Unlike other providers, Omair and his team truly dive deep into our infrastructure, delivering findings that go well beyond the surface level. Communication is direct and seamless.”

Jesus B.

Cybersecurity Manager, Banking

Verified G2 review ↗

★★★★★

“We were impressed with ioSENTRIX’s ability to independently validate the security of our customer-facing product.”

Zeehasham R.

Data Scientist, Security Operations · CNA

Verified Clutch review ↗

★★★★★

“The team at ioSENTRIX is professional and highly skilled at their craft.”

Awwab A.

SVP, Chief Information Security Officer

Verified G2 review ↗

How it works

How an AI penetration test works

Four steps, so you know what happens and what you get.

Scope and threat model

We walk through your architecture, data flows and trust boundaries, and agree what is in scope.

Week 1

Manual testing

Testers attack the model, its data and its integrations, mapped to the OWASP LLM Top 10 and MITRE ATLAS.

Most of the engagement

Report

Each finding has a severity, a reproducible proof of concept and remediation guidance.

At close

Remediation support

We walk your engineers through the findings, and retesting is available once fixes land.

After fixes

Reply within 1 business day → fixed quote in writing → engagements from 2 weeks → report, walkthrough and retest

How we compare

Not a prompt scanner. Not a web pentest with AI bolted on.

What an AI penetration test covers compared with the usual alternatives.

ioSENTRIX AI pentest

Automated LLM scanner

Standard web pentest

What is tested

Model, RAG, APIs, tools and the app around them

Known prompt patterns against the model endpoint

Web app and API; model layer out of scope

Indirect prompt injection

Tested through documents, pages and tool output

Rarely

Not tested

Authorization & data leakage

Tested by hand across users and tenants

Not tested

Web layer only

Evidence

Reproducible proof of concept and a fix

Pass/fail results to triage

Finding list

Standards

OWASP LLM Top 10 (2025), MITRE ATLAS

Varies by tool

OWASP Top 10 (web)

Get my AI pentest quote

Scope and price

What affects the scope and price of an AI pentest

AI penetration tests are priced on effort, not on the size of the model. These are the decisions that move the quote.

What moves the quote

01How many AI features, models and user roles are in scope
02Whether the AI can call tools, functions or agents
03The RAG data sources and how tenants are separated
04Whether the surrounding web app and APIs are tested in the same engagement
05Black-box testing, or access to prompts, architecture and code
06Retesting windows and the report format your customers expect

Read: How much does an AI penetration test cost? →

What we need from you to start

A test environment or staging tenant that mirrors production
Test accounts for each user role, across at least two tenants
Architecture notes: model provider, prompts, retrieval sources and tools
Rate-limit and spend guardrails we should respect
A technical contact for questions during testing

Get my AI pentest quote

Proof, not promises

AI systems we have already tested

A real engagement, described without the client’s name.

Case study

Fortune 500 financial services: AI/ML threat model across AWS and Azure

We threat-modeled the client’s AI estate — RAG pipelines, LLM orchestration layers and ML inference workflows — using STRIDE, NIST SP 800-30 and the NVIDIA AI Red Team framework.

Read the case study →

30+

AI components reviewed across AWS and Azure

threats identified and mapped

high-severity threats in RAG, orchestration and inference

Clients: Fortune 100, financial services, insurance and SaaS. Engagements: 2 weeks to 4 months, quoted on effort.

RAG systemsCustom LLM pipelines & trainingChatbotsOrganization-wide AI enablementAI features in SaaS productsMCP testing

Track record

Numbers you can check

Ratings you can verify on G2 and Clutch.

4.9 / 5

on G2 from 12 verified reviews

5.0 / 5

on Clutch, Premier Verified

2017

founded; CREST-accredited, ISO 27001 certified, SOC 2 Type 2 attested

Who you work with

Who does the testing

Offensive security engineers do the hands-on testing and write up the findings, so the person who found an issue explains it to your team.

Certified testers

Each tester holds one or more of OSCP, OSWE, OSCE, CISSP, CISA or AWS security certifications.

Accredited firm

ioSENTRIX is CREST-accredited for penetration testing, ISO/IEC 27001 certified and SOC 2 Type 2 attested.

Since 2017

Founded in 2017, with offensive security work for Fortune 100, financial services, insurance and SaaS companies.

Ready to see what your AI can be made to do?

A senior engineer replies within 1 business day · no sales pitch

Get my AI pentest quote

Standards

Frameworks we test and map to

◆OWASP Top 10 for LLM Applications◆OWASP Top 10 for Agentic Applications◆MITRE ATLAS◆NIST AI RMF◆ISO/IEC 42001◆STRIDE

FAQ

Frequently asked questions

How is it different from a regular web application pentest?

A web pentest covers the application and its APIs. An AI pentest adds the model layer: prompt injection, jailbreaks, retrieval of data the user should not see, and unsafe use of model output by the rest of the system. We test both layers in the same engagement.

Which OWASP LLM risks do you test?

All ten categories in the OWASP Top 10 for LLM Applications 2025: prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption, as they apply to your scope.

Is AI penetration testing the same as AI red teaming?

No. A penetration test aims to find as many exploitable weaknesses as possible within a scope. Red teaming pursues specific harmful outcomes the way an adversary would. See our AI red teaming services and our comparison of the two.

Do you test AI features inside our SaaS product?

Yes. Adding AI to SaaS products is one of the AI engagement types we run.

How long does it take, and how is it priced?

Engagements run from 2 weeks to 4 months depending on scope. Each is a fixed quote based on effort, bought with PTaaS credits or a subscription.

What should be in scope for an AI penetration test?

Scope should follow what the AI can reach and do: the model and its prompts, every data source it retrieves from, the tools and APIs it can call, and the web app and APIs around it. Read our guide to AI penetration test scope.

What are the best AI penetration testing tools?

Open-source tools such as NVIDIA garak, Microsoft PyRIT and promptfoo run large libraries of known attacks against a model, which is useful for coverage and regression testing. They do not understand your authorization model or business rules, so they miss the data-access and tool-abuse findings that matter most. A good AI penetration test uses tools for breadth and people for depth. See why an AI tool isn’t a pentest.

Resources

Go deeper before you scope a test

Practical guides from our testers on the questions buyers ask most.

Flat illustration of a dashed scope boundary holding prompt, data, tool and identity tiles, with one orange tile marked out of scope outside it

Guide · 12 min read

What Should Be in the Scope of an AI Penetration Test?

“Test the customer-facing AI assistant” is one sentence, and the whole engagement will be argued from it. The seven surfaces an AI penetration test…

Read the guide →

Flat illustration of a penetration testing quote with an orange price tag beside an AI agent connected to three tool nodes

Guide · 12 min read

How Much Does an AI Penetration Test Cost?

Two firms quote the same AI system and the numbers differ fourfold. Both are honest. The seven scope decisions that set the price of an AI…

Read the guide →

Guide · 7 min read

Prompt Injection Attacks Explained: Types, Examples & Defenses

Prompt Injection Testing explained: learn types, examples, attacks, and defenses for LLM security, including fuzzing, jailbreaks, and mitigation…

Read the guide →

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Ready when you are

Test your AI before an attacker does

Tell us what AI you have shipped — chatbots, RAG, model APIs or AI features — and we will scope a test and send a fixed quote based on effort.

A senior engineer replies within 1 business day · Fixed quote in writing before work starts · Your details are never shared