AI and LLM Penetration Testing

AI Penetration Testing Services for LLMs and Agents

Expert-led AI penetration testing for LLM applications, RAG pipelines, and agents. Assess prompt injection, data leakage, unsafe output handling, excessive agency, tool use, and chained attacks across the surrounding application.

Brain

What every AI and LLM pentest includes

  • OWASP LLM risk coverage
  • Prompt injection and jailbreak testing
  • System prompt and sensitive data exposure
  • Agent permission and tool use abuse
  • RAG retrieval and poisoning risks
  • Direct access to the researchers on your engagement
  • Fix validation when included in scope

What is AI penetration testing?

AI penetration testing is a controlled assessment of AI systems and the applications around them. Blaze tests prompt handling, retrieval pipelines, data exposure, agents, tool permissions, output handling, APIs, and identity controls to find exploitable paths and explain how to reduce the risk.

400+

organizations trust Blaze worldwide

OWASP + MITRE + NIST

AI security guidance used where relevant

Expert-led. System-aware.

Test the AI system and the application around it

Researchers assess prompts, model behavior, RAG, agents, tools, APIs, identities, and data flows. Every finding is reproduced and tied to a practical remediation path.

Why Blaze

Why choose Blaze for AI penetration testing services?

Combine offensive-security depth with practical testing of AI systems, LLMs, RAG, agents, tool use, and the applications around them.

Stack

AI-native expertise

Test prompt handling, RAG, model behavior, and agent workflows with adversarial techniques—not generic scanners.

Code Block

Full-stack coverage

Assess the AI layer and surrounding web, API, identity, and data flows.

Lightning

Actionable remediation

Receive practical guidance for guardrails, permissions, output handling, and safer AI architecture.

Framework & Methodology

AI security guidance

Testing can reference OWASP guidance, MITRE ATLAS, and NIST AI RMF where relevant to the system and scope.

OWASP Top 10

Common LLM application risks

AI testing

OWASP guidance for AI-system testing

Shield Check

MITRE ATLAS

Adversarial tactics and techniques for AI systems

NIST AI RMF

AI risk-management context

PTES

Penetration Testing Execution Standard

OWASP ASVS

Controls in the surrounding application

Compliance support

Relevant findings mapped to applicable requirements

Meet our experts

Expert-led AI penetration testing services

Named security researchers run the engagement and stay available throughout testing and remediation.

MedalNamed

expert team for each engagement

Seal CheckDirect

access throughout testing

BugManual

validation of every finding

Rocket LaunchClear

remediation guidance for engineers

Our Team Holds Industry-Leading Certifications

Research contributions: Our testers have spoken at BlackHat, DEF CON, and BSides conferences worldwide.

What AI penetration testing services cover

AI-specific attack paths that conventional application testing may not cover.

In scope

Prompt injection

Direct and indirect prompt injection in user and retrieved content.

In scope

Data leakage

Exposure of system prompts, confidential data, or retrieved content.

In scope

Guardrail bypass

Bypassing safety controls, context handling, or output validation.

In scope

Unsafe output handling

Untrusted model output reaching browsers, APIs, tools, or interpreters.

In scope

Excessive agency

Over-privileged agents taking unintended or unauthorized actions.

In scope

Model denial of service

Inputs causing excessive resource use or service degradation.

In scope

Retrieval manipulation

Manipulating retrieved content to influence model behavior.

In scope

Supply chain

Risks in models, tools, plugins, and dependencies.

Our Process

Our testing methodology

A systematic approach combining adversarial ML techniques with traditional offensive security.

01

Architecture review

Map AI/LLM integration points, data flows, model access, trust boundaries

02

Prompt injection testing

Direct and indirect prompt injection across all input vectors

03

Data leakage assessment

Assess system prompts, confidential data, PII, and connected data.

04

Guardrail bypass testing

Probe content filters, safety controls, and output handling

05

Agent and tool security

Test tool permissions, action authorization, and multi-step attacks.

06

Reporting and remediation

Findings with reproduction steps, risk ratings, and clear fixes

We know your AI stack

Test models, RAG pipelines, copilots, and agents in the application’s security model—not as a generic benchmark.

Warning

Illustrative risk

Indirect prompt injection in retrieved content can expose system instructions or trigger unsafe tool use.

Warning

Illustrative risk

An over-privileged agent can be manipulated into taking unintended actions when authorization controls are weak.

Related services

Web App

Web application penetration testing

Secure the web app wrapping your AI features.

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Database

API penetration testing

Test the APIs behind your AI application.

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Cloud

Cloud penetration testing

Secure the cloud infrastructure hosting your models.

Learn More

Frequently asked questions

Answers about AI application scope, models, RAG, agents, tools, safety limits, pricing, and remediation.

Pricing depends on the models, applications, RAG pipelines, agents, tools, data sources, user roles, environments, and testing depth. Share the architecture and objectives for a fixed scope and quote.
Blaze tests chatbots, copilots, RAG applications, content-generation systems, AI search, and agents that call external tools. Scope can include hosted or self-managed models and the surrounding application.
AI testing covers prompt injection, retrieval manipulation, data leakage, unsafe output handling, and excessive agency. Web testing covers the surrounding application; weaknesses can chain across both layers.
Yes. Coverage can reference the OWASP Top 10 for LLM Applications alongside MITRE ATLAS and NIST AI RMF, adapted to the actual system.
Yes. Blaze assesses tool permissions, action authorization, context handling, data access, and multi-step paths that could trigger unintended actions.
Testing follows agreed environments, accounts, rate limits, sensitive actions, data-handling rules, and safety limits. Potentially disruptive actions require explicit approval.
Not always. A pentest focuses on exploitable security risks in a defined system. AI red teaming may also examine misuse, safety, resilience, or broader adversarial behavior.
Not always. Black-box testing can assess exposed behavior; architecture, prompt, configuration, or source access can support deeper testing when appropriate.
Retest after fixes or material changes to models, prompts, retrieval data, integrations, tool permissions, or guardrails. The cadence should reflect the system's risk and rate of change.

Ready to test your AI application?

Talk to an expert about your models, RAG, agents, tools, data flows, timeline, and required coverage.