AI Security

LLM Penetration Testing

Test your LLM applications for prompt injection, data leakage and misconfiguration

Overview

Find How Your AI Can Be Misused

LLM applications add new ways to leak data and misuse systems: prompt injection, exposure of training data, and misconfigurations that reveal sensitive information. Our LLM penetration tests find these weaknesses and give you fixes your team can apply, along with evidence for the regulations you have to meet.

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cyphlon / approach 3 steps

01 Scoping & Onboarding
02 Pentesting
03 Reporting

Approach

Our Three-Step LLM Pentest Process

Step 01

Scoping & Onboarding

We agree security goals and priority areas with you, and set up the access the test needs.

Step 02

Pentesting

Our testers run real attack techniques against your AI systems, from prompt injection to training data leaks.

Step 03

Reporting

A technical report with remediation steps for your engineers, and an executive summary for decision-makers.

What you receive

Findings on prompt injection, data leakage and misconfiguration
A technical report with remediation steps
An executive summary for decision-makers

Common questions

How is this different from LLM Security?

A pentest attacks the running system to show what can be exploited. LLM Security reviews how the system is designed and deployed, and helps you put controls in place.

What do you test for?

Prompt injection, leaks of training or other sensitive data, and misconfigurations that expose information, using the techniques real attackers use against AI systems.

Benefits

How LLM Pentests Help

Weaknesses Found Before Launch

Fix vulnerabilities in your LLM applications before they are exploited.

Compliance Evidence

Show how your AI systems handle personal data under rules such as GDPR and HIPAA, with findings you can hand to auditors.

Customer Trust

Show customers and partners that your AI features have been independently tested.

Fix Early

Fixing an AI security issue before launch costs less than dealing with a data leak afterwards.

Why us

Why Cyphlon?

AI-Specific Testing

We test for AI-specific issues such as prompt injection, training data exposure and adversarial inputs. Each finding comes with a fix your team can apply.

Realistic Attacks

We replicate current attack techniques to show how your models behave when someone tries to abuse them.

Reports for Both Audiences

Technical guidance for your engineers and plain-language findings for decision-makers.

Informed by Our Research

We publish research on LLM agents in offensive security and apply what we learn to how we test.