06
5–10 working days

AI & LLM Security Assessment

Generative AI introduces a threat model traditional application security doesn't cover. We test for it directly.

Overview

New capability, new attack surface

As businesses adopt generative AI, securing the large language models behind it becomes critical. CyberMindX provides end-to-end security audits that test how your AI features hold up against prompt injection, adversarial inputs, and unauthorized data extraction — attack classes that don't fit neatly into traditional web application testing.

We test the full pipeline: the prompts and system instructions your application relies on, the API layer wrapping the model, and the guardrails meant to keep the model on-task. The goal is to find where your AI feature can be manipulated into leaking data, bypassing business rules, or behaving outside its intended scope, before an attacker does.

Methodology

How we run an AI security engagement

01

Scope & Threat Model

Map what the AI feature can access — data sources, tools, downstream actions — and what an attacker would want from it.

02

Prompt & Guardrail Recon

Probe system prompts, instructions, and safety guardrails to understand how the model is meant to behave and where it might deviate.

03

Adversarial Testing

Prompt injection, jailbreak attempts, and adversarial inputs designed to bypass guardrails or extract unintended information.

04

API & Infrastructure Review

Rate limiting, authentication, and the surrounding application layer that wraps the model — often more exploitable than the model itself.

05

Reporting & Free Retest

Findings mapped to a practical hardening guide for prompts, guardrails, and infrastructure, plus a free retest.

What We Cover

Scope of testing

  • Prompt injection and jailbreaking resistance
  • LLM API security and rate-limiting review
  • Training-data and retrieval-context privacy testing
  • AI model robustness and adversarial-input testing
  • Tool-use and function-calling boundary testing
  • Output filtering and data-leakage review
  • Third-party model and vector-store integration review
Common Vulnerability Classes

What we find most often

Direct & indirect prompt injection

Instructions hidden in user input or in third-party content the model reads can override the intended system prompt and hijack its behaviour.

Guardrail bypass via reframing

Safety instructions phrased as absolute rules are frequently bypassed through role-play framing, encoding tricks, or multi-turn manipulation.

Retrieval-context data leakage

Retrieval-augmented systems that don't enforce per-user access control on retrieved documents can leak data across tenants or user roles.

Unrestricted tool-calling

Models given access to internal tools or APIs without proper boundary checks can be manipulated into taking actions outside their intended scope.

Industries Served

Sector-aware testing

Enterprise IT & SaaS Media & Entertainment Healthcare Financial Services Engineering & Manufacturing Government & Public Sector
Deliverables

What you get

01 · Adversarial Test Report

Documented prompts and techniques that successfully bypassed guardrails.

02 · Hardening Guide

Concrete recommendations for prompts, guardrails, and API-layer controls.

03 · Free Retest

Verification pass once mitigations are in place.

Typical Duration

5–10 working days, depending on the number of AI features in scope.

What We Need From You

API or application access, system prompts if available, and a description of intended behaviour.

Pricing & Retest

[Add your starting price] · free retest included

FAQ

Buyer questions, answered honestly

Which AI providers and models do you test?

The methodology is model-agnostic — we test your application layer regardless of which foundation model or provider it's built on.

Do you need access to model weights or training data?

No — testing works against the deployed application and API, the same way an external attacker would interact with it.

Is this different from a standard web or API pentest?

It's complementary. AI-specific testing covers prompt-level and model-behaviour risks that traditional web and API testing doesn't examine.

Do you test retrieval-augmented generation (RAG) systems?

Yes — including per-user access control on retrieved documents and cross-tenant data isolation.

Do you provide a free retest?

Yes, once mitigations are implemented, at no additional cost.

Ready to scope this engagement?

We'll align on goals, rules of engagement, and timeline within one working day.

Schedule Audit