LLM Penetration Testing Guide: Scope, Cost & Providers

Most organisations securing AI applications are doing it wrong not because they're careless, but because they're applying web application security thinking to a fundamentally different attack surface.

A standard pentest doesn't test prompt injection. It doesn't test whether your RAG system leaks data across users. It doesn't test whether your chatbot's system prompt can be extracted, or whether your AI copilot can be manipulated into calling functions it shouldn't. Those vulnerabilities don't exist in traditional web applications they exist in yours, and most pentest providers aren't testing for them.

This is a guide for buyers: what LLM penetration testing should actually cover, what it costs when done properly, and the questions that separate providers doing real work from the ones who read the same blog posts you did. What LLM penetration testing actually covers, what it costs, and how to tell the real providers from the ones running jailbreak scripts and calling it a security assessment.

1. What LLM Penetration Testing Is
2. Who Needs an LLM Pentest
3. How LLM Testing Differs from Web App Pentesting
4. What LLM Penetration Testing Covers
5. The OWASP LLM Top 10 — What It Means for Scope
6. LLM Penetration Testing Cost in 2026
7. How to Evaluate and Choose a Provider
8. Get an LLM Pentest Scoping Call

What LLM Penetration Testing Is

LLM penetration testing is a security assessment of an application that uses a large language model as a component testing not just the surrounding web infrastructure, but the AI layer itself: how the model can be manipulated, what data it can be tricked into revealing, what actions it can be induced to take, and how the integration between the model and your application creates vulnerabilities that neither would have alone.

The target isn't the underlying model (GPT-4, Claude, Gemini, Llama you don't own those and can't fix them). The target is how your application uses it: the system prompt design, the input handling, the output validation, the context the model is given access to, and the trust boundaries between the model and the rest of your stack.

A skilled LLM tester approaches your application the way an adversarial user would probing system prompt boundaries, testing what context can be extracted, attempting to redirect the model's behaviour, and looking for paths from AI layer compromise to actual data exposure or functional abuse.

What LLM pentesting is not: Running automated jailbreak prompts from a list and calling it a test. The most dangerous vulnerabilities in real LLM applications aren't the ones that automated tools find they're the ones that require understanding your specific application logic, data architecture, and model integration to exploit.

Who Needs an LLM Pentest

If your application has any of the following, LLM-specific security testing is relevant:

Customer-facing AI chatbot or assistant — any natural language interface where users can provide arbitrary input to a model that has access to your data or backend systems.

Internal AI copilot — employee-facing tools that connect to internal knowledge bases, HR systems, code repositories, or business applications via an LLM layer.

RAG (Retrieval-Augmented Generation) systems — applications that retrieve documents or data to augment LLM responses. The retrieval layer, the document handling, and the context injection all create attack surfaces that standard pentesting misses.

LLM-powered API — any endpoint that accepts natural language input and processes it through a model before returning a response or triggering an action.

AI features in existing applications — chat, summarisation, recommendations, or classification features built into a broader product by calling an LLM API.

The compliance trigger: AI applications are increasingly in scope for SOC 2, ISO 27001, and HIPAA assessments — auditors want to see that the AI layer has been assessed alongside the rest of the application. If you're going through a compliance audit and your product has AI features, expect questions about how those features were tested.

How LLM Testing Differs from Web App Pentesting

For applications that have both a standard web interface and an LLM layer, both assessments are needed they cover different attack surfaces. The LLM pentest doesn't replace the web app pentest; it extends it.

What LLM Penetration Testing Covers

The attack surface of an LLM application has several distinct layers. A rigorous LLM pentest covers all of them not just the model interaction itself.

Prompt injection — direct and indirect. Direct prompt injection is when a user manipulates their input to override or subvert the model's system instructions getting the model to ignore its guidelines, reveal its system prompt, or behave in ways the developer didn't intend. Indirect prompt injection is more dangerous: malicious instructions embedded in documents, emails, or web pages that your application retrieves and feeds to the model as context. If your RAG system fetches external content, or processes user-uploaded documents, indirect injection is a real and underestimated risk.

System prompt confidentiality. Your system prompt likely contains proprietary business logic, persona instructions, internal tool descriptions, or sensitive operational context. Testing verifies whether that content can be extracted through adversarial prompting and whether the model's refusal mechanisms are robust enough to prevent it under sustained pressure.

Data exfiltration via the model layer. If your LLM has access to a knowledge base, user data, or document store, testing explores whether an attacker can craft inputs that cause the model to surface data from other users, internal systems, or restricted document sets it was given access to. This is a cross-tenant data exposure risk and one of the most commercially significant vulnerabilities in enterprise AI applications.

Excessive agency and function calling. Modern LLM applications use function calling or tool use to allow the model to take actions search databases, send emails, create records, call APIs. Testing evaluates whether those tool permissions are appropriately scoped, whether user input can cause the model to call functions it shouldn't, and what the blast radius of a compromised model action looks like.

Context window manipulation. Applications that include prior conversation history, retrieved documents, or user profile data in the model's context create opportunities for context poisoning manipulating what the model "knows" in a given session to influence its subsequent behaviour.

Trust boundary failures. The boundary between user-supplied input and trusted system context is where the most significant LLM vulnerabilities live. Testing focuses on whether the model correctly distinguishes instructions from the developer versus instructions from the user and whether an attacker can blur that distinction.

Output handling and downstream injection. What happens to the model's output? If it's rendered in a browser, does it create XSS risk? If it's used to construct database queries, does LLM-generated content create injection paths? The model output isn't just a text response in many applications, it's an input to the next system in the chain.

What we deliberately don't cover: We don't attempt to jailbreak the underlying model for its own sake. We're not testing whether GPT-4 can be made to say something offensive we're testing whether your application's specific integration and deployment creates exploitable security risk. Those are different questions with different answers.

The OWASP LLM Top 10 — What It Means for Scope

OWASP maintains an LLM-specific Top 10 the most widely referenced framework for understanding LLM application security risk. When providers say their LLM testing is "OWASP LLM aligned," here's what the categories mean for your scope decisions:

What this means for scope decisions: LLM01 (Prompt Injection), LLM02 (Insecure Output Handling), LLM06 (Sensitive Information Disclosure), LLM07 (Insecure Plugin Design), and LLM08 (Excessive Agency) are the categories relevant to almost every production LLM application. The others depend on your specific architecture whether you fine-tune, use plugins, call external tools, or serve multiple tenants.

LLM Penetration Testing Cost in 2026

LLM pentesting is newer than traditional web app testing, which means pricing is less standardised and varies more widely between providers. Here's what realistic engagements cost:

Be cautious of LLM "security assessments" quoted significantly below these ranges. At sub-$3,000 price points, what's being sold is automated prompt fuzzing running a library of known jailbreak attempts and returning a pass/fail. That's not a penetration test. The most damaging vulnerabilities in LLM applications are application-specific: they require a tester who understands your architecture, your data access patterns, and your intended model behaviour to identify.

How to Evaluate and Choose an LLM Pentest Provider

The LLM security market is young and noisy. Every provider who can run auto tools or write a few adversarial prompts is calling themselves an AI security firm. The questions below separate providers who've done real work from those who've read the same blog posts you have.

Ask what they've found in past LLM engagements. Not hypothetically what actual findings have they produced? Can they describe a real indirect prompt injection finding, a cross-user data exposure, or a function call abuse scenario from a previous client? Providers who respond with framework descriptions rather than findings haven't found anything.

Ask how they handle indirect injection testing. This requires active effort crafting malicious content, getting it into the retrieval pipeline, and verifying whether it can redirect the model's behaviour. Most automated tools don't do this at all. If a provider doesn't have a clear process for indirect injection testing in RAG applications, they're testing a subset of the attack surface.

Ask whether they test the integration layer, not just the model. The vulnerabilities that matter in production LLM applications aren't usually in the model itself they're in how the model is connected to your data and backend systems. Providers who focus on model behaviour rather than application-layer integration are looking in the wrong place.

Ask to see the report format. An LLM pentest report should look structurally similar to a web app pentest report: specific findings with reproduction steps, evidence of exploitability, business impact context, OWASP LLM category mapping, and remediation guidance. A report that describes risk categories without specific findings from your application isn't evidence of testing it's a framework summary.

Ask about their methodology for applications they haven't seen before. Good LLM testers adapt their approach to the specific application they don't run the same script on every engagement. Ask what they do in the first two hours of an engagement to understand your application's architecture before they start testing.

For applications that are also going through SOC 2, see our SOC 2 penetration testing requirements guide for how LLM testing fits into compliance evidence. For agentic AI applications specifically autonomous agents that plan and take actions our agentic AI penetration testing guide covers the additional attack surface that comes with autonomous operation.

LLM penetration testing is not optional for production AI applications it's the gap that standard web app pentesting doesn't cover, and the one most likely to produce significant findings in applications built on top of large language models.

The market for this work is still maturing. That means the quality variance between providers is significant much higher than in traditional web app pentesting. The providers doing real work are the ones who can tell you specifically what they've found in applications similar to yours, how they approach indirect injection in RAG systems, and what their findings look like in a report.

Get an LLM Pentest Scoping Call

Related reading:

• Penetration Testing Services
• Agentic AI Penetration Testing — OWASP 2026
• Web Application Penetration Testing Guide
• SOC 2 Penetration Testing Requirements
• PTaaS: The Complete Buyer's Guide

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