LLM Red Teaming vs Web App Penetration Test: What Each Covers, Cost and When You Need Both

These are the indicative ranges published on our cybersecurity services page. Final pricing depends on scope, and every engagement starts with agreeing what is in and out of bounds.

What a web application pen test covers

A web app pen test is an authorised attempt to break into your running application, using black-box, grey-box or white-box approaches depending on how much access the testers are given. Typical coverage maps to the OWASP Top 10: broken access control, injection, authentication failures, insecure configuration, vulnerable components and similar issues.

It answers questions such as: can one customer read another's data, can a normal user reach admin functions, can input break out of its intended use, and are secrets or debug interfaces exposed? The result is a written report with proven findings, severity and remediation advice, and it should include a re-test after you fix what was found.

If you are earlier in your journey and want to understand which review fits, see our comparison of technical audits, penetration tests and code reviews.

What LLM red teaming covers

LLM red teaming treats the model, and everything wired to it, as the target. The main attack types are:

• Direct prompt injection: a user writes instructions that try to override your system prompt ("ignore your previous instructions").
• Indirect prompt injection: hostile instructions hidden in a web page, document, email or tool result that the model reads on a user's behalf.
• Jailbreaks: getting the model past its safety and policy limits.
• Data extraction: making the model reveal its prompt, other users' data or content it was trained on or retrieves.
• Model denial of service: prompts designed to consume excessive compute or cost.
• Retrieval poisoning: planting content in the documents your retrieval system (RAG) searches so the model repeats or acts on it.
• Agent tool misuse: persuading an agent to call a powerful tool, such as sending email, issuing refunds or deleting records, with attacker-controlled inputs.
• Scope escalation through MCP servers and plugins: using a connected tool to reach data or actions the user should not have.
• Supply-chain risks: malicious third-party prompts, models or tools.

The report shows which of these work against your product, how an attacker would chain them and what to change. Many production AI products expose several of these at the same time, often because each feature was reasonable on its own.

Where the two overlap

The overlap is bigger than it looks. Once an AI agent can call tools, a prompt injection becomes an access control problem: the model is a new, easily persuaded user inside your system. If it can read a customer's records or trigger a payment, then everything a web pen test checks about permissions now also applies to what the model is allowed to do.

It also works the other way. Model output is untrusted text. If your interface renders it without care, a classic web vulnerability such as cross-site scripting can arrive through the model. A good red team will include conventional checks along these paths.

Do you need one or both?

• A normal web app with no AI features: a web app pen test, once the basics are fixed.
• A chatbot that only answers from public content: lower risk, but test prompt leakage, abuse and cost limits, and check the web layer too.
• An assistant with access to private or customer data: both. Data exposure through the model is the most expensive failure to explain.
• An agent that takes actions through tools or MCP servers: both, with the tool permissions as a central focus.
• A retrieval (RAG) product over many sources: both, plus attention to who can add documents to the index.

When budget is tight, start by listing three things: what data the model can see, what actions it can take and who can talk to it. The more of each, the more urgent the testing.

Reduce the risk before you test

Prompt injection cannot be fully eliminated today, so good design assumes the model will sometimes be fooled and limits the damage:

1. Give tools the least privilege possible and scope them per user, never with one powerful shared key.
2. Require human confirmation for destructive or high-value actions.
3. Treat model output as untrusted and validate or encode it before it reaches an interface, a database or another system.
4. Keep instructions and untrusted content separate where your architecture allows it, and avoid putting secrets in prompts.
5. Log tool calls and set rate and spend limits so abuse is visible and bounded.

Building these in from the start is much cheaper than retrofitting them after a finding. It is the approach we take when we build AI agents and MCP servers.

What to expect from an engagement

A credible provider will agree scope and rules of engagement up front, test only what you authorise, and deliver a written report written for both engineers and decision makers. Fixes should be followed by a re-test so you can show they work. Be wary of anyone who promises that your AI product will be "fully secure"; the honest outcome is a clearer picture, fewer exploitable paths and evidence you can share with customers.

For AI-built apps that are heading towards launch, the order we usually suggest is a technical audit, then fixes, then formal testing. Our production readiness checklist is a good place to start.