Pen testing · SaaS & technology
Penetration Testing for AI Startups & LLM App Builders
A standard web application pen test checks your login flow and your API endpoints; it does not check whether a crafted prompt can make your agent call a tool it shouldn't, or whether your retrieval pipeline will hand a stranger another tenant's documents. This engagement adds that layer: prompt injection, tool-access abuse and RAG data leakage tested alongside the infrastructure a normal test already covers. The trigger is usually an enterprise pilot's security rider, a new agent feature with real tool access, or a red-teaming requirement neither your team nor a generic tester has run before.
Reviewed by the Privacy Horizon team · Last reviewed
What you're protecting
What a pen test has to reach in an LLM application
The attack surface in an AI product extends past the application code your engineers wrote, into the model's own behaviour and the tools it can call.
The system prompt and guardrails
Whether a crafted user input can override instructions, extract the system prompt itself, or bypass the guardrails meant to keep the model on-task, since a leaked system prompt often reveals business logic and other secrets.
Tool and function-calling access
If your agent can call internal APIs, send emails or query a database, the test has to determine whether an attacker can steer that access through the model rather than around it.
RAG pipeline tenant isolation
Whether the retrieval layer can be tricked into returning another customer's documents or another tenant's embeddings, the specific failure mode a generic web test has no reason to look for.
Data exfiltration through model outputs
Whether a manipulated prompt can make the model encode sensitive data into its response in a form that slips past output filtering, turning the model's own answer into the exfiltration channel.
The infrastructure underneath the model
API keys, vector store access controls, orchestration configuration and the standard web and API surface still need the conventional testing a normal application would get, since AI-specific risk sits on top of ordinary risk, not instead of it.
Regulatory map
Why LLM-specific testing is now an explicit expectation
Regulator guidance and enterprise buyers have both started naming AI-specific attack patterns directly, rather than assuming a generic test covers them.
The OPC's safeguards principle names the attack
The generative-AI principles call out prompt injection and model inversion by name as risks developers must guard against, giving Canadian regulators a specific expectation that a standard pen test scope does not naturally satisfy.
PIPEDA's safeguards duty applied to a new surface
The statutory requirement to protect personal information with safeguards proportionate to its sensitivity extends naturally to a RAG pipeline or an agent's tool access, even though the statute predates the technology.
Enterprise questionnaires now ask directly
SIG and CAIQ-based reviews increasingly carry an AI-specific rider asking whether prompt injection and red-teaming have been tested, not just whether a general penetration test was performed.
SOC 2 and ISO 27001 expectations around testing
Neither standard names LLM-specific testing as a line item, but auditors and enterprise buyers pursuing either commonly expect evidence that AI-specific risk was tested, not just the underlying infrastructure.
What goes wrong
What this testing is built to find before an attacker does
These are documented patterns, not theoretical categories invented for a marketing page.
Prompt injection extracting data through a RAG pipeline
An attacker embeds instructions inside a document the retrieval layer indexes, redirecting the model to surface data it was never meant to expose to that user, a pattern the OPC's principles call out explicitly.
Tool access chained into real-world impact
An agent with email, database or payment tool access can be manipulated into taking an action rather than just returning bad text, turning a language-model quirk into an operational incident.
Weak basics behind an AI interface
A hiring chatbot secured only by the password '123456' and an insecure direct object reference exposed up to 64 million applicant records, a reminder that infrastructure-level testing still matters as much as model-level testing.
Cross-tenant leakage in shared inference infrastructure
A test that never probes tenant boundaries in a multi-tenant vector store or a shared model deployment will miss the exact failure mode that turns one customer's data into another customer's answer.
Jailbreaks bypassing content and data-handling guardrails
A model coaxed out of its intended role can be induced to ignore data-handling instructions entirely, which is why a pen test needs to probe the guardrails themselves, not just the application wrapped around them.
Our pen testing for ai startups & llm app builders
What our AI-application penetration testing covers
Standard application and infrastructure testing, extended with the AI-specific techniques a generic tester typically does not run.
Prompt injection testing
Direct and indirect injection attempts against the system prompt, guardrails and any content the model retrieves, scoped to what an attacker without special access could realistically attempt.
RAG pipeline and vector store assessment
Testing whether the retrieval layer can be manipulated into crossing tenant boundaries or surfacing documents outside the querying user's authorized scope.
Tool and agent-access abuse
Where the product grants function-calling or tool access, testing whether that access can be steered into unauthorized actions through crafted inputs rather than credential theft.
Standard application and API testing
The conventional web, API and authentication testing every product needs, covering the infrastructure the model sits on top of.
A report an enterprise reviewer will accept
Findings mapped to severity and business impact, with an executive summary and technical detail sized for both a security team's review and a founder explaining the results to a customer.
How the engagement runs
How we scope and run testing for an LLM application
Scoped around what your product actually does, since an agent with tool access needs a different test plan than a document-Q&A feature with no write access anywhere.
Step 1
Scope the application and its AI surface
We map the model provider, RAG pipeline, tool access and standard infrastructure to determine which techniques apply and how deep the engagement needs to go.
Step 2
Test infrastructure and application layers
Conventional web, API and authentication testing runs in parallel with the AI-specific work, since a compromised credential can bypass model-level defences entirely.
Step 3
Run AI-specific techniques
Prompt injection, tool-access abuse and RAG leakage testing target the model's behaviour directly, documenting what a realistic attacker could achieve with only the access a normal user has.
Step 4
Deliver findings and support remediation
You receive a report ranked by real-world impact, and we're available to clarify findings for your engineering team or for an enterprise customer's security reviewer.
What it costs
What drives the cost of testing an LLM application
Cost tracks scope and depth, as with any penetration test, but for an AI product the biggest factors are how many AI-specific surfaces are in play: a document-Q&A feature with no tool access is far more contained than an agent with database, email and payment access chained together. The number of tenants, the complexity of the RAG pipeline and whether red-teaming-style adversarial testing is required also move the price.
This work is frequently scoped alongside a broader web and infrastructure test, or timed to land before a SOC 2 or enterprise pilot deadline. We provide a fixed quote after a short scoping conversation about your model, tools and what the engagement needs to satisfy.
AI Startups & LLM App Builders: Pen testing questions, answered
By adding AI-specific techniques on top of a standard application and infrastructure test: direct and indirect prompt injection against the system prompt and guardrails, tool-access abuse where the agent can take real actions, and RAG pipeline testing for cross-tenant data leakage. The underlying infrastructure still needs conventional testing regardless.
Prompt injection testing checks whether crafted input, whether typed directly or hidden inside a document the model retrieves, can override the system's instructions or extract data it shouldn't. The OPC's generative-AI principles name it explicitly as a safeguard developers are expected to guard against, not an optional extra.
We attempt to manipulate retrieval queries and indexed content to see whether the pipeline returns documents or embeddings outside the querying user's authorized scope, including across tenant boundaries in a multi-tenant deployment. This is the failure mode a standard web application test has no reason to look for.
A penetration test is scoped and time-boxed to find and demonstrate specific vulnerabilities; red teaming is broader and more adversarial, probing how the whole system, including guardrails, moderation and human review, holds up against sustained, creative attack attempts. Many AI startups start with a scoped pen test and add red teaming once the product and its enterprise stakes have grown.
No, it extends it. The AI-specific techniques address the model, the RAG pipeline and tool access, but the conventional application, API and infrastructure surface still needs standard testing, since a stolen credential or an unpatched dependency bypasses model-level defences just as easily as it would in any other product.
If the pilot's security review includes an AI-specific rider, likely yes, since a generic pen test report often will not satisfy a reviewer asking specifically about prompt injection or RAG leakage testing. Scoping this early, rather than after the questionnaire arrives, keeps the deal timeline intact.
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Answers & guides
- How much does a penetration test cost (and what affects the price)?
- How does a startup pass an enterprise vendor security review?
- How do you assess the privacy and security risk of an AI vendor?
- How Often Should You Pen Test Your Web App?
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