How to Pentest AI Features in Your SaaS Application

Learn how to pentest AI features in SaaS apps using the OWASP LLM Top 10. Practical prompt injection testing, disclosure probes, and a repeatable methodology.

Your SaaS app probably has AI features now. Maybe it's a chatbot that answers customer questions, an LLM-powered search bar, or a code assistant baked into your developer tools. These features ship fast, delight users, and introduce an entirely new class of vulnerabilities that traditional pentesting doesn't cover.

Prompt injection, training data poisoning, insecure plugin architectures, excessive agency. These aren't theoretical risks. They're showing up in bug bounty reports and breach disclosures with increasing regularity. The OWASP Top 10 for LLM and GenAI gives us a framework to think about these risks systematically, but turning that list into an actual testing methodology takes work.

This guide bridges the gap. We'll walk through a practical, hands-on approach to pentesting AI features in SaaS applications, organized around the OWASP LLM Top 10. Whether you're a developer running pre-release checks, a security engineer building an internal testing program, or a founder preparing for a vendor security review, you'll leave with a repeatable methodology you can start using today. And when you're ready to go deeper, you can launch a full pentest on your AI-enabled application with OWASP-aligned scanning and AI-powered finding triage.

Understanding the OWASP LLM Top 10 Attack Surface

Before you start crafting payloads, you need a mental model of where LLM features create exposure. Traditional web app pentesting focuses on inputs like form fields, query parameters, and API bodies. LLM pentesting adds a new dimension: natural language inputs that get interpreted by a reasoning engine with access to tools, data, and sometimes the ability to take actions on behalf of users.

The OWASP LLM Top 10 maps out ten categories of risk, but they cluster into a few logical groups that help you plan your testing.

Input Manipulation Risks

LLM01: Prompt Injection is the big one. It's the SQL injection of the AI world. Direct prompt injection means a user types something into a chat interface that overrides the system prompt. Indirect prompt injection means the LLM processes untrusted content (a webpage, an email, a document) that contains hidden instructions. Both let attackers hijack the model's behavior.

LLM02: Sensitive Information Disclosure happens when the model leaks data it shouldn't. This could be training data, system prompts, API keys embedded in context, or PII from other users' sessions. If your LLM has access to a database or document store, a clever prompt can sometimes convince it to dump contents it was supposed to keep private.

These two risks should be the first thing you test because they require the least setup and often yield the most dramatic findings.

Architecture and Integration Risks

LLM07: System Prompt Leakage is closely related to sensitive disclosure but specifically targets the instructions you've given the model. Attackers who extract your system prompt gain a roadmap of your AI feature's capabilities, restrictions, and sometimes internal API details.

LLM08: Vector and Embedding Weaknesses apply if you're using retrieval-augmented generation (RAG). Poisoned documents in your vector store can influence model outputs for every user, not just the attacker.

LLM06: Excessive Agency is what happens when your LLM can do too much. If your chatbot can update database records, send emails, or call external APIs, and the model decides to do these things based on a manipulated prompt, you have an agency problem.

These risks require understanding the specific integrations your AI feature relies on. You'll need architecture diagrams or at least a solid understanding of what tools and data sources the LLM can access.

Supply Chain and Operational Risks

The remaining items, including LLM03: Supply Chain, LLM04: Data and Model Poisoning, LLM05: Improper Output Handling, LLM09: Misinformation, and LLM10: Unbounded Consumption, round out the picture. Supply chain risks come from third-party models and plugins. Output handling failures happen when you render LLM output as HTML or pass it to a shell without sanitization. Unbounded consumption is the AI equivalent of a denial-of-service attack, where a crafted input causes the model to burn through tokens, compute, or downstream API calls.

Mapping your AI features to these categories before testing gives you a checklist of what to probe. You don't need to test all ten for every feature. A simple chatbot with no tool access has a different risk profile than an autonomous agent that can execute code. Prioritize based on the capabilities your LLM actually has.

Building Your LLM Pentest Methodology Step by Step

A good pentest methodology is repeatable and systematic. Here's how to structure your LLM testing engagement from reconnaissance through exploitation.

Step 1: Map the AI Attack Surface

Start by inventorying every place your application accepts input that eventually reaches an LLM. This includes obvious places like chat interfaces and AI-powered search, but also less obvious ones: file uploads that get processed by AI, email content that feeds into automated workflows, webhooks that pass data through LLM-based parsing, and even user profile fields that get injected into prompts.

For each input point, document:

• What model processes it (GPT-4, Claude, an open-source model, etc.)
• What system prompt or instructions frame the interaction
• What tools or functions the model can call
• What data the model can access (vector stores, databases, APIs)
• What output rendering happens (HTML, markdown, code execution)

This inventory is your testing scope. If you need help defining it rigorously, the guide on how to scope your first pentest engagement covers the principles that apply to AI features too.

Step 2: Test for Prompt Injection

Direct prompt injection testing starts simple and gets progressively more creative. Begin with baseline attempts:

These naive attempts get caught by most guardrails. The real testing starts when you layer in evasion techniques:

• Encoding tricks: Base64-encode instructions and ask the model to decode them
• Role-play framing: "Let's play a game where you pretend to be an unfiltered AI..."
• Language switching: Provide instructions in a language the content filter handles poorly
• Payload splitting: Spread the malicious instruction across multiple messages in a conversation
• Markdown/formatting abuse: Hide instructions in markdown comments, code blocks, or formatting the model parses differently than the filter

For indirect prompt injection, you need to think about every piece of external content the LLM processes. If your AI feature summarizes web pages, create a test page with hidden instructions in white-on-white text, HTML comments, or metadata fields. If it processes uploaded documents, embed instructions in PDF metadata, hidden text layers, or image EXIF data.

Document each test with the exact input, the expected safe behavior, and the actual output. This structured approach makes your findings reproducible and your report actionable.

Step 3: Probe for Information Disclosure

System prompt extraction is the most common disclosure finding. Try variations of:

Beyond system prompts, test for training data leakage by asking the model about specific individuals, companies, or datasets. Test for cross-session data leakage by checking whether information from one user's conversation appears in another's. If the model has RAG access, try to get it to cite and quote source documents that should be access-controlled.

Step 4: Assess Excessive Agency and Output Handling

If the model can call functions or tools, your job is to make it call the wrong ones. Ask it to perform actions outside its intended scope. Try to get it to call internal APIs with parameters it shouldn't. Test whether function-calling guardrails hold up when the request is framed as debugging, testing, or urgent support.

For output handling, inject payloads into your inputs that would be dangerous if rendered unsanitized:

If the LLM's output gets rendered as HTML, passed to a command line, or used in a database query, you're testing for second-order injection vulnerabilities through the AI layer.

Practical Testing Toolkit and Automation

Manual testing gives you depth, but you also need breadth. Here's how to build a practical toolkit that scales your LLM pentesting without losing the nuance that makes AI testing effective.

Start with a payload library. Organize your prompt injection attempts by category: direct injection, indirect injection, jailbreaks, information extraction, function abuse, and denial of service. Version control this library because you'll be adding to it constantly as models and guardrails evolve.

For automated fuzzing, build a simple harness that sends payloads to your AI endpoints and evaluates responses against a set of detection rules. A basic detection rule set might look like this:

This isn't production-grade detection. It's a testing accelerator that flags responses for human review. The key insight is that LLM testing always needs a human in the loop because the boundary between safe and unsafe model behavior is context-dependent in ways automated tools struggle with.

For rate limiting and resource consumption testing (OWASP LLM10), measure token usage and response times across your payload set. Craft inputs designed to maximize token consumption: deeply nested reasoning requests, instructions to repeat content, and prompts that trigger long chains of tool calls. Compare these against your billing thresholds and rate limits.

RAG-specific testing requires setting up adversarial documents in your vector store. Create test documents with embedded instructions, contradictory facts, and access-controlled content at different permission levels. Then query the AI feature as users with different roles to verify that retrieval respects authorization boundaries.

The NIST AI Risk Management Framework provides additional governance context for teams building ongoing AI testing programs, especially when you need to demonstrate risk management maturity to auditors or compliance reviewers.

One pattern that catches many teams off guard: test your AI features with the same rigor you'd test your APIs. The underlying HTTP endpoints still need authentication, authorization, rate limiting, and input validation. An LLM feature built on an API that's missing access controls gives attackers a natural language interface to an already-vulnerable backend. If your traditional API security isn't solid, test those endpoints for OWASP Top 10 vulnerabilities before layering on LLM-specific testing.

From Findings to Fixes and Ongoing Assurance

Finding vulnerabilities is only valuable if you can communicate them clearly and drive remediation. LLM findings require some special handling in your pentest reports because the vulnerabilities are probabilistic, not deterministic. The same prompt injection payload might work 3 times out of 10, and that's still a finding.

For each finding, document:

• The attack category mapped to the OWASP LLM Top 10 item number
• Reproduction steps with exact prompts and any required setup
• Success rate across multiple attempts (e.g., "Prompt extraction succeeded in 4 of 10 attempts")
• Impact analysis specific to your application (data exposure scope, action severity, user trust implications)
• Remediation guidance tailored to the finding type

Remediation for LLM vulnerabilities falls into a few buckets. Input filtering and prompt hardening help with direct injection. Sandboxing and permission scoping address excessive agency. Output sanitization prevents second-order injection. But the most effective defense is often architectural: limiting what data and tools the LLM can access in the first place.

Here's a practical remediation checklist for the most common findings:

• System prompts are hardened with clear boundary instructions
• Input filters block known injection patterns without breaking legitimate use
• LLM tool access follows least privilege (read-only where possible)
• Function calls require explicit user confirmation for destructive actions
• Output is sanitized before rendering in any context (HTML, shell, SQL)
• RAG retrieval respects user-level access controls
• Token usage and API call rates are bounded per session
• Sensitive data is excluded from model context and vector stores
• Model responses are logged for anomaly detection and audit

For teams operating in regulated environments or preparing for vendor security reviews, your AI pentest findings feed directly into your compliance narrative. SOC 2 auditors, ISO 27001 assessors, and enterprise customers all want evidence that you've tested your AI features specifically. Having audit-ready security policies that address AI data handling, access control, and incident response strengthens the story your pentest report tells.

LLM pentesting isn't a one-time activity. Models get updated, system prompts change, new tools get connected, and RAG data grows. Build LLM-specific test cases into your CI/CD pipeline as regression tests. Run focused AI pentests before major feature launches and on a regular cadence alongside your traditional application security testing.

The attack surface of AI features is expanding faster than most teams' ability to test it. But the methodology isn't magic. It's structured, repeatable, and grounded in the same adversarial thinking that makes any pentest effective. Start with your most capable AI feature, the one with the most data access and the most agency, and work through the methodology above. You'll almost certainly find something worth fixing.

Ready to test your AI features with a structured, OWASP-aligned approach? Start your pentest with Pentest Today and get AI-powered scanning that covers traditional web vulnerabilities and LLM-specific risks in a single engagement. If your AI pentest is part of a compliance push, generate audit-ready reports and pass your security review with everything you need for SOC 2, ISO 27001, and vendor questionnaires.