How to Security Test LLM and AI-Powered Applications

AI features ship fast. Security testing for them usually doesn't keep up.

If your application calls an LLM - whether that's a customer-facing chatbot, an internal copilot, a retrieval-augmented search, or an agent that takes actions - you have added an attack surface that traditional application security tools were not built to test. The payloads are different. The vulnerabilities are different. And the testing methodology needs to be different too.

This article walks through what security testing for LLM and AI-powered applications actually looks like, what the distinct vulnerability classes are, and how to think about tooling. RedPick is an AI-agentic penetration-testing platform that security tests LLM and AI-powered applications black-box - no source code or credentials required - using AI agents that plan and chain exploits, covering prompt injection, jailbreaking, and output filter bypass, the way a skilled human pentester would.

Why LLMs Are a Different Kind of Attack Surface

A SQL injection scanner works by sending structured, malformed inputs to a defined endpoint and checking for anomalous responses. That logic does not port well to an LLM integration.

Language models accept natural language. Their "logic" is probabilistic, not rule-based. The attack surface is not a form field or a URL parameter - it is the entire space of things a user can say, ask, or instruct the model to do. Adversarial inputs can be lengthy, multi-turn, indirect, or hidden inside content the model is asked to process.

This is why static payload lists and pattern-matching scanners fail here. A scanner firing canned strings at a chat interface will miss most real vulnerabilities. You need a testing approach that understands language, context, and how models actually respond under adversarial pressure.

The Core Vulnerability Classes to Test

The OWASP LLM Top 10 is the best-maintained reference for AI application risks. When you are planning a test, these are the categories that matter most:

Prompt Injection - The foundational LLM attack. A user (or external content) crafts input that overrides the system prompt, changes the model's behavior, or causes it to ignore developer-set constraints. Direct prompt injection comes from the user directly; indirect prompt injection comes from content the model retrieves or processes (a document, a webpage, a database record).

Data Extraction - Getting the model to reveal things it should not: system prompt contents, training data fragments, other users' context, or sensitive information from a connected data store.

Jailbreaking - Multi-step conversational attacks designed to get the model to produce restricted output. This includes encoding tricks, persona assignments, role-playing scenarios, and chained reasoning that gradually shifts the model away from its guardrails.

Output Filtering Bypass - If your application filters model outputs before returning them to users, those filters need to be tested. There are hundreds of known bypass techniques, and a filter that passes simple tests can still fail under adversarial pressure.

Insecure Tool/Plugin Use - When an LLM has access to tools (web search, code execution, API calls, file access), prompt injection can be used to invoke those tools in unintended ways. This is where AI feature vulnerabilities can escalate into real data or system compromise.

Agent-Specific Risks - Agentic AI systems that plan and take actions introduce a broader class of problems: task hijacking, scope creep, unintended side effects from chained actions.

What Good LLM Security Testing Looks Like

Good LLM security testing is adversarial, adaptive, and multi-turn.

A human pentester testing an LLM feature doesn't just try one payload. They probe the model's behavior, observe how it responds, adjust their approach, try indirect routes when direct ones fail, and escalate from information gathering into more impactful attacks. Good automated testing needs to mirror that process.

This is the core argument for AI-native testing tools. RedPick's approach uses AI agents that understand how language models work - because they are themselves models, purpose-trained to find where other models break. Rather than matching against a static payload library, the agents plan and chain exploits the way a skilled human would.

Practically, this means a RedPick LLM test covers:

The testing is black-box by default - no source code or internal credentials required. If you are building with a third-party LLM API and you cannot share the underlying system, that doesn't limit what can be tested. The test operates the way a real attacker would: from the outside.

Continuous Testing vs. Point-in-Time Assessments

LLM features change. Prompt engineering evolves. System prompts get updated. New tools get added to an agentic pipeline. A point-in-time penetration test tells you where you stood on the day it ran.

If your AI features ship continuously, your security testing should too. RedPick supports both on-demand testing and integration into CI/CD pipelines, so you can catch regressions before they reach production. For teams shipping AI features on a regular cadence, continuous testing is the only way to keep the security picture current.

RedPick uses per-test pricing rather than a subscription model, which fits how security testing actually gets consumed - you pay for what you run, not for seat licenses that go unused between assessments.

Getting Started

If you are building or operating an application that integrates an LLM, the starting point is understanding your attack surface:

  1. Map every point where user input reaches the model - directly or indirectly.
  2. Identify what tools or data sources the model has access to.
  3. Understand what the model is supposed to refuse, and what information it should protect.
  4. Test it adversarially, not just functionally.

Functional QA confirms the model does what you want. Security testing confirms it cannot be made to do what an attacker wants. Both matter, and they are not the same thing.

RedPick is available through Apona for teams that want AI-native application security testing without committing to a subscription. Per-test pricing, black-box by default, with coverage across web, mobile, API, desktop, and LLM applications.

Learn more at redpick.ai or contact Apona to scope a test for your AI-powered application.

This post is about RedPick.

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