How to Security Test LLM and AI-Powered Applications
AI features ship fast. Security testing for them usually does not. If your application integrates a large language model - whether that is a customer-facing chatbot, a code assistant, an internal knowledge tool, or an AI agent with tool-calling capabilities - you have opened an attack surface that traditional scanners were not built to find.
This article breaks down what LLM application security testing actually covers, why the standard AppSec toolchain falls short, and how to approach it systematically.
Why LLM Apps Are a Different Kind of Target
A conventional web application has a defined input schema. A form field accepts a string. An API endpoint expects a JSON body. The attack surface is structured, and scanners can enumerate it predictably.
An LLM endpoint accepts natural language. The "input" is open-ended by design. An attacker does not need to find a SQL injection vector - they can just ask the model to behave differently, reveal data it should not, or take actions outside its intended scope. The attack surface is semantic, not syntactic.
That changes everything about how you test.
The OWASP LLM Top 10: What You Are Actually Testing For
OWASP published its LLM Top 10 to give practitioners a shared vocabulary for these risks. The categories most relevant to application-level testing are:
Prompt injection - an attacker embeds instructions in user-supplied input (or in content the model retrieves) that override the system prompt or intended behavior. This can redirect the model to produce harmful output, reveal confidential context, or take unintended actions via tool calls.
Data extraction - the model is coaxed into surfacing information it was trained on, has access to via retrieval, or holds in its context window - things like system prompts, other users' data, or API keys embedded in instructions.
Jailbreaking - multi-step, conversational attacks designed to get the model to violate its own guardrails. These are rarely single-turn. They may use role-playing scenarios, hypothetical framings, or gradual escalation across a conversation to wear down content filters.
Output filter bypass - even when a model has filtering on its responses, those filters can often be circumvented through encoding tricks, alternate character sets, indirect phrasing, or multi-turn conversation patterns. Testing output filtering means evaluating robustness across many bypass techniques, not just the obvious ones.
Why Static Scanners and Pattern-Matching Tools Miss This
Traditional DAST tools fire payloads. They know what a SQL injection looks like. They know what an XSS vector looks like. They do not know what a successful prompt injection looks like, because the "success condition" is a natural language response that has to be interpreted.
A pattern-matcher cannot tell the difference between a model that appropriately explains why it cannot help and a model that has been partially jailbroken into giving a hedged but still harmful response. Evaluating that requires understanding the model's intended behavior, the conversation context, and whether the output represents a genuine boundary violation.
This is why LLM security testing is one of the few areas where the testing tool genuinely benefits from being AI-based itself.
What Good LLM Security Testing Looks Like in Practice
Coverage, not spot checks. A single-turn prompt injection test tells you almost nothing. A real evaluation runs hundreds of attack variations - different injection syntaxes, different encoding schemes, different conversation paths - to understand the actual robustness boundary, not just whether one specific payload fires.
Multi-turn attack chains. Jailbreaks rarely succeed in a single message. Your testing methodology needs to model attacker patience: building rapport, shifting framing, and escalating gradually across a conversation the way a human attacker would.
Context-aware assessment. The model's system prompt, its tool-calling configuration, and what data it has access to all change the risk profile. A knowledge-retrieval assistant with access to internal documents is a very different target than a customer-facing FAQ bot with no tool access. Testing should account for what the model actually can do.
Output evaluation, not just input fuzzing. You need something that can read the response and determine whether a boundary was crossed - not just whether the model responded at all.
How RedPick Approaches LLM Testing
RedPick is an AI-agentic penetration testing platform that covers LLM and AI applications alongside web, mobile, API, and desktop targets. Its approach to LLM testing is grounded in the OWASP LLM Top 10 methodology.
The core design principle is that AI agents understand how language models work because they are language models - purpose-trained to find where other models break. Rather than firing static payloads, RedPick's agents plan and chain attacks the way a human pentester would, adapting based on model responses.
Coverage includes prompt injection, data extraction, jailbreaking via multi-step conversational attacks, and output filter bypass across a wide range of techniques. The platform is black-box by default - it does not require source code or model access, which means you can test third-party AI features or vendor-provided LLM integrations with the same workflow as your own.
RedPick operates on per-test pricing with no subscription, which fits the on-demand testing cadence most teams actually need for AI features: when you ship a new prompt configuration, when you integrate a new model, or when you change what tools the model can call.
More at redpick.ai.
Putting It Together
LLM security testing is not a one-time audit. AI features change frequently - prompt configurations get updated, new tools get added, the underlying model gets swapped. Each of those changes can shift the attack surface in ways that are not obvious without testing.
The right approach is to treat LLM security testing like you treat API security testing: systematically, repeatably, and close enough to the development cycle that findings are actionable. The toolchain has caught up. There is no longer a reason to ship AI features without testing them.
This post is about RedPick.