Service 02 / Model layer

LLM Security

We test the model and the application around it for manipulation and leakage.

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02Model layer
What we test
  • Prompt injection (LLM01)
  • Jailbreak resistance
  • System-prompt leakage
  • Unsafe output handling
Maps to
LLM01LLM02LLM07
Typical targets
Chatbots, copilots, LLM apps

What it is

An LLM interprets language, so language itself is the attack surface. We test prompt injection, jailbreaks, system-prompt leakage and unsafe output across the OWASP LLM Top 10.

The question is simple: can someone type the right words and make your AI ignore its rules, reveal its instructions, or behave in a way that exposes you?

Most apps ship with the model's guardrails assumed rather than tested. We test them.

The attack path
EntryPrompt
WeaknessModel
ImpactManipulation
Real-world example
Public incident

A major AI assistant's hidden instructions were extracted on launch

Soon after a well-known AI chatbot launched, researchers used a simple injection to make it reveal its confidential system prompt — the internal rules and codename meant to stay hidden.

System prompts are recoverable, not secret. Any business logic hidden in one should be treated as exposed.
Publicly reported incident, shown for illustration — not a DeepSentra assessment.
The risk of not testing

What’s at stake if you skip it.

Business logic exposed

Your pricing rules, restrictions and internal instructions can be extracted and worked around.

Guardrails bypassed

Jailbreaks push the model into output that creates legal, reputational or compliance problems.

Data leakage

A model coaxed past its instructions can reveal information it was trusted with.

Reputational harm

Manipulated output is public output — with your brand's name on it.

Free 30-minute consultation

Is your AI exposed to this?

Book a free, no-obligation consultation and we’ll take a first look at your llm security risk together.

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