05
Service 05Model red teaming · Prompt injection

AI Security

We secure your AI, the models, the agents, and the guardrails meant to hold them in.
At a glance
Typical duration2–4 weeks
Engagement shapesPoint-in-time · Retainer
Led bySenior AI security engineer
OutputFindings + guardrail design
§ 01Overview
AI Security
Your LLM stack is an attack surface. We test it like one: models, agents, tool-use, and retrieval pipelines, against the newest classes of attack.
§ 02What's included
The work, concretely.
Named capabilities, scope any one, or combine them into a single engagement.
01
Model red teaming
Adversarial testing of model behavior, jailbreaks, harmful output, and safety bypasses.
02
Prompt injection testing
Direct and indirect prompt-injection attacks against your application and its data sources.
03
Agent & tool-use review
When your AI can take actions, we test what happens when an attacker steers those actions.
04
RAG pipeline assessment
We probe retrieval pipelines for data leakage, poisoning, and context manipulation.
05
Guardrail design
We help design and validate the controls that keep your AI inside its intended bounds.
06
AI policy & governance
Practical policy aligned to NIST AI RMF and the EU AI Act, written for how you actually ship.
§ 03How we approach it
A clear method, every time.
1
Map the stack
We chart your models, agents, data sources, and the trust boundaries between them.
2
Attack the model
Adversarial prompts, injection, and tool-abuse against the live system.
3
Test the guardrails
We measure whether your controls actually hold under pressure.
4
Harden & document
Concrete fixes, guardrail recommendations, and governance you can defend.
§ 04What you get
Deliverables you can act on.
Every engagement ends with evidence, not just a score, written for the people who fix things and the people who fund the fixes.
01Adversarial findings with reproducible prompts
02Tool-use and agent abuse analysis
03Guardrail and mitigation recommendations
04AI risk and governance guidance
05Re-test of hardened controls
FAQ
Questions, answered
How do you secure an AI system?
We map the trust boundaries and attack across them, then fix at the architecture level. Prompt injection, tool-call hijacking, and data exfiltration are structural problems, not wording problems.
Can prompt injection be fixed with a better system prompt?
No. A model reads untrusted text with the same trust it gives your rules. The durable controls sit outside the prompt: scoped tools, hard boundaries, output validation, and full logging.
Do you test models, or the application around them?
The application around them. We red-team the whole system: retrieval, tools, and the data paths an attacker would actually use.
How does this map to the EU AI Act?
We align the testing and evidence to the obligations that apply to your system, so security work and regulatory readiness move together.
Ready to scope ai security?
A senior engineer will help you define scope on a 30-minute call. No SDR, no pressure.
Book a scoping call or email contact@raptoric.com