AI/LLM Penetration Testing
Secure your AI models, LLM applications, and ML pipelines against emerging threats.
What this assessment covers.
AI and large language models are transforming how organisations operate, but they introduce an entirely new class of security risks. DERISK’s AI/LLM Penetration Testing service evaluates the security of your AI systems, LLM applications, and machine learning pipelines against emerging threats unique to AI/ML environments.
Our testing covers prompt injection attacks, jailbreaks, model extraction and inversion, data leakage through model responses, insecure RAG pipelines, adversarial inputs, API and deployment security, training data integrity, and supply chain risks in ML dependencies. We test against the OWASP Top 10 for LLM Applications and emerging AI security frameworks to ensure your AI deployments are resilient against both traditional and AI-specific attack vectors.
How we test.
A structured, expert-led methodology from initial reconnaissance through to real-time reporting and remediation guidance.
- 01
AI System Discovery & Architecture Mapping
We map your AI stack — models, APIs, RAG pipelines, training data sources, inference endpoints, and integrations — to build a complete picture of your AI attack surface.
- 02
Prompt Injection & Jailbreak Testing
We craft adversarial prompts to test for prompt injection, jailbreaks, and system prompt leakage that could cause your LLM to bypass guardrails, leak sensitive data, or execute unintended actions.
- 03
Model Extraction & Data Leakage Assessment
We test whether an attacker can extract model weights, training data, or sensitive information through carefully crafted queries — assessing your exposure to model inversion and data extraction attacks.
- 04
RAG Pipeline & Integration Security Testing
We evaluate retrieval-augmented generation pipelines, embedding databases, and third-party integrations for injection points, data leakage, and insecure data handling that could compromise your AI application.
- 05
Adversarial Input & Model Robustness Testing
We craft adversarial inputs designed to cause misclassification, hallucination, or unexpected behaviour — testing the robustness of your model against manipulation and evasion attacks.
- 06
Real-time findings published to the DERISK PTaaS portal
AI-specific vulnerabilities are published live to the PTaaS portal with proof-of-concept evidence and remediation guidance tailored to AI/ML architectures.
- 07
Reporting & Remediation guidance
You receive a comprehensive report with risk-rated findings, adversarial evidence, and AI-specific remediation steps aligned with the OWASP Top 10 for LLM Applications.
Why teams choose us.
Expert-led, manually validated, and delivered through the DERISK PTaaS platform for continuous visibility and rapid remediation.
Outstanding Features
- ✓Assessment aligned with the OWASP Top 10 for LLM Applications
- ✓Prompt injection, jailbreak, and system prompt leakage testing
- ✓Model extraction, data leakage, and adversarial input testing
- ✓RAG pipeline, embedding database, and integration security assessment
Why Choose Our Service?
- ▸AI-specific expertise: Testing goes beyond traditional application security to address threats unique to AI/ML systems
- ▸Emerging threat coverage: Assess against the latest AI attack techniques including prompt injection, model extraction, and adversarial inputs
- ▸Real-time visibility: Track AI-specific findings and remediation through the DERISK PTaaS platform
- ▸Deployment confidence: Ensure your AI applications are secure before and after deployment with continuous testing
Frameworks & standards.
Testing is aligned with industry-recognised frameworks and standards to ensure comprehensive, repeatable coverage.
What you receive.
Everything you need to understand, prioritise, and remediate findings — delivered through the platform with ongoing validation.
AI/LLM Pentest questions.
What does AI and LLM penetration testing cover?
Prompt injection and jailbreaks, system prompt leakage, training and inference data exposure, model extraction, adversarial inputs, insecure output handling, and the security of RAG pipelines, embedding stores and agent tool integrations.
Which frameworks do you test against?
The OWASP Top 10 for LLM Applications, MITRE ATLAS and the NIST AI Risk Management Framework, alongside PTES for the surrounding application and infrastructure.
Why is prompt injection so hard to fix?
Because an LLM has no reliable boundary between instructions and data — everything in the context window is text. Mitigation is architectural: least-privilege tool access, output validation, and never trusting model output as a security decision. We test whether those controls actually hold.
Do you test agentic systems and tool use?
Yes, and it is where the highest-impact findings usually are. An agent with tool access turns a prompt injection from an embarrassing output into a real action against your systems, so we test what an injected instruction can actually reach.
Often scoped together.
API Pentest
Expose and fix vulnerabilities across your API endpoints.
Web App Pentest
Identify vulnerabilities in your web applications before attackers do.
Source Code Review
Catch vulnerabilities early in the development lifecycle.
Cloud Security Assessment
Identify misconfigurations and risks across your cloud environments.
Ready to secure your assets with AI/LLM Pentest?
Get in touch with DERISK to schedule an assessment or learn more about how our expert-led testing can strengthen your security posture.
Request a demo ↗