Penetration Testing
LLMs are being integrated into products, workflows, and customer-facing systems faster than anyone is testing them. A single prompt injection flaw can turn your AI feature into a data exfiltration tool.
About This Service
Security assessment of AI-powered applications and large language model (LLM) integrations — testing for prompt injection, model manipulation, data leakage, and AI-specific attack vectors.
Most AI and LLM deployments are in production before anyone has assessed what an attacker could extract, manipulate, or exploit. Prompt injection, data leakage, and model manipulation are real, exploitable risks — and the attack surface is new enough that most security teams have limited experience assessing it.
OWASP LLM Top 10, NIST AI RMF
Typical Timeline: 3–7 days depending on system complexity
Report: Executive summary + full technical findings
Retest: Complimentary, included in every engagement
NDA: Signed before any technical discussion
Deliverables
AI/LLM risk overview for product, security, and leadership teams
All findings mapped to the OWASP LLM Top 10 with proof-of-concept prompts and reproduction steps
Documented adversarial prompt attempts and their outcomes
Review of API integration security, input validation, and output handling
Practical mitigations including prompt hardening and architectural recommendations
Confirms mitigations are effective after your team applies fixes
Scope
Prompt injection — direct and indirect injection attacks
Jailbreaking and safety filter bypass attempts
Data exfiltration via LLM outputs
Training data leakage and memorisation issues
Model inversion and extraction attacks
Insecure LLM API integration — authentication, rate limiting, input validation
Retrieval-Augmented Generation (RAG) pipeline security
Agentic AI tool-use and plugin security
Output handling and downstream injection risks
Adversarial inputs targeting AI-driven decision systems
Process
Every engagement follows a defined, transparent process — no surprises, no hidden scope changes, and no invoice for work you did not agree to.
Understand the AI system architecture, LLM provider, integration points, and threat model.
Systematically attempt to manipulate LLM behaviour through adversarial prompts.
Test the security of the surrounding application and API layer.
Assess whether the model can be made to reveal training data or sensitive information.
Findings mapped to OWASP LLM Top 10 with remediation guidance.
Verify mitigations are effective.
Why Us
Automated tools find known signatures. Our certified consultants find the chained attack paths, logic flaws, and context-specific vulnerabilities that no scanner will surface. Every finding we report is manually verified — zero false positives.
Every finding includes a proof-of-concept, a CVSS risk score, the affected system or endpoint, and step-by-step remediation guidance written for the team that has to fix it. Your developers and your board both get a report they can use.
We sign a formal NDA before any technical discussion begins. Your vulnerabilities, your architecture, and your engagement findings are treated with the same confidentiality as attorney-client communications. We have never disclosed client information.
Every engagement includes a complimentary retest once your team has addressed the findings. We verify that the vulnerabilities are genuinely closed — not surface-patched — and issue a formal retest certificate you can share with clients and auditors.
AI security testing is not just a renamed web app checklist. We have hands-on experience with LLM integrations, RAG pipelines, and agentic AI systems — and we follow the rapidly evolving OWASP LLM Top 10 and NIST AI Risk Management Framework to ensure your AI deployment is assessed against the latest known threats.
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FAQ
LLMs are being integrated into customer-facing products, internal tools, and automated workflows at speed — often without security review. Prompt injection alone can lead to data exfiltration, unauthorised actions, and reputational damage.
It is OWASP’s list of the ten most critical security risks for applications built on large language models. Our testing methodology covers all ten categories.
Yes. We assess how your application integrates with third-party LLM APIs — including input validation, output handling, and the security of the surrounding application code.
If you are building any AI-powered features — chatbots, document analysis, code generation, decision support — AI security testing is relevant and increasingly expected by enterprise clients and compliance auditors.
Get a free scoping consultation — no commitment required. We’ll scope the right AI & LLM penetration testing engagement for your environment and send a fixed-fee proposal within 24 hours.
