AI/LLM Security Assessment
Prompt injection, data leakage and model abuse testing for AI features, assistants and agents.
AI features add attack surface that traditional testing doesn't cover. A chatbot can be talked into revealing data it should hold back. An agent with tool access can be steered into taking actions nobody intended. Retrieval pipelines can be poisoned. We test for all of it, using the methods attackers already use.
The assessment produces a threat model for your AI system, findings with proof, and guardrail recommendations that your developers can implement.
- Testers work with our AI engineering practice, so findings come with fixes that fit how the system is built.
- We test the whole chain, including retrieval, tools and integrations, not only the model prompt.
- Findings are rated by business impact, so a jailbreak that reveals nothing sensitive isn't treated as critical.
- A bank or insurer that has put an assistant in front of customers and must answer the regulator's questions about it.
- An airline or retailer whose support agent can issue refunds, rebook or change orders.
- A group whose staff use a vendor copilot connected to internal documents.
- The assistant has tool access and there is no approval step for consequential actions.
- Nobody has tried to make it reveal its instructions or another customer's data.
- Retrieval indexes documents without enforcing the permissions that apply to them.
- The model vendor has been assessed but your integration has not.
What is included.
- 01
Threat modelling
Data flows, trust boundaries, tool access and abuse cases for the AI system mapped.
- 02
Prompt injection testing
Direct and indirect injection through user input, documents, web content and tool outputs.
- 03
Data leakage testing
System prompt extraction, cross-tenant leakage and sensitive data exposure through retrieval.
- 04
Agent and tool abuse
Attempts to escalate actions, bypass approvals and misuse connected systems.
- 05
Guardrail review
Existing filters, evaluations and monitoring assessed and improved.
Four steps, no surprises.
- 01
Model the threat
Understand the system, its data, its tools and who could abuse it.
- 02
Attack
Manual and automated adversarial testing across the identified paths.
- 03
Report
Findings with reproduction, impact and guardrail recommendations.
- 04
Retest
Verification after fixes, with an evaluation set you can keep running.
From first meeting to steady state.
- 01Week 1
Threat model
Data flows, trust boundaries, tool access and abuse cases mapped with your engineers.
- 02Weeks 2 to 3
Attack
Manual and automated adversarial testing across the identified paths in a controlled environment.
- 03Weeks 4 to 5
Report and retest
Findings with reproduction, guardrail recommendations, a reusable evaluation set and verification after fixes.
- Findings by severity from prompt injection, data leakage and tool abuse, tracked to closure.
- Pass rate on the adversarial evaluation set in your release pipeline.
- Share of consequential actions gated by approval or policy.
- Time from a new attack technique being published to it being covered in the evaluation set.
- AI security lead
- Penetration tester
- AI engineer
- Engagement manager
- AI threat model.
- Findings report with reproduction steps.
- Guardrail and architecture recommendations.
- Reusable adversarial evaluation set.
- Retest confirmation.
AI security assessments are fixed scope, agreed after a scoping call about the system and its integrations. A single assistant or agent typically takes one to two weeks of testing plus reporting. Organisations shipping AI features regularly can add this to an annual testing retainer.
Workflow Automation & AI Agents
Agents and automations wired into CRM, ERP and support tools with human approval where it matters.
AI Consulting & AutomationCustom LLM & RAG Solutions
Assistants, copilots and knowledge tools grounded in your own documents and data.
CybersecurityPenetration Testing (Web, API, Mobile)
Manual, adversary-style testing of applications and their business logic, with a retest included.
AI/LLM Security Assessment, in plain terms.
The OWASP Top 10 for LLM applications and the MITRE ATLAS knowledge base, adapted to your specific architecture.
We test your configuration and integration of them and the data you expose. Testing the vendor's model itself depends on their terms.
In a staging environment with sandboxed tool access wherever possible, with agreed limits on any live actions.
Yes. Our AI engineers can implement the recommended controls and the evaluation set becomes part of your release pipeline.