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AI SYSTEMS · AUTHORISED ADVERSARIAL TESTING
AI Security Testing for Businesses
Test what your AI trusts, reveals and can do.
AI security testing evaluates how an AI-enabled system behaves when inputs, retrieved content or connected tools are used in unintended ways. SabreShield AI helps UK businesses examine the paths between models, applications, sensitive data and real-world actions.
We agree the models, applications, data boundaries, test accounts and permitted agent actions before testing. Explicit written authorisation and rules of engagement are required.
BEYOND THE PROMPT
The risk is in the connections.
An apparently helpful answer can conceal an access-control failure or an unsafe tool call. AI red teaming explores how realistic adversarial inputs interact with your system’s instructions and permissions.
Our specialist focus is the whole AI workflow: the model, its retrieval sources, the application around it and the actions it can trigger. Testing is adapted to your deployment rather than treated as a generic jailbreak checklist.
01
Prompt injection
Direct prompts and instructions hidden in retrieved documents may redirect model behaviour or attempt to override the intended task. Read our guide to prompt injection for the risks and defensive controls.
02
Sensitive data leakage
Responses, retrieval results or tool output may expose information across users, roles or organisational boundaries.
03
Agent and tool abuse
Excessive permissions or weak approval controls may allow an agent to take actions beyond the user’s authority.
04
Untrusted integrations
RAG sources, third-party APIs and automation steps may introduce unsafe data or propagate model output without validation.
AI systems and boundaries we test
- Chatbots and AI assistants
- Models and application prompts
- RAG retrieval and document access
- Agent permissions and tool calls
- Sensitive-data handling
- Identity and tenant boundaries
- Model APIs and integrations
- Output handling and downstream use
- Approval gates for automation
- Logging and security controls
Final scope and permitted activities are agreed before testing begins.
How an AI security engagement works
We translate business concerns into bounded abuse cases, with test data, allowed actions and stop conditions agreed in writing.
01
Map the system
Identify users, models, retrieval sources, tools and data flows. Confirm ownership, provider terms and the authorised boundaries.
02
Design abuse cases
Choose scenarios around prompt injection, cross-user disclosure and unsafe actions that matter to your actual workflows.
03
Test safely
Run controlled adversarial inputs using agreed accounts and data. Restrict tool calls and automation to permitted effects.
04
Validate impact
Repeat significant observations and trace the application or permission controls involved, recording variability and limitations.
05
Remediate & retest
Recommend controls at the relevant layer and retest agreed fixes against the original scenarios and related regressions.
A model response is a clue, not a conclusion.
Language models can respond inconsistently. A single alarming output does not establish a reliable attack path or prove access to protected data.
We examine the surrounding evidence: retrieved records, permission boundaries, tool calls and resulting actions. Reporting separates observed impact from plausible but unconfirmed behaviour.
01
Contextual evidence
Capture the scenario, relevant configuration and observed behaviour so your team can investigate it.
02
Control-level fixes
Address retrieval permissions, tool restrictions and approval checks, rather than relying only on prompt wording.
03
Bounded conclusions
State what was tested, how repeatable the behaviour was and what remains outside the evidence.
Evidence your AI team can act on
The output connects adversarial behaviour to engineering decisions, with enough context to reproduce and prioritise the material findings.
- AI system and trust-boundary summary
- Authorised abuse-case coverage
- Prioritised AI security findings
- Reproduction scenarios and observations
- Affected models, tools and integrations
- Business-impact explanation
- Control and remediation recommendations
- Testing limitations and residual risks
- Agreed retest results
AI security testing and conventional application testing
Conventional application testing examines controls such as authentication, access control and input handling. Those controls still matter when an application uses AI.
AI security testing also examines instruction handling, retrieval boundaries and agent behaviour. A model’s probabilistic output can introduce failure modes that ordinary endpoint tests do not address.
For systems with customer portals or APIs, combine AI-focused scenarios with application-layer testing so the relationship between model behaviour and software controls is covered.
YOUR QUESTIONS, ANSWERED
AI Security Testing FAQs
What is AI security testing?
It is a structured assessment of how an AI-enabled system handles adversarial inputs, data boundaries and connected actions. The scope can include the model interface, RAG sources, application controls and agent tools.
Is AI red teaming just trying to jailbreak a chatbot?
No. Jailbreak attempts are one technique. Useful AI red teaming also tests data access, retrieval, tool permissions and automation, with scenarios chosen for your system and agreed in advance.
Can you test RAG applications?
Yes, where authorised. RAG testing can examine document access, retrieval boundaries, untrusted instructions in source content and the handling of retrieved data. Test corpora and access roles are agreed before work begins.
How are agent actions kept under control?
We agree which tools, accounts and actions may be exercised and define approval gates and stop conditions. Representative test environments and synthetic data are preferred when production actions could affect customers or records.
Can third-party AI services be included?
They can be considered where the relevant provider terms and permissions allow it. We distinguish testing your own configuration and application from testing infrastructure you do not own or control.
Will the assessment make our AI safe in every situation?
No assessment can guarantee that. The report describes the scenarios and system version tested, observed weaknesses and limitations. Material changes to models, retrieval or tool permissions may warrant further testing.
Do you need written permission and can you retest fixes?
Yes. Testing requires explicit written authorisation, an agreed scope and rules of engagement. Retesting can be included to verify agreed controls against the original scenarios after remediation.
Related security services
AI-specific findings often sit alongside application flaws or broader attack paths. Choose complementary testing around the controls and systems involved.
Explore the service that answers your next security question. View all services. For an introduction to the methods and risks, read our guide to AI security testing.
AUTHORISED SECURITY TESTING
Find the unsafe path before your AI takes it.
Tell us what your AI can access, which tools it can use and which outcomes matter most. We will help define a proportionate, explicitly authorised test scope.
Discuss your requirements through our existing assessment enquiry contact.