Testing
AI Security Testing
Testing for the failure modes that only apply to AI systems.
If your product uses a large language model, it has an attack surface that conventional testing does not cover. The model takes untrusted input and produces output that your application then acts on. That pattern creates failure modes with no equivalent in traditional software, and a standard penetration test will usually miss them entirely.
What is actually different about testing AI systems
Prompt injection is the central problem. Instructions and data arrive through the same channel, so content the model reads — a document, a web page, a support ticket — can carry instructions it follows. Where the model has tools available, an injected instruction can become a real action: sending an email, querying a database, calling an API. Unlike SQL injection, there is no complete fix, only mitigation and containment. Data exposure is the second area. Models can surface information from their context that the current user should not see, particularly in systems where retrieval pulls from a shared corpus without enforcing per-user permissions. Testing checks whether one tenant's data can be drawn out through another tenant's session. Agent tooling widens the impact considerably. An agent with the ability to execute code, browse, or call internal services turns a prompt-level compromise into a system-level one. Scope here includes what the agent is permitted to do and what stops it exceeding that. Output handling matters too — application code that treats model output as trusted can reintroduce classic vulnerabilities, with the model as the injection vector rather than the user. This is a young discipline. Approaches are less standardised than in conventional testing, and provider quality varies more widely.
Who needs it
- Companies shipping a customer-facing feature built on an LLM
- Teams deploying agents with access to internal tools, data, or the ability to take actions
- Products where the model processes content from untrusted sources such as uploaded files or web pages
- Organisations asked by a customer or investor how they have tested their AI features
What you get
Scoping that reflects the architecture
What the model can access, which tools it can call, and where untrusted content enters. The risk sits in the integration far more than in the model itself.
Testers who work on these systems
A conventional application tester without AI-specific experience will test the surrounding application well and miss the model-layer issues. We match on the latter.
Findings framed as containment
Since prompt injection has no complete fix, useful output focuses on limiting what a successful injection can reach and do.
Frequently asked questions
Is this different from a normal penetration test?
Substantially. A conventional test covers the application around the model — authentication, access control, the API layer — and should still happen. AI-specific testing covers what the model does with untrusted input, what it can be induced to reveal, and what tools it can be made to invoke. Most organisations building on LLMs need both, and they are usually separate engagements.
Can prompt injection be fixed?
Not eliminated. Instructions and data share a channel, so a model that reads attacker-controlled content can be influenced by it. Practical defence is containment rather than prevention: limiting what the model can access, requiring confirmation for consequential actions, treating model output as untrusted, and keeping privileges narrow. Any provider promising complete prevention is overselling.
We use a third-party model API rather than our own. Are we still exposed?
Yes, and this is the more common case. The provider secures the model; you remain responsible for what you feed it, what you let it reach, and what your application does with its output. Most real incidents in this area arise from integration decisions rather than from the model provider's infrastructure.
When should we test?
Before a feature reaches customers, and again after any change that widens what the model can access — a new tool, a new data source, a new integration. Adding capability to an agent changes the risk profile more than changing the model does.