RAG Applications
Retrieval-augmented applications connect model behavior to private documents and enterprise data. Their security depends on whether retrieval boundaries hold under adversarial conversation.
What can go wrong
Cross-tenant retrieval leakage
A crafted request causes the application to retrieve context belonging to another user, tenant, or access tier.
Context-window disclosure
The model reveals hidden retrieved passages, source metadata, or surrounding context that should not appear in the answer.
Retrieval-boundary manipulation
Adversarial framing expands the search beyond the sources and scope intended for the requesting user.
Indirect instruction execution
Instructions embedded in retrieved content alter the model's behavior when that content enters the conversation context.
Illustrative attack path
This is a representative validation target, not a claim that every customer system exposes the same chain.
What Oxyne tests
How it works
Connect the RAG application's supported chat or API interface. Oxyne runs controlled conversations that probe retrieval scope, source disclosure, and cross-tenant data boundaries, then scores the resulting transcript against explicit success criteria.
Scoped interface
Define supported connection paths, authorized actions, credentials, and prohibited behavior before testing.
Evidence produced
Receive the supporting transcript, explicit success criteria, judge reasoning, validation level, and remediation context.
Applicable workflows
Use recurring AI Security Testing for baseline coverage and a separately scoped AI Red Teaming campaign for deeper analysis.
See Oxyne on your own systems.
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