LLM Integration Security Testing
We assess RAG pipelines, embeddings, fine-tuning workflows, and LLM API usage with scoped, non-disruptive testing. You get clear findings and practical fixes without guesswork.
Fixed scope, agreed testing windows, and no production disruption.
What We Test
Real-world misuse often looks like ordinary inputs, documents, or API calls. We model those behaviors end-to-end so you can see how your LLM stack responds under realistic conditions.
RAG Pipeline Security
Testing retrieval-augmented generation systems for injection, poisoning, and data leakage.
- Document injection paths
- Retrieval manipulation
- Context window overflow
- Source attribution spoofing
Embedding & Vector Store Security
Testing embedding generation and vector database security.
- Embedding poisoning
- Similarity search manipulation
- Access control on vector stores
- Metadata leakage
LLM API Security
Testing the security of your LLM API integrations.
- API key management
- Rate limiting and abuse prevention
- Input/output filtering
- Cost manipulation risks
Fine-tuning & Training Data
Testing the security of custom model training workflows.
- Training data poisoning
- Model extraction risks
- Checkpoint security
- Inference manipulation
Guardrails & Output Filtering
Testing the effectiveness of safety measures.
- Jailbreak resistance
- Output filter bypass
- Content policy enforcement
- PII detection and redaction
What Findings Look Like
Findings explain how data moves through retrieval, prompts, and outputs, with clear remediation so your team can resolve issues without guesswork.
Prompt boundaries blur across retrieval contexts
Retrieved content can influence system instructions in certain flows, changing how the model handles user requests.
Resolution: Isolate system prompts, separate untrusted context, and add allowlist constraints for tool and prompt inputs.
Vector store access scope is broader than intended
Similarity search can return documents outside the expected tenant or project scope under edge filters.
Resolution: Enforce namespace scoping at the query layer and add explicit access checks for every retrieval.
LLM API keys exposed in build configuration
Keys used for model access are present in client build settings and reused across environments.
Resolution: Move keys to a server-side proxy, rotate existing keys, and add environment-specific secrets.
Output redaction misses sensitive identifiers
Summaries can include customer IDs or internal references that should be masked.
Resolution: Add structured redaction rules and automated tests for high-risk fields.
Frequently Asked Questions
What parts of the LLM stack do you need access to?
We typically need scoped access to the RAG pipeline, vector store, prompt construction, and the service that calls the model. Read-only code access plus a staging or test environment is usually enough. Production access is only used if you explicitly approve it.
Do you test the LLM provider or our integration?
We focus on your integration: how inputs are collected, how retrieval is performed, how prompts are built, and how outputs are handled. We do not test the LLM provider's infrastructure.
What will the report include?
You receive clear findings with evidence, impact context, and practical remediation steps. We also include reproduction notes so your engineers can validate fixes without guesswork.
How are testing windows and timelines handled?
Scope and timelines are agreed before we start. We test within the approved window and keep activity controlled and non-disruptive.
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Safe next step
Talk through your LLM integration scope.
No commitment required.
We will review your architecture, outline what we would test, and share a fixed-scope plan. You decide if and when to proceed.
Discuss your LLM scopeor view a sample report first