# 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.