Most LLM failures happen in the integration: RAG poisoning, unsafe output handling, leaked secrets, and “the model will behave” assumptions. We audit architecture, data flow, and control points.
RAG pipeline integrity and retrieval boundaries
Prompt construction and system prompt isolation
Output handling, validation, and downstream execution safety
Secrets management (prompts, tools, logs, traces)
Cost controls and abuse resistance
Permissions and tool access design
identify control points and trust boundaries
what can influence prompts and outputs
injection, leakage, and tool abuse
output gating and execution safety
concrete fixes tied to real failure modes
Integration vulnerabilities with reproducible inputs
Data leakage paths with evidence
Fix direction focused on gates and boundaries
Retest confirmation
Frequently Asked Questions
Key risks include data exposure (sending sensitive data like PII to third-party APIs), prompt injection, hallucination-driven logic errors, cost abuse, and dependency on external model availability. Without proper safeguards, LLMs can introduce serious security and reliability issues.
We trace all data flowing into LLM prompts and outputs. If sensitive data such as PII, API keys, or proprietary logic is being sent to external providers, we identify and flag these risks.
Yes—we evaluate custom and fine-tuned models for training data leakage, susceptibility to adversarial inputs, and whether sensitive information can be extracted from the model.
Yes—we assess risks such as vendor lock-in, model version instability, rate limit abuse, and unexpected cost increases. We also evaluate resilience if an LLM provider changes pricing or becomes unavailable.
We test whether your application validates LLM outputs before using them. If outputs like file paths, SQL queries, or API calls are executed without verification, we flag it as a critical vulnerability.
Yes—we test for vector database poisoning, retrieval manipulation, and whether malicious documents can influence the system to return unsafe or misleading outputs.
Typically 1–3 weeks depending on the complexity of the integration.
$8K–$20K depending on scope. Use our pricing calculator for accurate estimates.
A blockchain security audit firm with the goal of making the Web3 space more secure through innovative and effective solutions.
