Are you wondering whether a language model that calls another model could inadvertently expose or corrupt your BigQuery data, creating a prompt-injection risk? The question is no longer hypothetical. When a primary LLM hands off a request to a secondary “nested” agent, the original prompt can be altered, expanded, or corrupted, creating a classic prompt-injection risk that reaches deep into your analytics warehouse.
In many organizations, engineers embed AI assistants directly into data pipelines. The assistant runs a query against BigQuery, receives a result, and then passes the result to a downstream model for summarisation, anomaly detection, or report generation. The downstream model receives the raw result as part of its prompt. If an attacker can influence the downstream model’s prompt, by injecting malicious instructions, crafted data, or even a hidden token, the downstream model may issue new queries, exfiltrate data, or modify tables without the original engineer’s knowledge.
This chain of calls is called a nested‑agent workflow. It is attractive because it lets teams compose sophisticated AI‑driven analytics without writing glue code. The downside is that each hop adds a new surface for prompt-injection risk. The original request is no longer the sole authority; the downstream agent can reinterpret or extend the prompt in ways that bypass existing safeguards.
Why the current setup leaves you exposed to prompt-injection risk
Most teams build nested agents on top of existing credentials that have broad, standing access to BigQuery. A service account may have read‑write rights across many datasets, and that account key is stored in a CI secret manager. The primary LLM authenticates with the service account, runs a query, and hands the raw rows to the secondary model. Because the secondary model talks directly to BigQuery using the same credentials, the following conditions hold:
- Direct connection. The request reaches the data store without an intervening enforcement point.
- No per‑query audit. The BigQuery audit logs capture the identity that ran the query, but they cannot distinguish whether the query originated from a human, a first‑stage model, or a maliciously altered prompt.
- No inline data masking. Sensitive columns (PII, financial figures) are returned in clear text to the downstream model, which may inadvertently embed them in a generated response.
- No just‑in‑time approval. A query that touches a high‑value table is executed automatically, even if the downstream model’s intent is to exfiltrate data.
These gaps constitute the precondition that this post addresses: the environment already authenticates agents and grants them access, but it does not interpose any control that can observe, modify, or halt a request once it leaves the initial model. In other words, the request still reaches BigQuery directly, with no audit, no masking, no approval, and no way to block a malicious query that originated from a compromised prompt.
How a data‑path gateway can break the chain
To mitigate prompt-injection risk in nested‑agent workflows, the enforcement point must sit where the request passes from the model to BigQuery. That is the role of a Layer 7 gateway that proxies the connection, inspects the payload, and applies policies before the query is sent downstream. By placing the gateway in the data path, you gain three essential capabilities:
- Session recording. hoop.dev records every query and response, creating a replayable audit trail that shows exactly which prompt generated which query.
- Inline masking. hoop.dev can redact or replace sensitive fields in query results before they reach the secondary model, preventing accidental leakage.
- Just‑in‑time approval. hoop.dev can require a human reviewer to approve any query that matches a high‑risk pattern, such as SELECT * FROM a protected table, even if the request originates from an AI agent.
These enforcement outcomes exist only because hoop.dev sits in the data path. The identity verification step, whether via OIDC, SAML, or service‑account tokens, decides who may start a session, but it does not enforce what the session can do. hoop.dev is the only component that can block a malicious query, mask a column, or require an approval after the identity check has succeeded.