Api Key Exposure in Phoenix with Postgresql

When Phoenix applications interact with Postgresql, developers sometimes store API keys or database credentials directly in configuration files or environment variables that are accessible to the application runtime. If those keys are then passed into SQL queries—intentionally or accidentally—they can be exposed through logging, error messages, or insecure query construction. This becomes particularly risky when dynamic query building in Ecto or raw SQL strings embeds secrets without proper sanitization or separation of duties.

For example, consider a Phoenix endpoint that retrieves a service token from the database to call an external API and then logs the full query or response. If an attacker can trigger an error or inspect logs (for instance, through an insecure debug endpoint or verbose middleware), they might observe the API key in plaintext. This exposure can occur when developers use string interpolation to construct queries instead of parameterized statements, inadvertently placing secrets into query text that may be captured by instrumentation or stack traces.

middleBrick identifies this class of risk under Data Exposure and Input Validation checks, noting whether API keys or secrets appear in logs, error responses, or unparameterized queries. In a scan of a Phoenix + Postgresql stack, findings may highlight that a route directly interpolates user input into a query that fetches or logs credentials, bypassing expected isolation between data access and sensitive configuration. The LLM/AI Security checks further probe whether prompts or outputs might reveal these keys through generated code examples or debug traces, ensuring that even AI-assisted development does not amplify leakage paths.

Because middleBrick scans the unauthenticated attack surface, it can detect scenarios where an endpoint returns sensitive configuration or where an OpenAPI spec inadvertently documents parameters that expose keys. By cross-referencing the Postgresql interaction patterns defined in the Phoenix codebase with the runtime behavior observed during the scan, the tool surfaces concrete remediation guidance: prefer compile-time configuration for non-dynamic secrets, enforce parameterized queries, and ensure logging frameworks redact sensitive values before output.

To mitigate Api Key Exposure when using Postgresql in Phoenix, adopt strict separation between configuration, queries, and runtime data. Store static API keys in runtime environment variables or vault-backed configuration (e.g., via config/runtime.exs) and avoid persisting them in the database unless strictly necessary. When database-stored keys are required, ensure they are encrypted at rest and retrieved through isolated, audited queries that do not expose the key in logs or error messages.

Use Ecto's parameterized queries consistently to prevent injection and accidental logging of sensitive values. For example, instead of embedding values directly into fragments, bind them as parameters:

from(u in "users",
  where: u.service_name == ^service_name,
  select: u.api_key
)

If raw SQL is necessary, use Ecto.Adapters.SQL.query! with explicit parameter bindings:

Ecto.Adapters.SQL.query!(Repo, "SELECT api_key FROM services WHERE name = $1", [service_name])

Ensure that any logging or telemetry around these queries redacts the retrieved key. Configure your Logger to filter sensitive metadata and avoid inspecting full query strings in production:

config :logger, :console,
  format: "[$level] $message\n",
  metadata: [:request_id]

config :my_app, MyAppWeb.Endpoint,
  render_errors: [view: MyAppWeb.ErrorView, accepts: ~w(json), layout: false],
  filter_parameters: [:api_key, :secret]

For applications leveraging middleBrick, the Pro plan’s continuous monitoring can schedule regular scans to verify that no new endpoints inadvertently reintroduce key exposure, while the CLI provides quick feedback during development. If your workflow includes CI/CD, the GitHub Action can enforce that new changes do not introduce insecure patterns by failing builds when findings exceed your chosen threshold.