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A text-to-SQL agent may propose tables before the database evaluates a query. That is not itself the security failure: the failure is letting the model’s table choices, filters, or instructions decide what the caller is authorized to see. Bind the caller’s identity in trusted application or database context, narrow the agent’s tools and database permissions, and enforce access before data is returned or changed.
Why the agent’s own instructions cannot authorize a query
A text-to-SQL agent can generate a query that names a table containing records for many customers, then omit or alter a tenant filter. Even if the prompt says to return only the current user’s records, the prompt is guidance to the model—not an access-control boundary.
Google Cloud’s guidance for securing agent interactions with Model Context Protocol states: “Instructing the agent to enforce the access rules is typically not sufficient to protect data.” Its unsafe example gives an agent a general SQL tool for a table containing all users’ orders. Its safer pattern uses a purpose-built lookup tool whose user identity is set outside the agent’s control.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThe relevant security property is not that authorization must run before the model proposes table names. It is that untrusted query selection cannot expand the caller’s allowed access, and that a trusted layer rejects unauthorized reads or writes before the database returns or changes data.
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What should happen between a request and a database result?
- Authenticate the caller outside the model. The application verifies the human or service identity and stores a stable user or tenant identity in trusted request state.
- Limit what the agent can ask for. Prefer task-specific tools—such as “list this caller’s invoices”—over an unrestricted
execute_sqltool. The backend should bind the identity and permitted scope; the model must not be responsible for supplying or preserving a tenant ID. - Apply database permissions. Use a database role with only the operations and objects the task requires. Keep migration, administration, and other privileged credentials off the ordinary request path, and use separate credentials where trust levels differ.
- Enforce row access in the database when appropriate. For shared tables, use the database’s row-policy mechanism where it fits the design. Confirm which roles are subject to those policies and how identity context reaches the database.
- Validate generated SQL as an additional guardrail. Parse queries and allowlist accessible schemas or tables; reject unsupported constructs when applicable. These checks can catch mistakes, but they do not replace database authorization.
- Return only the authorized result. Ensure the execution path cannot switch to a broader role, connection, or tool after validation.
There is no universal query-rewriting order that fits every database and tenancy architecture. The essential invariant is that the database operation is constrained by trusted caller identity and permissions, regardless of which tables the model proposed.
Choose the tool boundary before giving the agent general SQL
Purpose-built tools
A narrow tool can encode both the operation and its scope: for example, a backend function that fetches orders for the authenticated caller. The backend derives the identity from trusted request state rather than accepting an agent-generated tenant identifier. This reduces the number of query shapes the model can request and makes authorization logic easier to inspect and test.
General SQL tools
A general SQL tool can support more flexible analysis, but it exposes a larger surface: table selection, joins, subqueries, and possibly writes. If the use case requires it, restrict the database role, constrain schemas and tables, validate SQL, and enforce row or object permissions at the database boundary. A parser or prompt alone is not a substitute for those controls.
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OWASP’s Database Security Cheat Sheet recommends least privilege and describes granular permissions at database, table, column, and row levels, as well as restricted views that prevent access to underlying base tables. OWASP’s Secure Database Access guidance also covers parameterization, validation, stored procedures, least privilege, and using credentials appropriate to each trust distinction.
When row-level security helps—and what it does not guarantee
Row-level security (RLS) can make the database apply tenant restrictions to rows, rather than relying on the model to add a correct filter to every query. It is especially useful when tenants share tables and a policy can reliably associate each operation with the authenticated caller’s permitted rows.
PostgreSQL
PostgreSQL 18 documents that, when row security is enabled, normal row access must be allowed by a policy; if no policy allows access, the default is to deny rows. However, table owners are typically exempt from row-security policies. An application that connects as the owner may therefore bypass the protection it expected. Verify the exact role, ownership, and policy behavior used by the application rather than assuming that enabling RLS constrains every connection.
Rank #3
SQL Server
SQL Server’s row-level security uses filter predicates to filter rows from reads and block predicates to reject writes that violate a policy. Those are SQL Server mechanisms; their behavior should not be generalized to other database engines.
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A row policy is only useful if its decision is based on trustworthy identity context. The model should not be able to set that context to another tenant. Check how the application passes identity to the database, how connection reuse is handled, and whether a privileged role or alternate execution path can bypass the policy. Exact syntax and bypass semantics depend on the engine and configuration.
Pick a tenant-isolation design that matches the risk and workload
OWASP’s Multi Tenant Security Cheat Sheet describes separate databases, separate schemas, shared tables with row-level security, and hybrid arrangements. No option is a universal winner. Compare the boundary you need with operational overhead and the effort required to prove that missing or invalid identity fails closed.
Rank #4
| Design | Boundary characteristics | Operational and implementation trade-offs |
|---|---|---|
| Separate databases | Can provide stronger separation of data and credentials between tenants, depending on deployment, network, and backup design. | More database provisioning, maintenance, migrations, and cost; tenant routing and backup handling must be managed. |
| Separate schemas | Separates tenant objects within a database, but isolation depends on grants, schema selection, and preventing cross-schema access. | Requires careful grant and search-path controls, plus migration and testing discipline across schemas. |
| Shared tables with row-level policies | Uses database policies to restrict rows in common tables; the strength depends on correct identity binding and policy coverage. | Can simplify shared schema operations, but policy behavior, role exemptions, joins, views, and write paths need thorough testing. |
| Hybrid | Combines approaches—for example, grouping tenants into databases while applying row policies within each group. | Can fit distinct risk or workload tiers, but adds routing and operational complexity across the chosen boundaries. |
For each candidate, assess credential, network, and backup isolation; grant and schema complexity; migration burden; whether every request is attributable to the caller; and how readily negative tests can show that absent or invalid identity is denied.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use SQL parsing and allowlists as defense in depth
Application-level checks can reject malformed or out-of-scope SQL before execution. Useful controls may include parsing rather than matching query text, limiting accessible schemas and tables, and rejecting unsupported statement types or constructs for the task. The precise rules depend on the SQL dialect and the agent’s capabilities.
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Apache Airflow’s agent security guidance describes SQL parsing and table checks as strong application-level guardrails, while identifying a least-privilege database role as the security boundary that still applies if parser checks fail. This distinction matters: validation narrows what the agent is likely to execute, while database grants and policies constrain what the execution credential can actually access.
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Test the denial paths, not just successful queries
A test that confirms the agent can retrieve its own tenant’s data does not establish isolation. Build negative tests around the actual database role, tools, views, and policy context used in production.
- Attempt to retrieve a known row belonging to a different tenant.
- Omit, corrupt, or substitute identity context and verify that access fails closed.
- Try joins, subqueries, aggregates, and views that could expose cross-tenant data indirectly.
- Exercise writes as well as reads where the agent can modify data.
- Verify that the ordinary application role cannot use owner, administrator, superuser, or policy-bypass privileges.
- Repeat relevant checks across alternate tools, pooled connections, and elevated execution paths.
Tailor the matrix to the database engine and exposed tools. A policy that protects direct reads, for example, should not be assumed to cover every view, procedure, or privileged path without verification.
How to decide whether the design is safe enough
Ask whether a malicious or mistaken model output can cause the request’s database credential to read or change another tenant’s data. If the answer depends on the model remembering a prompt instruction or adding the right filter, the authorization boundary is in the wrong place. Make identity and scope trusted inputs, keep the agent’s capabilities narrow, and let database permissions and policies enforce the limits that remain even when query generation or validation goes wrong.
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