Llm Data Leakage in Gin with Cockroachdb

When building a Go API with the Gin framework and CockroachDB as the backend, an LLM data leakage risk arises if application logic inadvertently exposes sensitive data through prompts, responses, or logs that an LLM endpoint might ingest. CockroachDB’s distributed SQL semantics and strong consistency do not inherently prevent leakage, but the way developers model data access in Gin handlers can create pathways for sensitive information to reach LLM-facing endpoints.

Consider a Gin route that queries CockroachDB for user details and then forwards those details to an LLM service for analysis or summarization. If the handler constructs a prompt using raw database fields such as email, phone, or internal identifiers without applying strict filtering, those fields can be exposed to the LLM. This becomes a data exposure vector, particularly when the LLM endpoint is unauthenticated or when output scanning is not enforced. middleBrick’s LLM/AI Security checks detect such exposures by scanning for PII and secrets in LLM responses and by testing for system prompt leakage across common chat formats (ChatML, Llama 2, Mistral, Alpaca).

Insecure use of context cancellation or timeouts can also contribute to leakage. For example, if a Gin handler starts a CockroachDB query and then passes the request context to an LLM call, premature cancellation might leave partial results or error details in logs that an attacker could later probe. Because middleBrick tests for unsafe consumption patterns and excessive agency (e.g., tool_calls or function_call patterns in LLM integrations), it can flag scenarios where CockroachDB query results are handed to an LLM with insufficient validation or authorization checks.

The interaction with CockroachDB’s secondary indexes and distributed joins can amplify risk if the API surface reflects internal table structures directly in error messages or debug output. An attacker might trigger edge cases that return stack traces or constraint violations containing schema details, which could be forwarded to an LLM for troubleshooting. middleBrick’s input validation and property authorization checks help surface such issues by correlating OpenAPI/Swagger specs (with full $ref resolution) against runtime behavior under unauthenticated conditions.

Additionally, rate limiting and data exposure checks are critical in this stack. Without proper rate limiting in Gin, an attacker could flood endpoints that query CockroachDB and trigger verbose error responses that include data fragments. middleBrick’s rate limiting and data exposure tests simulate such conditions to verify whether LLM-facing components inadvertently echo database content.

To mitigate LLM data leakage when using Gin with CockroachDB, enforce strict separation between database models and LLM prompts, and apply least-privilege data selection. Below are concrete, working examples that demonstrate secure patterns.

1. Parameterized queries with explicit field selection

Always select only the fields you need and use placeholders to avoid SQL injection and accidental data exposure.

rows, err := db.Query(ctx, `SELECT id, username, display_name FROM users WHERE id = $1`, userID)
if err != nil {
    c.JSON(500, gin.H{"error": "internal_server_error"})
    return
}
defer rows.Close()
var user User
for rows.Next() {
    if err := rows.Scan(&user.ID, &user.Username, &user.DisplayName); err != nil {
        c.JSON(500, gin.H{"error": "internal_server_error"})
        return
    }
}
// Only pass safe, non-sensitive fields to an LLM if necessary
prompt := fmt.Sprintf("Summarize profile for user %s", user.DisplayName)

2. Context timeouts and cancellation safety

Use dedicated timeouts for database and LLM calls to avoid leaking partial data through context propagation.

ctx, cancel := context.WithTimeout(req.Context(), 8*time.Second)
defer cancel()

err := db.SelectContext(ctx, &users, "SELECT id, username, hashed_email FROM users WHERE team_id = $1", teamID)
if err != nil {
    // Log safely without exposing query internals or data
    c.JSON(500, gin.H{"error": "request_timeout"})
    return
}

3. Input validation and property authorization

Validate IDs and enforce ownership before querying CockroachDB to ensure BOLA/IDOR protections.

type TeamParams struct {
    TeamID int64 `uri:"team_id" binding:"required,min=1"`
    UserID int64 `uri:"user_id" binding:"required,min=1"`
}
var params TeamParams
if err := c.ShouldBindUri(&params); err != nil {
    c.JSON(400, gin.H{"error": "invalid_parameters"})
    return
}

if !userCanAccessTeam(params.UserID, params.TeamID) {
    c.JSON(403, gin.H{"error": "forbidden"})
    return
}

4. Safe error handling and logging

Avoid echoing SQL errors or schema details in responses. Standardize error messages and scrub logs.

err := db.GetContext(ctx, &record, "SELECT id, name FROM records WHERE id = $1", reqID)
if err != nil {
    if errors.Is(err, sql.ErrNoRows) {
        c.JSON(404, gin.H{"error": "not_found"})
    } else {
        // Log the error ID for internal tracing without exposing details
        log.Printf("db_error: trace_id=%s", traceID)
        c.JSON(500, gin.H{"error": "internal_server_error"})
    }
    return
}

5. Guarding LLM prompts and outputs

When integrating with LLMs, sanitize inputs and outputs and avoid forwarding raw database rows.

// Build prompt from sanitized data only
prompt := fmt.Sprintf("User %s (id: %d) requests assistance.", sanitizedName, userID)
// Do not include email, phone, or internal UUIDs in the prompt

// If calling an LLM endpoint, use structured requests and enable output scanning
llmResp, err := callLLMEndpoint(ctx, prompt)
if err != nil {
    c.JSON(502, gin.H{"error": "llm_unavailable"})
    return
}
// Validate llmResp for PII or secrets before any further use

By combining these patterns, you reduce the attack surface that could lead to LLM data leakage and align the Gin+CockroachDB stack with secure development practices. middleBrick’s CLI can help validate these controls by scanning your endpoints for insecure data handling and LLM exposure risks.