Graphql Batching in Buffalo

GraphQL batching allows clients to send multiple operations in a single HTTP request, reducing network overhead. In Buffalo applications, this feature becomes a security risk when the GraphQL handler processes each operation in a batch without applying per-operation security controls like rate limiting or authorization checks. Buffalo doesn't include built-in GraphQL support, so developers typically integrate libraries like graph-gophers/graphql-go and create custom handlers. If these handlers accept arrays of GraphQL requests without validation, attackers can bypass security boundaries.

A vulnerable Buffalo handler might look like this:

// handlers/graphql.go
func GraphQLHandler(c buffalo.Context) error {
    var requests []graphql.RawRequest
    if err := json.NewDecoder(c.Request().Body).Decode(&requests); err != nil {
        // Fallback to single request
        var single graphql.RawRequest
        if err2 := json.NewDecoder(c.Request().Body).Decode(&single); err2 != nil {
            return c.Error(400, errors.New("invalid request"))
        }
        requests = []graphql.RawRequest{single}
    }

    // Process all requests without per-operation checks
    for _, req := range requests {
        // Each req is executed with the same context
        result := schema.Exec(c.Request().Context(), req.Query, req.OperationName, req.Variables)
        // ... collect responses
    }
    return c.JSON(200, batchResponses)
}

This handler accepts both single and batched requests. The critical flaw is that HTTP-level rate limiting (e.g., via middleware like github.com/gorilla/csrf or custom logic) counts the entire batch as one request. An attacker can send 100 operations in one batch, each of which would normally be rate-limited, but now they all execute.

Attack scenarios specific to Buffalo apps:

• Rate limit bypass: Send 100 user(id: 1) { email } queries in one batch to harvest emails beyond the per-request limit.
• Amplified BOLA/IDOR: If the GraphQL resolver lacks proper authorization checks (a common BOLA pattern), a batch can access multiple resources. For example: [{"query": "query { user(id: 1) { ssn }"}, {"query": "query { user(id: 2) { ssn }"}].
• N+1 query amplification: Batch 50 queries that each trigger N+1 database queries, causing database saturation.
• Cost exploitation: Send computationally expensive operations (e.g., complex aggregations) in a batch to drain server resources without triggering rate limits.

Buffalo's flexibility with middleware and handlers makes this vulnerability easy to introduce inadvertently, especially when developers assume GraphQL libraries handle batching securely by default.

Detect GraphQL batching in Buffalo apps through manual testing or automated scanning. Manually, send a batch request and inspect the response structure and timing. A valid batch returns an array of responses, and processing time scales linearly with the number of operations.

Example batch test with curl:

curl -X POST https://your-buffalo-app.com/graphql \
  -H "Content-Type: application/json" \
  -d '[
        {"query": "query { user(id: 1) { name }"},
        {"query": "query { user(id: 2) { name }"}
      ]'

If the response is [{"data":{"user":{"name":"Alice"}}}, {"data":{"user":{"name":"Bob"}}}], batching is enabled. Also, time a batch of 10 operations vs. 1 operation; a 10x increase indicates sequential processing.

Use middleBrick for automated detection. Its Rate Limiting check specifically tests for batching by:

1. Sending a batch of operations (e.g., 5 identical queries).
2. Analyzing the response count and structure.
3. Comparing response times against single-operation baselines.
4. Checking if rate limit headers (e.g., X-RateLimit-Remaining) decrement by 1 instead of 5.

Scan a Buffalo GraphQL endpoint with middleBrick's web dashboard or CLI:

middlebrick scan https://your-buffalo-app.com/graphql

The report flags batching under the Rate Limiting category, showing:

• Severity: High (due to bypass potential).
• Evidence: Batch response array and linear time scaling.
• Remediation guidance tailored to Buffalo's handler pattern.

middleBrick also cross-references findings with your OpenAPI/Swagger spec (if available) to identify mismatches between documented and actual behavior.

Remediate based on whether batching is required. If not, reject array requests entirely. If batching is necessary (e.g., for a specific client), implement per-operation rate limiting and authorization within the batch loop.

Option 1: Reject batches (simplest). Modify your handler to only accept single operations:

// handlers/graphql.go
import (
    "encoding/json"
    "io"
    "errors"
)

func GraphQLHandler(c buffalo.Context) error {
    body, err := io.ReadAll(c.Request().Body)
    if err != nil {
        return c.Error(400, err)
    }

    // Detect if body is an array
    var raw interface{}
    if err := json.Unmarshal(body, &raw); err != nil {
        return c.Error(400, errors.New("invalid JSON"))
    }
    if _, ok := raw.([]interface{}); ok {
        return c.Error(400, errors.New("batching is not supported"))
    }

    var req graphql.RawRequest
    if err := json.Unmarshal(body, &req); err != nil {
        return c.Error(400, errors.New("invalid GraphQL request"))
    }

    // Process single request with existing rate limiting
    result := schema.Exec(c.Request().Context(), req.Query, req.OperationName, req.Variables)
    return c.JSON(200, result)
}

This uses io.ReadAll to inspect the request body before decoding, ensuring arrays are rejected early. Existing HTTP middleware rate limits now apply correctly.

Option 2: Per-operation checks in batches. If you must support batching, iterate over the batch and apply rate limiting and authorization per operation. Buffalo doesn't provide built-in per-operation rate limiting, so you'll need to integrate a token bucket or fixed-window counter:

// handlers/graphql.go
import "golang.org/x/time/rate"

var limiter = rate.NewLimiter(rate.Every(1*time.Second), 10) // 10 ops/sec

func GraphQLHandler(c buffalo.Context) error {
    var requests []graphql.RawRequest
    if err := json.NewDecoder(c.Request().Body).Decode(&requests); err != nil {
        // ... handle single request
    }

    responses := make([]interface{}, len(requests))
    for i, req := range requests {
        // Apply rate limit per operation
        if !limiter.Allow() {
            responses[i] = map[string]interface{}{
                "errors": []interface{}{
                    map[string]interface{}{
                        "message": "rate limit exceeded",
                    },
                },
            }
            continue
        }

        // Perform authorization check per operation (example: user ID from variables)
        if !authorizeUser(c, req.Variables) {
            responses[i] = map[string]interface{}{
                "errors": []interface{}{
                    map[string]interface{}{
                        "message": "unauthorized",
                    },
                },
            }
            continue
        }

        result := schema.Exec(c.Request().Context(), req.Query, req.OperationName, req.Variables)
        responses[i] = result
    }
    return c.JSON(200, responses)
}

func authorizeUser(c buffalo.Context, variables map[string]interface{}) bool {
    // Extract user ID from variables and check against c.
    // This is a simplified example; implement your logic.
    userID, ok := variables["id"].(string)
    if !ok {
        return false
    }
    // Compare with authenticated user from context
    authUserID := c.Value("user_id").(string)
    return userID == authUserID
}

Trade-offs:

After fixing, rescan with middleBrick to verify the batching issue is resolved and your Rate Limiting score improves. Also, review other findings like BOLA/IDOR that may be amplified by batching.