Out Of Bounds Write in Buffalo with Dynamodb

An Out Of Bounds Write in a Buffalo application using DynamoDB typically originates from unchecked user input used as a key attribute or as part of a key expression, combined with unsafe type handling. Because DynamoDB is a managed NoSQL store, out-of-bounds writes do not manifest as classic memory corruption as in lower-level languages, but as logical data boundary violations that can overwrite items, corrupt index structures, or bypass expected partition-key cardinality limits.

In Buffalo, models are often bound directly from HTTP parameters. If input used for a DynamoDB key (e.g., a partition key or sort key) is not validated for length, format, or range, an attacker can supply values that exceed DynamoDB’s limits (e.g., attribute value size limits of 400 KB for an item, or restrictions on key schema). This can lead to writes that displace adjacent items in a sorted index or overwrite unintended items when keys collide due to truncation or hashing artifacts.

DynamoDB’s strongly consistent writes and conditional expressions can be bypassed if the application uses unchecked numeric or string inputs in condition expressions (e.g., expecting a numeric step but receiving a very large integer). This can cause writes to proceed past business-rule boundaries, such as updating account balances beyond allowed limits or allowing duplicate entries where uniqueness is enforced at the application layer only.

Moreover, because Buffalo encourages rapid prototyping with automatic parameter binding, developers might map request form values directly to DynamoDB attribute values without sanitization. For example, binding a user-controlled string to an attribute used as a sort key without length or pattern checks can result in oversized key values or injection of reserved characters that affect query behavior. The scanner’s BOLA/IDOR and Input Validation checks are designed to surface these unchecked key usages, while Property Authorization and Unsafe Consumption checks highlight missing authorization around who can trigger these writes.

Remediation focuses on strict input validation, bounded key construction, and conditional writes to enforce business rules. Always validate and sanitize user input before using it as a DynamoDB key attribute in Buffalo handlers.

Example: Safe key construction with validation

// In a Buffalo action, validate and sanitize before writing to DynamoDB
import (
  "github.com/gobuffalo/buffalo"
  "github.com/aws/aws-sdk-go/aws"
  "github.com/aws/aws-sdk-go/service/dynamodb"
  "github.com/aws/aws-sdk-go/service/dynamodb/dynamodbattribute"
  "errors"
  "regexp"
  "strings"
)

func CreateItem(c buffalo.Context) error {
  type Payload struct {
    PartitionKey string `json:"partition_key"`
    SortKey      string `json:"sort_key"`
    Data         string `json:"data"`
  }

  var p Payload
  if err := c.Bind(&p); err != nil {
    return c.Render(400, r.JSON(map[string]string{"error": "invalid payload"}))
  }

  // Validate partition key: alphanumeric, max 10 chars for this example schema
  if !regexp.MustCompile(`^[A-Za-z0-9]{1,10}$`).MatchString(p.PartitionKey) {
    return c.Render(422, r.JSON(map[string]string{"error": "invalid partition_key"}))
  }

  // Validate sort key: no control chars, max length 256
  if len(p.SortKey) > 256 || strings.ContainsAny(p.SortKey, "\x00\x0a\x0d") {
    return c.Render(422, r.JSON(map[string]string{"error": "invalid sort_key"}))
  }

  svc := dynamodb.New(session.New())
  item, err := dynamodbattribute.MarshalMap(map[string]interface{}{
    "pk":   p.PartitionKey,
    "sk":   p.SortKey,
    "data": p.Data,
  })
  if err != nil {
    return c.Render(500, r.JSON(map[string]string{"error": "failed to marshal"}))
  }

  input := &dynamodb.PutItemInput{
    TableName: aws.String("MyTable"),
    Item:      item,
    ConditionExpression: aws.String("attribute_not_exists(pk) AND attribute_not_exists(sk)"),
  }

  _, err = svc.PutItem(input)
  if err != nil {
    return c.Render(500, r.JSON(map[string]string{"error": "write failed"}))
  }

  return c.Render(201, r.JSON(map[string]string{"status": "created"}))
}

The above ensures keys conform to expected format and size, preventing oversized writes that could violate DynamoDB item size limits or cause logical collisions. The ConditionExpression enforces uniqueness at the database level, mitigating race conditions where an unchecked write could overwrite an existing item.

Safe update with numeric guardrails

// Example: bounded numeric update to prevent overflow writes
func UpdateBalance(c buffalo.Context) error {
  type Payload struct {
    UserID string `json:"user_id"`
    Delta  int64  `json:"delta"`
  }
  var p Payload
  if err := c.Bind(&p); err != nil {
    return c.Render(400, r.JSON(map[string]string{"error": "invalid payload"}))
  }

  // Enforce business guardrails: delta must be within allowed adjustment range
  const maxDelta = 10000
  const minDelta = -10000
  if p.Delta > maxDelta || p.Delta < minDelta {
    return c.Render(422, r.JSON(map[string]string{"error": "delta out of allowed range"}))
  }

  svc := dynamodb.New(session.New())
  tableName := "Accounts"
  key, _ := dynamodbattribute.MarshalMap(map[string]string{"user_id": p.UserID})

  // Conditional update to ensure balance does not underflow/overflow beyond app limits
  updateInput := &dynamodb.UpdateItemInput{
    TableName: aws.String(tableName),
    Key:       key,
    UpdateExpression: aws.String("SET balance = balance + :delta"),
    ConditionExpression: aws.String("balance >= :min_balance AND balance + :delta <= :max_balance"),
    ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
      ":delta":    {N: aws.String(strconv.FormatInt(p.Delta, 10))},
      ":min_balance": {N: aws.String(strconv.FormatInt(0, 10))},
      ":max_balance": {N: aws.String(strconv.FormatInt(1_000_000, 10))},
    },
    ReturnValues: aws.String("UPDATED_NEW"),
  }

  _, err := svc.UpdateItem(updateInput)
  if err != nil {
    if awsErr, ok := err.(awserr.Error); ok && awsErr.Code() == dynamodb.ErrCodeConditionalCheckFailedException {
      return c.Render(409, r.JSON(map[string]string{"error": "balance constraint violated"}))
    }
    return c.Render(500, r.JSON(map[string]string{"error": "update failed"}))
  }

  return c.Render(200, r.JSON(map[string]string{"status": "updated"}))
}

These patterns align with the scanner’s checks: BOLA/IDOR and Input Validation ensure keys are bounded and authorized; Property Authorization confirms that only permitted actors can trigger writes; and the scans will flag missing condition expressions or unchecked numeric inputs that could lead to privilege escalation or data corruption.