Mass Assignment in Fiber with Dynamodb

Mass assignment occurs when an API directly binds user-supplied JSON to a data model without filtering fields. In a Fiber-based service that uses AWS DynamoDB as the persistence layer, this becomes especially risky because the application may construct DynamoDB attribute-value objects from raw request bodies. If the server-side code copies all keys from the incoming JSON into a DynamoDB PutItem or UpdateItem request without allowlisting, an attacker can inject unexpected top-level or nested attributes. These can include metadata keys used by DynamoDB (such as __type or type-hint fields), condition expressions, or parameters intended for higher-level control, leading to privilege escalation, data tampering, or unintended side effects.

With DynamoDB, mass assignment can bypass intended access patterns and overwrite reserved or system attributes, and it can also enable injection of expressions that affect conditional writes. For example, if a request binds directly to a struct that is serialized into a DynamoDB map, an attacker might add keys that change the semantics of the operation. In a Fiber application, this commonly happens when developers use generic binding (e.g., ctx.BodyParser) into a struct or map and then forward that data to DynamoDB without sanitization. Because the scan checks for BOLA/IDOR and BFLA/Privilege Escalation across unauthenticated endpoints, it can flag endpoints where mass assignment intersects with DynamoDB write operations that lack proper authorization checks.

Consider a user profile update endpoint in Fiber that accepts JSON and writes to a DynamoDB table named users. If the handler does not restrict fields, an attacker can add an admin attribute or a version attribute that influences conditional updates. The scan’s Property Authorization and Input Validation checks will highlight whether field-level permissions and type constraints are enforced before data reaches DynamoDB. Because the scanner runs unauthenticated and tests input vectors, it can identify endpoints where mass assignment could be chained with DynamoDB’s conditional expressions to escalate privileges or leak data.

OpenAPI/Swagger analysis (2.0, 3.0, 3.1) helps by comparing the schema defined for request bodies against the shape of data sent to DynamoDB. If the spec defines a strict schema but the runtime payload includes extra properties that map into DynamoDB attribute names, the cross-reference will flag a discrepancy. This is valuable for LLM/AI Security as well: system prompt leakage patterns may appear in unexpected fields if user input is forwarded into DynamoDB attributes that later influence model prompts or responses. Overall, the combination of Fiber’s flexible routing, permissive binding, and DynamoDB’s schemaless attribute model amplifies mass assignment risk when authorization and input validation are not explicitly enforced.

To mitigate mass assignment in a Fiber application that writes to DynamoDB, explicitly allowlist fields before constructing DynamoDB attribute maps. Avoid binding directly into structs or maps that are forwarded to PutItem or UpdateItem. Instead, parse into a generic intermediate map, remove or ignore sensitive fields, and then build the DynamoDB expression attribute values manually. This ensures only intended attributes are written and prevents attackers from controlling condition expression keys or metadata fields.

Example: Unsafe mass assignment

// UNSAFE: direct binding into a map that is forwarded to DynamoDB
app.Post("/profile", func(c *fiber.Ctx) error {
    var input map[string]interface{}
    if err := c.BodyParser(&input); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": err.Error()})
    }
    // input may contain admin, version, conditionExpression keys
    av, err := dynamodbattribute.MarshalMap(input)
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": err.Error()})
    }
    _, err = svc.PutItem(context.TODO(), &dynamodb.PutItemInput{
        TableName: aws.String("users"),
        Item:      av,
    })
    return errResp(c, err)
})

Example: Safe remediation with explicit allowlist

// SAFE: allowlist fields and construct DynamoDB attribute values explicitly
type ProfileUpdate struct {
    Name  string `json:"name"`
    Email string `json:"email"`
    // do not include admin, version, or condition keys
}

app.Post("/profile", func(c *fiber.Ctx) error {
    var p ProfileUpdate
    if err := c.BodyParser(&p); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": err.Error()})
    }
    // Build condition expression safely if needed; do not forward user input into ExpressionAttributeNames/Values without validation
    av, err := dynamodbattribute.MarshalMap(map[string]interface{}{
        "name":  p.Name,
        "email": p.Email,
    })
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": err.Error()})
    }
    _, err = svc.PutItem(context.TODO(), &dynamodb.PutItemInput{
        TableName: aws.String("users"),
        Item:      av,
    })
    return errResp(c, err)
})

For updates that require conditional writes, validate and parameterize condition expression names and values instead of allowing user input to dictate them. Use the DynamoDB SDK’s expression builder to construct condition expressions with static attribute names, and only permit user input for values that are strictly validated and escaped. The scanner’s BFLA/Privilege Escalation and Property Authorization checks will highlight endpoints where condition logic or item ownership is missing or improperly enforced.

Additionally, enable server-side encryption and use IAM policies that follow least privilege for the DynamoDB table. While middleBrick does not fix or block findings, its detailed per-category breakdowns can guide you toward safer patterns: prioritize findings with severity High or Critical, and follow the remediation guidance to tighten input validation and property authorization before deploying changes. Using the CLI (middlebrick scan <url>) or GitHub Action can help you enforce a minimum security score before merges, and the Pro plan’s continuous monitoring can alert you if new mass assignment risks appear across API changes.