Insecure Deserialization in Fiber with Firestore

Insecure deserialization occurs when an application accepts untrusted serialized data and reconstructs objects from it without sufficient validation. In a Fiber application that uses Firestore as a backend, risk arises when endpoint inputs (e.g., JSON payloads, URL parameters, or headers) are deserialized into Go structs and then used to build Firestore queries or document updates. If the input is not strictly validated and sanitized before interacting with Firestore, an attacker can supply crafted payloads that cause the application to deserialize malicious objects, potentially altering query behavior or triggering unintended logic when documents are read or written.

Consider a handler that retrieves a Firestore document ID from a deserialized request object:

type RequestPayload struct {
    DocumentID string `json:"document_id"
}

func GetDocument(c *fiber.Ctx) error {
    var payload RequestPayload
    if err := c.BodyParser(&payload); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid payload"})
    }
    docRef := client.Collection("items").Doc(payload.DocumentID)
    snap, err := docRef.Get(c.Context())
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "failed to fetch document"})
    }
    return c.JSON(snap.Data())
}

If DocumentID is not validated (type, length, allowed characters), an attacker may supply paths or object references that lead to sensitive collections, or exploit polymorphic deserialization in client-side code that later sends manipulated data to this endpoint. Another scenario involves accepting serialized objects from clients to build Firestore writes:

type UpdatePayload struct {
    Updates map[string]interface{} `json:"updates"
}

func UpdateDocument(c *fiber.Ctx) error {
    var payload UpdatePayload
    if err := c.BodyParser(&payload); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid payload"})
    }
    _, err := client.Collection("items").Doc(payload.DocumentID).Set(c.Context(), payload.Updates)
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "write failed"})
    }
    return c.SendStatus(fiber.StatusOK)
}

Although this example uses a map, deserializing complex nested structures from untrusted sources can result in objects that, when marshaled for Firestore, expose sensitive fields or bypass intended validation layers. Attack patterns relevant here include injection via maliciously crafted serialized objects (e.g., overriding type information to inject unexpected keys), and abuse of Firestore rules that may implicitly trust document paths derived from deserialized input. Because Firestore operations are rule-driven, unchecked deserialized inputs can lead to privilege escalation or data exposure when rules inadvertently grant broader access than intended.

The LLM/AI Security checks in middleBrick specifically test for prompt injection and output risks, which are orthogonal to deserialization but highlight the importance of validating all external inputs before they influence backend services like Firestore. Insecure deserialization in Fiber interacting with Firestore is a high-severity concern because it can lead to unauthorized data access or manipulation, and it often requires minimal attacker knowledge if input validation is weak.

Remediation focuses on strict input validation, avoiding direct deserialization of untrusted data into executable objects, and enforcing least privilege Firestore rules. Prefer explicit field extraction over generic deserialization, and use Firestore document IDs that are validated for format and length.

1) Validate and sanitize document IDs instead of accepting raw serialized identifiers:

func GetDocumentSafe(c *fiber.Ctx) error {
    docID := c.Params("docID")
    if docID == "" || len(docID) > 500 {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid document identifier"})
    }
    // Allow only alphanumeric, hyphen, underscore
    matched, _ := regexp.MatchString(`^[a-zA-Z0-9_-]+$`, docID)
    if !matched {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid document identifier"})
    }
    docRef := client.Collection("items").Doc(docID)
    snap, err := docRef.Get(c.Context())
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "failed to fetch document"})
    }
    return c.JSON(snap.Data())
}

2) For updates, explicitly whitelist allowed fields and avoid binding raw deserialized maps directly to Firestore writes:

allowedFields := map[string]bool{
    "name": true, "description": true, "quantity": true,
}

func UpdateDocumentSafe(c *fiber.Ctx) error {
    var raw map[string]interface{}
    if err := c.BodyParser(&raw); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid payload"})
    }
    filtered := make(map[string]interface{})
    for k, v := range raw {
        if allowedFields[k] {
            filtered[k] = v
        }
    }
    docID := c.Params("docID")
    if docID == "" {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "missing document identifier"})
    }
    _, err := client.Collection("items").Doc(docID).Set(c.Context(), filtered)
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "write failed"})
    }
    return c.SendStatus(fiber.StatusOK)
}

3) Enforce Firestore security rules that limit write access by document owner and validate data types server-side. For example, in Firestore rules, require that modified fields exist in the allowed set and that numeric fields are within expected ranges. These rules act as a final safeguard when application-layer validation is in place.

4) Use middleware to sanitize inputs globally and log suspicious deserialization attempts, integrating monitoring to detect patterns consistent with injection attempts. middleBrick scans can be incorporated into your workflow; the CLI (middlebrick scan <url>) and GitHub Action help detect insecure deserialization patterns in exposed endpoints, while the Dashboard lets you track security scores over time and fail CI/CD gates when risk thresholds are exceeded.

By combining strict input validation, explicit field filtering, and tightly scoped Firestore rules, you reduce the attack surface for insecure deserialization in Fiber applications interacting with Firestore.