Hallucination Attacks in Echo Go with Mongodb

In an Echo Go service that uses MongoDB as the primary data store, hallucination attacks occur when an AI component or an indirect integration fabricates or misrepresents database content. This can happen when an LLM is used to generate natural-language explanations, summaries, or suggested queries without direct validation against the actual MongoDB documents. Because Echo Go often binds HTTP routes to business logic that queries MongoDB, any AI-assisted construction of queries or responses may introduce inconsistencies between what is returned to the user and what exists in the database.

For example, an AI helper might produce a MongoDB query based on user intent but incorrectly infer field names, types, or expected values. If the application trusts this AI-generated input and passes it to MongoDB without strict validation, the query may match unintended documents or fail to enforce intended filters, leading to over-permissive results or data exposure. In a worse scenario, the AI may hallucinate the existence of fields or metadata that do not exist, causing the application to behave as if the database contains sensitive or structured information that is not present, which can mislead both users and downstream systems.

Echo Go applications that integrate AI for query construction or response generation are particularly vulnerable when MongoDB is used without strict schema enforcement or input sanitization. Because MongoDB is schema-less by default, it does not inherently prevent mismatches between expected and actual data shapes. If an AI component suggests a query like { status: "active", role: "admin" } but the actual documents store roles as "administrator" or use nested fields like permissions.role, the query may return empty results while the AI continues to hallucinate that data is present. This mismatch can be exploited to infer information about data existence or to manipulate user trust in AI-generated outputs.

Another vector involves AI-generated text that references specific MongoDB document IDs or inferred relationships that do not exist. If an Echo Go endpoint uses AI to summarize or link records based on hallucinated ObjectId values or assumed connections, clients may be presented with logically inconsistent navigation paths or references. Because MongoDB does not enforce referential integrity by default, such hallucinations can persist across requests, especially if caching or client-side rendering layers assume correctness. Attackers can probe these endpoints to identify inconsistencies and determine whether the system relies on unverified AI assumptions about stored data.

LLM security checks in middleBrick are designed to detect scenarios where AI components might hallucinate about data existence, structure, or access. These checks include system prompt leakage detection, active prompt injection testing, and output scanning for fabricated content such as fake ObjectIds, incorrect permissions, or non-existent collections. When integrated into an Echo Go workflow that uses MongoDB, such checks help surface cases where the application or its AI dependencies invent or misrepresent database state, allowing developers to enforce stricter validation before data is presented to users.

To mitigate hallucination risks in Echo Go with MongoDB, apply strict validation and schema-aware handling before any database interaction. Avoid constructing queries or filters based on AI-generated text without explicit type and existence checks. Use strongly typed structures and validate all inputs against expected formats using Go structs and MongoDB schema validation where applicable.

Below are concrete, working MongoDB code examples for Echo Go that demonstrate secure handling of user input and AI-assisted content.

1. Validate AI-Generated Query Fields Against a Whitelist

Do not allow raw field names from AI suggestions. Instead, compare against a known set of valid fields.

package main

import (
    "context"
    "net/http"
    "strings"

    "go.mongodb.org/mongo-driver/bson"
    "go.mongodb.org/mongo-driver/mongo"
    "go.mongodb.org/mongo-driver/mongo/options"
)

var validFields = map[string]bool{
    "status": true, "role": true, "name": true, "email": true,
}

func handleQueryUsers(c *echo.Context) error {
    userInput := c.QueryParam("filter") // e.g., "status: active, role: admin"
    filters, err := parseUserInput(userInput, validFields)
    if err != nil || len(filters) == 0 {
        return c.JSON(http.StatusBadRequest, map[string]string{"error": "invalid filter"})
    }

    client, _ := mongo.Connect(context.TODO(), options.Client().ApplyURI("mongodb://localhost:27017"))
    coll := client.Database("appdb").Collection("users")

    var results []bson.M
    cursor, err := coll.Find(context.TODO(), filters)
    if err != nil {
        return c.JSON(http.StatusInternalServerError, map[string]string{"error": "db error"})
    }
    defer cursor.Close(context.TODO())
    cursor.All(context.TODO(), &results)
    return c.JSON(http.StatusOK, results)
}

func parseUserInput(input string, allowed map[string]bool) (bson.D, error) {
    var doc bson.D
    parts := strings.Split(input, ",")
    for _, p := range parts {
        kv := strings.SplitN(strings.TrimSpace(p), ":", 2)
        if len(kv) != 2 {
            continue
        }
        key := strings.TrimSpace(kv[0])
        val := strings.TrimSpace(kv[1])
        if !allowed[key] {
            return nil, &ValidationError{msg: "invalid field"}
        }
        doc = append(doc, bson.E{Key: key, Value: val})
    }
    return doc, nil
}

type ValidationError struct{ msg string }
func (e *ValidationError) Error() string { return e.msg }

2. Enforce Schema Validation on Collection Level

Define JSON Schema rules in MongoDB to reject malformed or hallucinated documents that AI components might otherwise accept.

db.createCollection("users", {
   validator: {
      $jsonSchema: {
         bsonType: "object",
         required: ["status", "role"],
         properties: {
            status: { bsonType: "string", enum: ["active", "inactive", "pending"] },
            role: { bsonType: "string", enum: ["user", "administrator"] },
            email: { bsonType: "string", pattern: "^.+@.+$" }
         }
      }
   }
})

3. Use Typed Structs and Explicit Mapping

Do not unmarshal AI-generated text directly into MongoDB queries. Use explicit structs and bind only validated fields.

type UserFilter struct {
    Status string `json:"status" validate:"oneof=active inactive pending"`
    Role   string `json:"role" validate:"oneof=user administrator"`
}

func applyFilter(filter UserFilter) bson.D {
    var cond bson.D
    if filter.Status != "" {
        cond = append(cond, bson.E{Key: "status", Value: filter.Status})
    }
    if filter.Role != "" {
        cond = append(cond, bson.E{Key: "role", Value: filter.Role})
    }
    return cond
}

By combining input whitelisting, server-side schema validation, and typed Go structures, Echo Go services can safely interact with MongoDB while minimizing the risk of hallucination-based inconsistencies or exposure of non-existent data.