Hallucination Attacks in Echo Go with Firestore

Hallucination attacks in an Echo Go service that uses Firestore occur when an LLM or AI-driven handler generates plausible but incorrect or fabricated data, and that data is directly read from or written to Firestore without sufficient validation. In this setup, user input or indirect prompts can steer the LLM to produce responses that misrepresent document states, inject false references, or synthesize non-existent entity keys. Because Firestore serves as the source of truth, the application may unknowingly treat hallucinated document IDs or fields as valid, leading to inconsistent reads, unsafe fallbacks, or logic errors that propagate through the API response.

The vulnerability is amplified when Firestore document paths are constructed from LLM output without strict allowlists or schema checks. For example, if an LLM suggests a document ID such as users/abc123/profile based on patterns it has seen, but that document does not exist, Echo Go might still attempt to unmarshal it into a struct, returning zero values that the LLM then reinterprets as valid data. This creates a feedback loop where the model’s confidence increases despite the data being fictional. Additionally, if the service uses Firestore to store prompt templates or model metadata, malicious input can trigger path traversal or key manipulation, causing the LLM to retrieve or write to unintended collections. Attackers may exploit this by injecting crafted prompts that lead to data exfiltration through Firestore references or by forcing the system to hallucinate administrative settings.

Echo Go’s runtime behavior can further enable these attacks when Firestore listeners or queries are influenced by dynamic user prompts. If query constraints such as collection names or filter fields are derived from LLM suggestions without whitelisting, an attacker can coerce the service into scanning broader or sensitive collections. The interplay between LLM creativity and Firestore’s real-time synchronization means that a single hallucinated reference can cascade through multiple documents, especially when cascading writes or batch operations are used. This makes it critical to validate all Firestore interactions at the boundaries, ensuring that document paths, field names, and query parameters are derived from trusted sources rather than model output.

To mitigate hallucination attacks in Echo Go with Firestore, enforce strict schema validation and path canonicalization before any Firestore operation. All document references should be built from predefined constants or configuration, never from raw LLM output. Use allowlists for collection and document identifiers, and validate that referenced documents exist before attempting to read or merge data.

Example: Safe Document Reference Construction

Instead of concatenating LLM-generated strings into Firestore paths, use typed structs and explicit mapping:

//go
import (
	"context"
	"fmt"

	firestore "cloud.google.com/go/firestore"
)

const profilesCollection = "profiles"

func getProfile(ctx context.Context, client *firestore.Client, userID string) (*Profile, error) {
	// Validate userID format before using it
	if !isValidUserID(userID) {
		return nil, fmt.Errorf("invalid user ID")
	}
	ref := client.Collection(profilesCollection).Doc(userID)
	var p Profile
	if err := ref.Get(ctx, &p); err != nil {
		return nil, err
	}
	return &p, nil
}

func isValidUserID(id string) bool {
	// Allow only alphanumeric and underscores, length constraints
	// e.g., ^[a-zA-Z0-9_]{1,64}$
	return len(id) > 0 && len(id) <= 64
}

Example: Validating Firestore Data Before LLM Consumption

Ensure that data read from Firestore conforms to expected structures before being fed to the LLM. This prevents the model from hallucinating based on missing or malformed fields:

//go
type UserProfile struct {
	Name  string `firestore:"name"`
	Email string `firestore:"email"`
}

func fetchProfileForLLM(ctx context.Context, client *firestore.Client, userID string) (map[string]interface{}, error) {
	docRef := client.Collection("profiles").Doc(userID)
	ctx, cancel := context.WithTimeout(ctx, firestoreTimeout)
	defer cancel()
	snap, err := docRef.Get(ctx)
	if err != nil {
		return nil, err
	}
	var p UserProfile
	if err := snap.DataTo(&p); err != nil {
		return nil, err
	}
	// Only pass validated fields to the LLM prompt
	data := map[string]interface{}{
		"name":  p.Name,
		"email": p.Email,
	}
	return data, nil
}

Example: Using Firestore Transactions to Prevent Stale Reads

When writing back LLM-assisted results, use transactions to ensure that the document state has not changed unexpectedly, reducing the risk of acting on hallucinated context:

//go
func updateWithValidation(ctx context.Context, client *firestore.Client, userID string, newData map[string]interface{}) error {
	_, err := client.RunTransaction(ctx, func(ctx context.Context, tx *firestore.Transaction) error {
		ref := client.Collection("profiles").Doc(userID)
		if err := tx.Get(ref, &Profile{}); err != nil {
			return err
		}
		return tx.Set(ref, newData, firestore.MergeAll)
	})
	return err
}

By combining strict input validation, constant-based collection references, and defensive reads, Echo Go services can significantly reduce the surface for hallucination attacks that involve Firestore. Treat LLM output as untrusted input and verify every Firestore interaction against a known schema.