Hallucination Attacks in Echo Go with Cockroachdb

A hallucination attack in the context of an Echo Go service backed by Cockroachdb occurs when a language model or AI-driven component generates plausible but incorrect or fabricated database-related responses. This can happen when prompts or query results are misinterpreted, and the system confidently presents incorrect data as factual. In Echo Go, routes that expose database-driven responses without strict validation can become channels for leaking sensitive information or amplifying inconsistencies if an attacker manipulates inputs to trigger misleading LLM outputs.

When integrating Cockroachdb with Echo Go, the risk arises if SQL results are directly consumed by an LLM or if query parameters influence prompt construction without thorough sanitization. For example, an endpoint that builds dynamic prompts from user-controlled identifiers may inadvertently allow an attacker to inject context that shifts the LLM’s behavior, leading to hallucinated results that appear authoritative. Consider an endpoint that retrieves a user profile and then asks an LLM to summarize account activity; if the profile ID is user-supplied and not rigorously validated, an attacker can supply crafted IDs or malformed queries that produce misleading or sensitive data in the LLM’s response.

Echo Go handlers that construct prompts using raw SQL output are particularly vulnerable if the schema or data relationships are not explicitly constrained. Cockroachdb’s strong consistency does not prevent logical inconsistencies in prompt engineering; if the prompt includes assumptions about data state that do not hold, the LLM may fill gaps with hallucinated content. Additionally, if error messages from Cockroachdb are exposed to the LLM during prompt assembly, an attacker can probe for schema details or table structures, enabling reconnaissance that supports more targeted injection or extraction attempts.

Specific attack patterns include injecting SQL meta-characters or malformed identifiers that cause the Go code to produce unexpected prompt segments, which the LLM then interprets as valid instructions. Inconsistent handling of NULL values or missing rows can also lead to unpredictable prompt states, where the LLM fabricates missing context. Because Echo Go services often stream responses, an attacker can iteratively probe endpoints to observe how hallucinations evolve, potentially extracting fragments of sensitive information or inferring internal logic through crafted outputs.

Mitigating these risks requires strict separation between data retrieval and prompt construction, rigorous input validation, and avoiding the direct inclusion of raw database content in LLM prompts. By treating LLM outputs as untrusted when they reference underlying data, and by validating all inputs against expected formats and access scopes, the attack surface is significantly reduced.

Remediation focuses on ensuring that Cockroachdb interactions are predictable, validated, and isolated from prompt logic. The following practices and code examples demonstrate secure patterns for Echo Go services using Cockroachdb.

• Use parameterized queries to prevent injection: Always use placeholders for user input rather than string concatenation. This prevents malformed inputs from altering query structure or exposing schema details.
• Validate and constrain identifiers: Ensure IDs and keys conform to expected formats before using them in queries or prompts.
• Do not expose raw database errors to LLMs: Map errors to generic messages and log detailed issues server-side.
• Separate data fetching from prompt generation: Build prompts only from sanitized, high-level abstractions of data, never from raw rows or columns.

Example: Secure user profile retrieval and prompt-safe handling in Echo Go

//go
package main

import (
	"context"
	"database/sql"
	"fmt"
	"net/http"
	"regexp"

	"github.com/labstack/echo/v4"
	_ "github.com/lib/pq"
)

var db *sql.DB

// isValidUUID checks basic UUID format to prevent unexpected input.
func isValidUUID(u string) bool {
	pattern := `^[a-fA-F0-9]{8}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{4}-[a-fA-F0-9]{12}$`
	matched, _ := regexp.MatchString(pattern, u)
	return matched
}

// getUserProfile fetches profile data using a parameterized query.
func getUserProfile(c echo.Context) error {
	userID := c.Param("id")
	if !isValidUUID(userID) {
		return c.JSON(http.StatusBadRequest, map[string]string{"error": "invalid user identifier"})
	}

	var profile struct {
		ID       string
		Username string
		Email    sql.NullString
	}

	// Parameterized query ensures Cockroachdb treats input as data, not executable code.
	row := db.QueryRow(c.Request().Context(), "SELECT id, username, email FROM users WHERE id = $1", userID)
	err := row.Scan(&profile.ID, &profile.Username, &profile.Email)
	if err != nil {
		if err == sql.ErrNoRows {
			return c.JSON(http.StatusNotFound, map[string]string{"error": "user not found"})
		}
		// Log detailed error internally; do not expose to LLM or client.
		c.Logger().Error(err)
		return c.JSON(http.StatusInternalServerError, map[string]string{"error": "unable to retrieve profile"})
	}

	// Build prompt from sanitized data only, avoiding raw SQL output.
	prompt := fmt.Sprintf("Summarize activity for user %s (%s).", profile.Username, profile.ID)
	// Here you would pass prompt to your LLM client, ensuring no raw profile fields are directly inserted.
	_ = prompt

	return c.JSON(http.StatusOK, map[string]string{"username": profile.Username})
}

func main() {
	e := echo.New()
	// Initialize db connection securely (omitted for brevity).
	// db = ...

	e.GET("/profile/:id", getUserProfile)
	e.Start(":8080")
}

Example: Safe error handling and logging without leaking details

//go
package main

import (
	"context"
	"database/sql"
	"log"

	"github.com/labstack/echo/v4"
)

func safeQuery(c echo.Context, query string, args ...interface{}) (*sql.Rows, error) {
	rows, err := db.Query(c.Request().Context(), query, args...)
	if err != nil {
		// Map Cockroachdb-specific errors to generic responses.
		log.Printf("db query error: %v", err)
		return nil, sql.ErrConnDone
	}
	return rows, nil
}

By adopting these patterns, Echo Go services reduce the risk of hallucination attacks that exploit database interactions, ensuring that LLM-facing outputs remain grounded in validated, sanitized data rather than raw or inferred database states.