Hallucination Attacks in Echo Go with Dynamodb

Hallucination attacks occur when an application returns fabricated or misleading information as if it were factual. In the context of Echo Go applications that use Dynamodb as a backend, this risk arises when the service layer constructs responses by combining data from Dynamodb with logic that may interpolate, infer, or guess missing information. Because Echo Go is commonly used to build HTTP APIs, any endpoint that reads from Dynamodb and then generates natural-language summaries, error messages, or structured outputs can become a vector for hallucination if input validation and authorization checks are weak.

Specifically, the combination increases exposure in two ways. First, if an endpoint queries Dynamodb using user-supplied identifiers without strict property-level authorization, an attacker may supply an ID that belongs to another user. The application might then fill gaps with plausible but incorrect data, producing a response that appears authoritative. Second, if the application layer formats Dynamodb item attributes into messages (for example, returning a user profile or configuration), missing fields or inconsistent types can trigger the service to invent values to maintain a consistent shape, especially when using JSON marshalling in Go that omits empty values differently depending on struct tags.

The LLM/AI Security checks in middleBrick highlight these risks by testing for system prompt leakage and output anomalies. When an endpoint backed by Dynamodb interacts with language model components or generates verbose logs, outputs can inadvertently reveal patterns that allow an attacker to infer data relationships or inject crafted content. Because middleBrick scans the unauthenticated attack surface and runs active prompt injection probes, it can identify cases where responses from Echo Go endpoints include sensitive context or overreaching statements that should not be exposed from Dynamodb records alone.

Real-world patterns tied to this include improper handling of conditional writes in Dynamodb, missing validation on sort keys, and insufficient checks on filter expressions. These can lead to inconsistent reads that Echo Go then attempts to reconcile, producing outputs that blend stale or partial data with generated text. Such behavior aligns with broader API security concerns around Input Validation and Data Exposure, where unchecked assumptions about data completeness create openings for misinformation or inference attacks.

Remediation focuses on strict schema validation, precise querying, and defensive response assembly. When interacting with Dynamodb in Echo Go, always validate identifiers against a known format, enforce ownership through additional lookup filters, and avoid merging inferred values into structured outputs. Use strongly typed structures with explicit omitempty rules to control JSON serialization, and ensure that error messages do not echo raw user input or synthesize missing fields.

Example: safe retrieval from Dynamodb with input validation and canonical response formatting.

// Define a typed structure that matches the expected Dynamodb item
type Profile struct {
    UserID   string `json:"userId" dynamodbav:"userId"`
    Email    string `json:"email" dynamodbav:"email"`
    Verified bool   `json:"verified" dynamodbav:"verified"`
}

// Function to load a profile with strict checks
func loadProfile(svc *dynamodb.Client, userID string) (*Profile, error) {
    if userID == "" || !regexp.MustCompile(`^[a-zA-Z0-9_-]{1,64}$`).MatchString(userID) {
        return nil, errors.New("invalid user identifier")
    }
    out, err := svc.GetItem(context.TODO(), &dynamodb.GetItemInput{
        TableName: aws.String("Profiles"),
        Key: map[string]types.AttributeValue{
            "userId": &types.AttributeValueMemberS{Value: userID},
        },
    })
    if err != nil {
        return nil, fmt.Errorf("unable to fetch profile")
    }
    if out.Item == nil {
        return nil, errors.New("profile not found")
    }
    var p Profile
    err = attributevalue.UnmarshalMap(out.Item, &p)
    if err != nil {
        return nil, fmt.Errorf("failed to unmarshal profile")
    }
    return &p, nil
}

// Handler that returns a canonical JSON response
func profileHandler(svc *dynamodb.Client) echo.HandlerFunc {
    return func(c echo.Context) error {
        userID := c.Param("userID")
        profile, err := loadProfile(svc, userID)
        if err != nil {
            // Return a generic error without inventing details
            return c.JSON(http.StatusNotFound, map[string]string{"error": "not found"})
        }
        // Explicitly include only verified fields; avoid adding inferred metadata
        return c.JSON(http.StatusOK, profile)
    }
}

Additional practices include using ConditionExpression to ensure read consistency, avoiding expressions that fill missing attributes with defaults, and logging only sanitized identifiers. For endpoints that integrate LLM components, apply middleBrick’s LLM/AI Security checks to detect system prompt leakage and output anomalies. These measures align with findings mapped to frameworks such as OWASP API Top 10 and help reduce data exposure and input validation risks associated with Dynamodb-backed services.