Hallucination Attacks in Gorilla Mux with Dynamodb

Gorilla Mux is a popular routing library for Go that supports dynamic path patterns, wildcards, and strict route matching. When combined with DynamoDB as a backend data source, one risk is that route parameters can be improperly reflected into queries or responses, enabling hallucination attacks where the API returns fabricated or inconsistent data that does not match the stored state.

In a typical setup, a developer might define a route like /items/{id} in Gorilla Mux and use the extracted id to query DynamoDB. If input validation and authorization are weak, an attacker can supply an id that triggers inconsistent behavior between the routing layer and the database layer. For example, the route may accept an id that does not correspond to any item, but the application may hallucinate a default or synthetic response instead of returning a proper 404. This can leak information about internal logic or default objects and may allow an attacker to infer valid identifiers or data structure through side channels.

DynamoDB-specific aspects exacerbate the risk. Because DynamoDB is a NoSQL store with flexible schema design, it is common to store items with composite keys (partition key and sort key). If Gorilla Mux extracts only a single path parameter and maps it incorrectly to the wrong key attribute, the query may return an item with a different identity than intended. The API might then present this mismatched item as if it were the correct resource, effectively hallucinating a relation between the path and the data. In addition, DynamoDB’s pagination and filtering behaviors may cause partial results to be presented as complete records, especially if the client never validates that the returned item’s key matches the route key. This mismatch is a classic hallucination pattern: the route says one resource, the response shows another.

Another vector involves the use of query parameters that are not properly bound to DynamoDB expression attribute values. For instance, a filter like ?status=active might be appended to a DynamoDB query constructed by concatenating strings in Go, and if the parameter is not validated, the route may hallucinate matches by returning items that do not actually satisfy the stated filter. Attackers can probe these endpoints to observe differences in timing, response size, or error messages, which can reveal how the hallucination logic behaves. Because Gorilla Mux does not inherently enforce schema or type checks, developers must explicitly validate that path and query inputs align with the expected DynamoDB key schema and attribute types.

LLM/AI security checks are relevant here because hallucination attacks against AI-facing endpoints can compound the problem: an LLM integration might synthesize plausible but incorrect text based on DynamoDB responses that are themselves hallucinated. middleBrick’s LLM/AI security tests include system prompt leakage detection and active prompt injection probes, which help identify whether an AI endpoint reflects inconsistent or fabricated route-to-database mappings. Output scanning further ensures that PII or API keys are not leaked through these hallucinated responses.

To mitigate hallucination attacks when using Gorilla Mux with DynamoDB, enforce strict validation, canonical key mapping, and explicit response checks. Below are concrete, realistic code examples that demonstrate secure patterns.

First, define a structured router handler that extracts and validates the id parameter before building the DynamoDB key. Use a regex route pattern to ensure the ID conforms to an expected format, and map it directly to the partition key attribute.

import (
    "github.com/gorilla/mux"
    "github.com/aws/aws-sdk-go/aws"
    "github.com/aws/aws-sdk-go/aws/session"
    "github.com/aws/aws-sdk-go/service/dynamodb"
    "github.com/aws/aws-sdk-go/service/dynamodb/dynamodbattribute"
    "net/http"
    "regexp"
)

type Item struct {
    ID    string `json:"id" dynamodbav:"id"`
    Name  string `json:"name" dynamodbav:"name"`
    State string `json:"state" dynamodbav:"state"`
}

func getItemHandler(db *dynamodb.DynamoDB) http.HandlerFunc {
    // enforce a UUID-like pattern to reduce injection surface
    idRegex := regexp.MustCompile(`^[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}$`)
    return func(w http.ResponseWriter, r *http.Request) {
        vars := mux.Vars(r)
        id := vars["id"]
        if !idRegex.MatchString(id) {
            http.Error(w, "invalid item id", http.StatusBadRequest)
            return
        }

        svc := dynamodb.New(session.New())
        out, err := svc.GetItem(&dynamodb.GetItemInput{
            TableName: aws.String("Items"),
            Key: map[string]*dynamodb.AttributeValue{
                "id": {S: aws.String(id)},
            },
        })
        if err != nil {
            http.Error(w, "server error", http.StatusInternalServerError)
            return
        }
        if out.Item == nil {
            http.Error(w, "not found", http.StatusNotFound)
            return
        }
        var item Item
        if err := dynamodbattribute.UnmarshalMap(out.Item, &item); err != nil {
            http.Error(w, "data error", http.StatusInternalServerError)
            return
        }
        // explicit consistency check: ensure returned key matches route id
        if *item.ID != id {
            http.Error(w, "not found", http.StatusNotFound)
            return
        }
        // respond safely
        w.Header().Set("Content-Type", "application/json")
        json.NewEncoder(w).Encode(item)
    }
}

Second, when querying with secondary indexes or filters, use DynamoDB ExpressionAttributeValues rather than string concatenation to avoid injection-induced hallucination. For example, if filtering by status, prepare the query with condition expression and attribute values.

func queryItemsByStatus(db *dynamodb.DynamoDB, status string) ([]Item, error) {
    svc := dynamodb.New(session.New())
    // validate status against an allowlist to prevent unexpected behavior
    allowed := map[string]bool{"active": true, "inactive": true, "pending": true}
    if !allowed[status] {
        return nil, fmt.Errorf("invalid status")
    }
    out, err := svc.Query(&dynamodb.QueryInput{
        TableName:              aws.String("Items"),
        IndexName:              aws.String("GSI_StatusID"),
        KeyConditionExpression: aws.String("status = :s AND begins_with(id, :p)"),
        ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
            ":s": {S: aws.String(status)},
            ":p": {S: aws.String("item_")},
        },
    })
    if err != nil {
        return nil, err
    }
    var items []Item
    if err := dynamodbattribute.UnmarshalListOfMaps(out.Items, &items); err != nil {
        return nil, err
    }
    // verify that each item has the expected status to prevent hallucination
    for _, it := range items {
        if it.State != status {
            // handle mismatch safely instead of assuming correctness
            continue
        }
    }
    return items, nil
}

Third, apply middleware in Gorilla Mux to log and audit route-to-database mappings, making it easier to detect mismatches that could lead to hallucination. Combine this with continuous monitoring and input schema checks to ensure that path parameters, query parameters, and DynamoDB keys remain aligned. These practices reduce the chance that the API fabricates data when the route and storage layer do not agree.