Formula Injection in Gorilla Mux with Dynamodb

Formula Injection occurs when user-controlled data is interpreted as a formula (e.g., Excel-style expressions) by a downstream system, leading to unintended evaluation or data leakage. In a Gorilla Mux route combined with Amazon DynamoDB, this typically arises when path or query parameters are used to construct DynamoDB expressions, key conditions, or filter logic without proper sanitization.

Gorilla Mux is a URL router and dispatcher for Go that enables expressive routing patterns, including variable placeholders like {id} or {expression}. If these placeholders are directly mapped into DynamoDB input—such as the key condition expression in a Query or a FilterExpression—an attacker can supply crafted values like ?id=123 + SUM(1,2) or embed JSON path-like syntax that is misinterpreted by downstream visualization or tooling as a formula.

DynamoDB itself does not evaluate formulas, but client-side construction of expressions can cause issues when the data is consumed by other systems (e.g., exported to spreadsheets, processed by reporting tools, or used in serverless functions that interpret strings as code). For example, a developer might build a DynamoDB KeyConditionExpression by concatenating strings:

expr := "user_id = :uid AND timestamp > :start"
input := "user_id = " + mux.Vars(r)["user_id"] + " AND timestamp > :start"

If mux.Vars(r)["user_id"] contains untrusted input, an attacker could supply "123 + 1/0" or other payloads that, while not directly executed by DynamoDB, may cause parsing errors or logic bypasses in validation layers. More critically, injection can occur in FilterExpression or ConditionExpression when user input is used to build attribute names or values without escaping.

An example of a vulnerable pattern using the AWS SDK for DynamoDB in a Gorilla Mux handler:

func getUserHandler(w http.ResponseWriter, r *http.Request) {
    vars := mux.Vars(r)
    userID := vars["user_id"]
    tableName := "users"

    // Vulnerable: direct concatenation
    keyCondition := fmt.Sprintf("user_id = :uid")
    input := &dynamodb.QueryInput{
        TableName: aws.String(tableName),
        KeyConditionExpression: aws.String(keyCondition),
        ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
            ":uid": {S: aws.String(userID)},
        },
    }
    result, err := svc.Query(context.TODO(), input)
    // ... handle result
}

Although DynamoDB treats :uid as a literal value, downstream tooling that parses logs or exports data may misinterpret injected tokens. Additionally, if the application builds dynamic expression strings for FilterExpression using user input, special characters or logical operators could alter intended filtering, leading to over-fetching or data exposure.

Remediation focuses on strict input validation, avoiding dynamic expression building, and using DynamoDB’s built-in mechanisms such as ConditionExpression with placeholders. MiddleBrick’s LLM/AI Security checks are particularly effective at detecting prompt injection and output anomalies that could indicate formula injection risks in AI-assisted API workflows.

To prevent Formula Injection in Gorilla Mux with DynamoDB, ensure that user input is never directly concatenated into expression strings. Use parameterized expressions and validate all inputs against strict patterns.

Below is a secure implementation for a Gorilla Mux handler that queries DynamoDB safely:

func getUserHandler(w http.ResponseWriter, r *http.Request) {
    vars := mux.Vars(r)
    userID, ok := vars["user_id"]
    if !ok || !isValidUserID(userID) {
        http.Error(w, "invalid user_id", http.StatusBadRequest)
        return
    }

    tableName := "users"
    // Safe: using placeholders and ExpressionAttributeValues
    keyConditionExpression := "user_id = :uid"
    input := &dynamodb.QueryInput{
        TableName: aws.String(tableName),
        KeyConditionExpression: aws.String(keyConditionExpression),
        ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
            ":uid": {S: aws.String(userID)},
        },
    }
    result, err := svc.Query(context.TODO(), input)
    if err != nil {
        http.Error(w, err.Error(), http.StatusInternalServerError)
        return
    }
    // Process result...
}

func isValidUserID(s string) bool {
    // Allow only alphanumeric and underscores, length 1-36
    matched, _ := regexp.MatchString(`^[a-zA-Z0-9_]{1,36}$`, s)
    return matched
}

For FilterExpression, avoid building expressions from user input. If filtering is required, define a fixed set of allowed fields and values:

func listUsersHandler(w http.ResponseWriter, r *http.Request) {
    status := r.URL.Query().Get("status")
    allowedStatus := map[string]bool{"active": true, "inactive": true}
    if status != "" && !allowedStatus[status] {
        http.Error(w, "invalid status filter", http.StatusBadRequest)
        return
    }

    var filterExp string
    var attrVals map[string]*dynamodb.AttributeValue
    if status != "" {
        filterExp = "status = :status"
        attrVals = map[string]*dynamodb.AttributeValue{
            ":status": {S: aws.String(status)},
        }
    } else {
        filterExp = "attribute_exists(user_id)"
        attrVals = map[string]*dynamodb.AttributeValue{}
    }

    input := &dynamodb.ScanInput{
        TableName: aws.String("users"),
        FilterExpression: aws.String(filterExp),
        ExpressionAttributeValues: attrVals,
    }
    result, err := svc.Scan(context.TODO(), input)
    // ... handle result
}

When using MiddleBrick’s CLI or Web Dashboard, you can validate that your API endpoints are not reflecting user input into DynamoDB expressions by running scans that include BFLA/Privilege Escalation and Input Validation checks. The Pro plan’s continuous monitoring can alert you if new endpoints introduce unsafe string concatenation patterns.

Always treat path and query parameters as opaque strings. Use middleware to sanitize inputs before they reach route handlers, and rely on DynamoDB’s placeholder system rather than string interpolation. The GitHub Action can enforce these rules in CI/CD, failing builds if insecure patterns are detected in your codebase.