Injection Flaws in Gin with Dynamodb

When building APIs in Go with the Gin framework that interact with Amazon DynamoDB, injection flaws arise when user-controlled input is used to construct DynamoDB API requests without proper validation or escaping. DynamoDB uses a JSON-based query model and attribute-value placeholders, but developers sometimes interpolate values directly into expression strings or build table/attribute names from request parameters. This creates injection surfaces for NoSQL injection, including unexpected type confusion or traversal through DynamoDB's partiql-like syntax when placeholders are misused.

For example, a common pattern is to accept a query parameter like id and plug it into a KeyConditionExpression or filter expression. If the value is concatenated into the expression string instead of being passed as an expression attribute value, an attacker can inject logical operators such as OR, comparison operators, or path traversal to read or modify unintended items. In Gin handlers, this often happens when binding query parameters or JSON bodies and then constructing DynamoDB input structs without sanitizing or parameterizing expressions.

Middleware or routing logic that derives DynamoDB table names or index names from user input compounds the risk. An attacker may attempt table name injection to target a different table with sensitive data, especially if the application assumes table names are safe because they are internal. Another vector is the use of expression attribute names: if attribute names are built from user input without strict allowlisting, attackers can traverse nested maps or inject reserved keywords, bypassing intended access controls.

The 12 security checks in middleBrick validate these scenarios by testing unauthenticated endpoints that use DynamoDB. For instance, it checks whether input validation blocks unexpected types or paths in query parameters, whether rate limiting prevents mass enumeration via injected filter expressions, and whether data exposure checks detect excessive item retrieval due to bypassed authorization in expressions. Because DynamoDB responses can contain sensitive PII or API keys, middleBrick also scans outputs for accidental leakage in error messages or malformed responses that arise from injection-triggered runtime exceptions.

Real-world patterns include using condition_expression with injected attribute names or values that alter query semantics. A vulnerable Gin route might look like constructing a GetItem input where the key is assembled from URL parameters without validation. middleBrick's LLM/AI Security checks are particularly relevant here because they test for prompt injection-style risks in any LLM-integrated API that may use DynamoDB as a backend store, ensuring that unauthorized instructions do not lead to unintended data access via malformed inputs.

To remediate injection flaws when using DynamoDB with Gin, always use expression placeholders for attribute values and expression attribute names, and never concatenate user input into expression strings. Validate and type-check all inputs against an allowlist, especially IDs, keys, and attribute paths. Use strict schema validation for request bodies and query parameters to ensure types and lengths are within expectations.

Below are concrete, safe examples in Go using the AWS SDK for DynamoDB with Gin. The first example shows a vulnerable pattern and the corrected, parameterized version.

// Vulnerable: concatenating user input into an expression
// DO NOT DO THIS
c.BindQuery(&params)
key := map[string]*dynamodb.AttributeValue{
    "PK": {S: aws.String(params["id"])},
}
expr := "PK = :val" // Unsafe if params["id"] is used directly in expression
// Risk: injection via params["id"] if it contains logical operators

// Correct: using expression attribute values
input := &dynamodb.GetItemInput{
    TableName: aws.String("MyTable"),
    Key: key,
    ExpressionAttributeNames: map[string]*string{
        "#pk": aws.String("PK"),
    },
    ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
        ":val": {S: aws.String(params["id"])},
    },
    KeyConditionExpression: aws.String("#pk = :val"), // Safe: placeholders used
}
_, err := client.GetItem(context.TODO(), input)

Another important fix is to avoid using user input as DynamoDB table or index names. If dynamic table selection is required, map the input to a predefined set of allowed tables.

// Allowlist of safe table names
tableAllowlist := map[string]bool{
    "prod-users": true,
    "staging-users": true,
}
table := params["table"]
if !tableAllowlist[table] {
    c.JSON(http.StatusBadRequest, gin.H{"error": "invalid table"})
    return
}
input := &dynamodb.ScanInput{
    TableName: aws.String(table),
    FilterExpression: aws.String("#status = :status"),
    ExpressionAttributeNames: map[string]*string{
        "#status": aws.String("status"),
    },
    ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
        ":status": {S: aws.String("active")}, // Safe placeholder usage
    },
}
_, err := client.Scan(context.TODO(), input)

For expression attribute names, always use allowlisted aliases instead of passing raw attribute names from clients. This prevents path traversal and injection via reserved keywords. Combine this with input validation libraries to enforce type and format constraints before constructing DynamoDB requests. middleBrick’s checks for Property Authorization and Input Validation help verify that these patterns are correctly enforced in your API’s runtime behavior.