Injection Flaws on Aws

Injection flaws in Aws applications typically occur when untrusted data is sent to an interpreter as part of a command or query. In the Aws ecosystem, these vulnerabilities often manifest through DynamoDB queries, Lambda function parameters, and API Gateway request processing.

DynamoDB injection is particularly prevalent when using the DocumentClient API without proper parameterization. Consider this vulnerable code:

const params = { TableName: 'Users', KeyConditionExpression: 'username = ' + event.username };
const result = await dynamodb.query(params).promise();

An attacker could inject NoSQL syntax through the username parameter, potentially bypassing authentication or extracting data. For example, submitting admin OR 1=1 as the username might return all user records.

Lambda function injection occurs when environment variables or function parameters are constructed from user input without validation. Aws SAM templates that dynamically generate IAM policies based on user input create similar risks:

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      Environment:
        Variables:
          USER_INPUT: !Ref UsernameInput

If UsernameInput comes from an API request and is later used in IAM policy generation or resource access control, an attacker could escalate privileges.

API Gateway injection vulnerabilities arise when request parameters are directly embedded in backend service calls. A common pattern is using path parameters in DynamoDB keys or S3 object names without sanitization:

const key = event.pathParameters.filename;
const s3 = new AWS.S3();
const data = await s3.getObject({ Bucket: 'my-bucket', Key: key }).promise();

An attacker could manipulate the filename parameter to access unauthorized S3 objects or trigger unintended behavior.

Detecting injection flaws in Aws environments requires both static analysis and runtime scanning. For DynamoDB injection, middleBrick's black-box scanning tests for NoSQL injection patterns by attempting to manipulate query parameters and observing response behavior.

middleBrick specifically tests for:

• NoSQL injection in DynamoDB queries using common injection payloads
• Parameter injection in Lambda environment variables and function arguments
• Path traversal and injection in S3 object access patterns
• Template injection in Aws SAM/CloudFormation templates

The scanner examines API endpoints for unvalidated parameters that could be used in injection attacks. For example, it tests whether username parameters in API calls are properly sanitized before being used in database queries or IAM policy construction.

Runtime detection can be enhanced using Aws services like X-Ray for tracing and CloudWatch for logging. Monitor for unusual query patterns, such as:

{
  "type": "DYNAMODB",
  "httpMethod": "POST",
  "request": {
    "queryStringParameters": {
      "KeyConditionExpression": "username = 'admin' OR 1=1"
    }
  }
}

CloudTrail logs can reveal suspicious API calls, particularly those involving IAM policy modifications or resource access attempts with unusual parameters.

For LLM/AI security specific to Aws, middleBrick's active probing tests for prompt injection vulnerabilities in Aws Bedrock or other AI services integrated into your APIs. This includes testing for system prompt leakage and instruction override attempts.

Remediating injection flaws in Aws requires a defense-in-depth approach using Aws-native security features. For DynamoDB injection prevention, always use parameterized queries:

const params = {
  TableName: 'Users',
  KeyConditionExpression: 'username = :username',
  ExpressionAttributeValues: {
    ':username': event.username
  }
};
const result = await dynamodb.query(params).promise();

This approach ensures user input is treated as data, not executable code.

For Lambda function security, implement strict input validation using Aws API Gateway request validation:

Resources:
  MyApi:
    Type: AWS::Serverless::Api
    Properties:
      DefinitionUri: swagger.yaml
      RequestValidator:
        ValidateRequestBody: true
        ValidateRequestParameters: true

Combine this with Lambda function-level IAM policies that follow the principle of least privilege:

Resources:
  MyFunction:
    Type: AWS::Serverless::Function
    Properties:
      Policies:
        - DynamoDBCrudPolicy:
            TableName: Users
        - S3ReadPolicy:
            BucketName: my-restricted-bucket

For S3 injection prevention, use Aws S3 Block Public Access and implement bucket policies that restrict access based on principle of least privilege:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {"AWS": "arn:aws:iam::123456789012:root"},
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/*",
      "Condition": {
        "StringLike": {
          "s3:prefix": [
            "public/",
            "uploads/${aws:username}/*"
          ]
        }
      }
    }
  ]
}

Implement WAF rules using Aws WAF to block common injection patterns at the API Gateway level:

Resources:
  WebACL:
    Type: AWS::WAFv2::WebACL
    Properties:
      DefaultAction: { Block: {} }
      Rules:
        - Name: SQLInjectionRule
          Action: { Block: {} }
          Priority: 1
          Statement: { SqliMatchStatement: { FieldToMatch: { Body: {} }, TextTransformations: [ { Name: "URL_DECODE", Priority: 1 } ] } }

middleBrick's scanning can verify these protections by testing whether injection payloads are properly blocked before reaching your backend services.