Hallucination Attacks on Aws

Hallucination attacks in AWS environments exploit the way AI models generate responses, causing them to produce fabricated information that appears legitimate. In AWS Lambda functions, this manifests when AI-powered features generate hallucinated IAM permissions, resource ARNs, or service configurations that don't exist but appear valid to the model.

A common pattern occurs in Lambda functions that use AI services like Amazon Bedrock or SageMaker. When an AI model hallucinates AWS resource identifiers, it might generate fake ARNs like arn:aws:s3:::nonexistent-bucket or hallucinated IAM role names that don't exist in the account. These fabricated references can propagate through the system, causing downstream failures or security bypasses.

const { TextGeneration } = require("@aws-sdk/client-bedrock");

async function generateResponse(input) {
  const client = new TextGeneration({ region: 'us-east-1' });
  
  const params = {
    modelId: 'anthropic.claude-v2',
    maxTokens: 1000,
    inputText: input
  };
  
  try {
    const data = await client.generateText(params);
    return data.outputText;
  } catch (error) {
    console.error('Bedrock generation failed:', error);
    return null;
  }
}

The above code shows a Lambda function using Bedrock, but without proper validation, the AI model could hallucinate AWS service responses. For instance, when querying for available S3 buckets or IAM roles, a model might generate fake ARNs that the function then attempts to use, leading to runtime errors or, worse, security vulnerabilities if the function blindly trusts the AI output.

Another manifestation appears in API Gateway integrations where AI models generate hallucinated OpenAPI specifications. The model might invent endpoints, methods, or security schemes that don't exist in the actual API, leading to broken integrations or security misconfigurations.

Detecting hallucination attacks in AWS requires both runtime monitoring and proactive scanning. AWS CloudTrail logs provide the first line of defense by recording all API calls, including those from AI services. Monitoring for unusual patterns like repeated failed attempts to access non-existent resources or sudden spikes in IAM policy changes can indicate hallucination-based attacks.

middleBrick's AWS-specific scanning identifies hallucination vulnerabilities by testing AI endpoints for prompt injection and output validation weaknesses. The scanner examines how your AWS Lambda functions handle AI-generated responses, checking if they validate ARNs, resource identifiers, and service responses before using them.

Key detection patterns include:

• Monitoring for API calls to non-existent resources (CloudTrail events with ResourceNotFoundException)
• Tracking IAM policy changes that reference hallucinated permissions
• Observing Lambda function logs for repeated failures accessing fabricated resources
• Analyzing CloudWatch metrics for abnormal patterns in AI service usage
• Scanning for unvalidated AI model outputs in your API endpoints

middleBrick specifically tests for AWS hallucination vulnerabilities by:

• Scanning Lambda functions that integrate with AI services
• Testing prompt injection vulnerabilities in Bedrock and SageMaker endpoints
• Verifying proper validation of AI-generated AWS resource identifiers
• Checking for exposed AI system prompts that could be manipulated
• Analyzing IAM role assumptions for hallucinated permissions

The scanner's LLM/AI security checks include 27 regex patterns that detect common hallucination formats, such as fabricated ARNs, IAM role names, and service configurations specific to AWS services.

Remediating hallucination attacks in AWS requires a defense-in-depth approach that combines input validation, output sanitization, and proper IAM controls. The first layer of defense is implementing strict validation of AI-generated outputs before they're used in AWS service calls.

const AWS = require('aws-sdk');
const { validateArn } = require('./arnValidator');

async function processAIResponse(aiOutput) {
  // Validate that generated ARNs match expected patterns
  if (aiOutput.generatedArn && !validateArn(aiOutput.generatedArn)) {
    throw new Error('Invalid ARN format detected');
  }
  
  // Check if referenced resources actually exist
  if (aiOutput.bucketName) {
    const s3 = new AWS.S3();
    try {
      await s3.headBucket({ Bucket: aiOutput.bucketName }).promise();
    } catch (error) {
      if (error.code === 'NotFound') {
        throw new Error('Referenced S3 bucket does not exist');
      }
    }
  }
  
  return aiOutput;
}

function validateArn(arn) {
  const arnRegex = /^arn:aws:[a-z0-9-]+:[a-z0-9-]+:[0-9]{12}:.*$/;
  return arnRegex.test(arn);
}

This code demonstrates validating AI-generated ARNs against AWS patterns and verifying resource existence before use. The validateArn function ensures the format matches AWS specifications, while the S3 headBucket call confirms the referenced bucket actually exists.

For API Gateway integrations, implement response validation middleware that checks AI-generated OpenAPI specifications against your actual API definitions:

const { APIGateway } = require('aws-sdk');

async function validateAPIGeneration(generatedSpec) {
  const apiGateway = new APIGateway();
  
  // Verify that all referenced resources exist
  for (const resource of generatedSpec.resources) {
    try {
      await apiGateway.getResources({ restApiId: resource.apiId }).promise();
    } catch (error) {
      throw new Error(`Resource ${resource.path} does not exist in API ${resource.apiId}`);
    }
  }
  
  // Check for hallucinated IAM permissions
  const invalidPermissions = generatedSpec.permissions.filter(
    perm => !VALID_AWS_PERMISSIONS.includes(perm)
  );
  
  if (invalidPermissions.length > 0) {
    throw new Error(`Invalid permissions detected: ${invalidPermissions.join(', ')}`);
  }
}

Implement IAM policies that limit AI services to specific, pre-approved resources:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:GenerateText",
        "sagemaker:InvokeEndpoint"
      ],
      "Resource": "*",
      "Condition": {
        "ArnLike": {
          "bedrock:ModelId": [
            "anthropic.claude-v2",
            "amazon.titan-express"
          ]
        }
      }
    },
    {
      "Effect": "Deny",
      "Action": "*",
      "Resource": "*",
      "Condition": {
        "StringNotLike": {
          "aws:ARN": [
            "arn:aws:s3:::approved-bucket/*",
            "arn:aws:dynamodb:us-east-1:*:table/approved-table"
          ]
        }
      }
    }
  ]
}

This IAM policy allows AI services to operate but restricts them to approved models and resources, preventing the use of hallucinated identifiers.