Attack Path Analysis

Utilize Generative AI to identify potential attack paths in your AWS environment.

Netlumi's Attack Path Analysis
Leveraging Generative AI, Netlumi doesn't just find isolated vulnerabilities; we uncover exploitable sequences – the actual paths an attacker could take to compromise your critical assets in AWS.

Understanding these chained risks is crucial for proactive defense.

Why Attack Paths Matter for Startups & SMEs

Instead of overwhelming you with a long list of individual misconfigurations (like 'S3 bucket is public' or 'IAM role has excessive permissions'), Netlumi's Attack Path Analysis connects the dots. We show you the sequence of vulnerabilities an attacker could exploit to reach critical assets or achieve their objectives.

This contextualized view helps small, busy teams to:

  • Prioritize Effectively: Focus on fixing the most critical chains of vulnerabilities first, not just the noisy ones.
  • Understand Real Risk: See the actual business impact of combined misconfigurations, not just isolated low-severity issues.
  • Reduce Alert Fatigue: Move beyond chasing individual alerts to understanding and mitigating genuinely exploitable sequences.
  • Communicate Clearly: Easily explain complex security risks and the need for remediation to stakeholders with clear, visualized paths.

Illustrative Attack Path Examples

Public S3 -> Lambda with Privileged Role -> SecretsManager Exfiltration

Attack Path Steps:

  • Bucket `customer-data-uploads` is public (BlockPublicAcls: false, PublicAccess: Everyone).
  • Lambda `parse-uploaded-docs` uses IAM role `lambda-role-docparser` with permission `secretsmanager:GetSecretValue` for secret `arn:aws:secretsmanager:us-east-1:321456789012:secret:Prod/DbCredentials`.

Configuration Snippet (S3 Bucket Policy)

{
  "BucketPolicy": {
    "Effect": "Allow",
    "Principal": "*",
    "Action": "s3:PutObject",
    "Resource": "arn:aws:s3:::customer-data-uploads/*"
  }
}

Scenario Explanation:

An attacker uploads a ZIP file containing malicious code to the public S3 bucket. When the Lambda function processes this file, the malicious code executes with the Lambda's permissions, retrieves secrets from Secrets Manager, and exfiltrates them via an HTTP request to an attacker-controlled server.

EC2 Metadata SSRF -> IAM Role Abuse

Attack Path Steps:

  • EC2 instance `web-app-1` is running a PHP application vulnerable to Server-Side Request Forgery (SSRF) via the `/fetch-data` endpoint.
  • The instance is associated with an IAM instance profile `ec2-admin-role` that has overly broad permissions (e.g., `iam:*`, `s3:*`).

SSRF Payload Example

GET /fetch-data?url=http://169.254.169.254/latest/meta-data/iam/security-credentials/ec2-admin-role

Scenario Explanation:

An attacker exploits the SSRF vulnerability to make the EC2 instance request temporary credentials for its IAM role from the instance metadata service. Once the attacker obtains these credentials, they can use them to perform unauthorized actions as allowed by the `ec2-admin-role`, such as listing IAM users, modifying IAM policies, or accessing sensitive data in S3 buckets.

Compromised CodeBuild -> IAM Role Misuse -> S3 Ransomware

Attack Path Steps:

  • AWS CodeBuild project `build-finops-ui` is configured to allow unreviewed pull requests to trigger builds using its `buildspec.yml` file.
  • The IAM role `codebuild-finops-role` assumed by the CodeBuild project has excessive permissions, such as `s3:*` and `kms:*` on critical company S3 buckets and KMS keys.

Malicious buildspec.yml Snippet

phases:
  build:
    commands:
      - aws s3 rm s3://finance-reports-prod/ --recursive
      - aws kms schedule-key-deletion --key-id alias/main-prod-key --pending-window-in-days 7

Scenario Explanation:

An attacker submits a pull request with a malicious `buildspec.yml`. Once the PR is merged (if reviews are lax or bypassed), the CodeBuild project executes the malicious commands. These commands could delete all objects in a production S3 bucket and schedule the deletion of the KMS key used to encrypt that data, effectively holding the data for ransom or causing irreversible data loss.

Cross-Account AssumeRole -> Data Access in Another Account

Attack Path Steps:

  • An IAM user `analytics-bot` in a less trusted account (Account A: 123456789012) has its access keys exposed or compromised.
  • An IAM role `DataReadRole` in a more trusted account (Account B: 789012345678) has a trust policy that overly trusts Account A, allowing any principal from Account A to assume it (e.g., `"Principal": { "AWS": "arn:aws:iam::123456789012:root" }`).
  • The `DataReadRole` in Account B has permissions to access sensitive data, such as `s3:GetObject` on specific buckets or `rds:DescribeDBInstances`.

IAM Role Trust Policy Snippet (Account B)

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::123456789012:root" 
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Scenario Explanation:

An attacker who has compromised the `analytics-bot` user keys in Account A can use those keys to assume the `DataReadRole` in Account B. With the assumed role's permissions, the attacker can then access and potentially exfiltrate sensitive data from S3 buckets or gather information about RDS instances in Account B, bypassing normal cross-account security boundaries.