Server-Side Request Forgery (SSRF)

Server-Side Request Forgery (SSRF) occurs when an attacker can manipulate a server into making requests to locations that the server can reach but the attacker normally cannot. This vulnerability is particularly dangerous in cloud environments where internal metadata services, such as AWS’s 169.254.169.254 endpoint, expose sensitive credentials and configuration data. SSRF has risen sharply in prevalence with the adoption of microservice architectures, where backend services routinely fetch resources from URLs provided in API requests, webhooks, and integration configurations.

Why It Matters

SSRF vulnerabilities have been responsible for some of the most impactful cloud breaches in recent years. The 2019 Capital One breach, which exposed over 100 million customer records, was facilitated by an SSRF vulnerability that allowed the attacker to query the AWS metadata service and obtain IAM role credentials. In modern cloud-native architectures, SSRF can enable an attacker to map internal network topology, access internal APIs that lack authentication (relying on network segmentation for security), read cloud provider metadata including temporary credentials, and pivot from a web-facing application into backend infrastructure.

For instance, an application that allows users to provide a URL for a profile image import might fetch http://169.254.169.254/latest/meta-data/iam/security-credentials/ when an attacker supplies that URL, returning cloud IAM credentials that grant broad access to the organization’s cloud infrastructure.

How Revaizor Handles This

Revaizor’s AI agents systematically test every parameter that accepts URLs or hostnames for SSRF potential. The platform tests a comprehensive range of bypass techniques including IP address encoding variations, DNS rebinding, redirect chains, and protocol smuggling. Revaizor understands cloud provider metadata endpoints across AWS, GCP, and Azure, and specifically validates whether these high-value targets are reachable through identified SSRF vectors. The platform’s continuous testing model ensures that new SSRF surfaces introduced by feature deployments are caught before they reach production.