Credential Stuffing

Credential Stuffing is a type of brute-force attack that exploits the widespread problem of password reuse across services. Attackers obtain large datasets of username and password combinations from previous data breaches, which are readily available on dark web marketplaces, and use automated tools to test these credentials against target login endpoints at scale. Unlike traditional brute-force attacks that try to guess passwords, credential stuffing uses known-valid credentials, which dramatically increases the success rate. Industry data suggests that credential stuffing attacks have a success rate between 0.1% and 2%, which translates to thousands of compromised accounts when millions of credentials are tested.

Why It Matters

Credential stuffing is one of the most economically efficient attacks available to threat actors. Breach compilations containing billions of credentials are available for negligible cost, and automated tooling like Sentry MBA, OpenBullet, and custom scripts make large-scale testing trivial. For organizations, the impact includes account takeovers leading to financial fraud, data exfiltration, and reputational damage. A successful credential stuffing campaign against a financial platform can result in millions of dollars in fraudulent transactions. Even non-financial applications suffer when compromised accounts are used for spam, phishing, or as pivots into corporate environments where employees reuse passwords.

Consider an attacker who purchases a breach dataset containing 10 million email and password combinations from a compromised forum. They target a popular e-commerce platform and, at a 1% success rate, compromise 100,000 accounts. Those accounts contain stored payment methods, shipping addresses, and order histories, all valuable for fraud and identity theft.

How Revaizor Handles This

Revaizor’s security testing platform evaluates login endpoints for vulnerability to credential stuffing by assessing rate limiting, account lockout policies, CAPTCHA implementation, and anomaly detection capabilities. The platform tests whether applications properly detect and block high-volume authentication attempts, whether they support multi-factor authentication, and whether account lockout mechanisms can be bypassed. Revaizor’s continuous testing model catches regressions when authentication defenses are inadvertently weakened by code changes or configuration updates.