Credential stuffing attacks use automated processes to test lists of stolen username and password pairs, and gain unauthorized access to websites and apps. These attacks can cause millions of dollars’ worth of damage. The tactic succeeds in part because, while users might be prompted to make a more complex password, password complexity policies have no way to account for people using the same credentials across various websites or services.

Below, we’ll take a closer look at what credential stuffing is, how it differs from brute force and password spraying, the kinds of fraud that can follow a successful takeover, and how to stop credential stuffing by mounting a layered defense.

Highlights

• Credential stuffing exploits password reuse across various accounts. Strong password policies are ineffective as a stand-alone defense against this kind of attack.

• Software-as-a-service (SaaS) and artificial intelligence (AI) platforms face risk from credential stuffing attacks, including account takeovers, application programming interface (API) abuse, and free tier farming.

• Effective defense requires layering bot detection, compromised credential screening, and shared signals across login, sign-up, and API endpoints.

What is credential stuffing?

Credential stuffing is a cyberattack in which criminals take username and password pairs leaked from previous data breaches and automatically test them against other services. It works because password reuse is common. Studies consistently show that many people use the same credentials across multiple accounts, with 72% of Gen Z reporting that they reuse passwords.

How does credential stuffing work?

Credential stuffing attackers source credential lists, sometimes called “combo lists,” from breach databases on criminal forums and darknet marketplaces. Some of these lists contain hundreds of millions of verified username and password pairs. They load these lists into software that can send millions of login requests, handle Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs), rotate through proxy Internet Protocol (IP) addresses, and parse success or failure responses.

To avoid simple rate-limiting rules, attackers send these login requests from thousands of different addresses, often residential proxies that resemble legitimate user traffic. When a login succeeds, the tool flags it. The attacker now has a verified, working account and can extract stored payment methods, personal data, API access, or account credits.

How is credential stuffing different from brute force and password spraying?

Credential stuffing, brute force attacks, and password spraying are related, but structurally distinct. The right countermeasure for one won’t necessarily stop the others.

Here’s a rundown:

• Brute force: A brute force attack systematically tries every possible password combination against a specific account. It’s computationally expensive, slow, and largely impractical against web-facing login endpoints with account lockout policies.

• Password spraying: A password spraying attack tries one or a few common passwords (e.g., Password1!, Welcome2024) across a large number of accounts. It’s designed to stay under per-account lockout thresholds while exploiting weak password choices. Password policies can prevent users from having these common passwords in the first place and are a helpful countermeasure.

• Credential stuffing: Sometimes considered a subcategory of brute force attacks, credential stuffing attacks run legitimate username and password pairs through unconfirmed login pages and take control of any accounts they get into. Standard password policies aren’t much help against credential stuffing because they can’t prevent customers from reusing passwords across different accounts.

What are the early warning signs of a credential stuffing attack?

Credential stuffing attacks rarely appear as one dramatic spike. They’re more often visible as a slow aggregation of signs.

Look out for the following:

• Elevated login failure rates across accounts: A brute force attack shows many failures on one account. Credential stuffing shows a moderate failure rate spread across thousands of accounts simultaneously. If your baseline failed login rate is 2%–5%, and it suddenly jumps to 15%–20% across the board, that change warrants immediate investigation.

• A sudden influx of novel IPs: Residential proxy networks generate requests from addresses that look clean on reputation lists. But a surge of IPs that have never previously come to your platform is a significant signal when correlated with login activity.

• Unusual geographic distributions: Logins clustered in regions that don’t match an account’s historical access patterns can indicate automated access.

• Consistent user-agent strings across many requests: Legitimate user populations show enormous variation in browser fingerprints. In credential stuffing attacks, the same user-agent string often repeats across thousands of requests, which is a pattern worth flagging.

• A high attempt-to-success ratio: Normal users fail once or twice, then either succeed or reset. Credential stuffing tools succeed infrequently but generate many attempts per session.

• Unexplained volume spikes at the login endpoint: Even with IP rotation, the endpoint itself sees more total requests during credential stuffing attacks. A huge increase without a corresponding marketing event or product launch is a warning signal.

Why is credential stuffing dangerous for SaaS and AI platforms?

Credential stuffing attackers verify working credentials to build a portfolio of access. After they get in, they can do even more damage.

If you operate a SaaS or AI platform, here’s what you need to know:

• Account takeover and data theft: Once they’ve gotten inside a customer account, attackers can exfiltrate personal data, payment details, private files, or communications. If your platform handles sensitive business data, a single compromised account can cause exposure beyond the attacker’s original target.

• Immediate account monetization: Attackers might cash out a compromised account right away. The account might be drained of credits or used for fraudulent purchases, or the credentials might be resold on criminal forums.

• Fraud via stored payment methods: Accounts with saved payment methods are high-value targets. Attackers can initiate purchases, transfer credits, or redirect payouts, which creates direct financial exposure for both the platform and its users.

• New account fraud: After they’ve confirmed your platform has accessible value, attackers often start creating more fraudulent accounts. Free trials, sign-up bonuses, and API access tied to new accounts are all targets.

• Free trial abuse and free tier farming: Credential stuffing can turn into other kinds of fraud. Attackers might cycle through free tier access across many accounts to avoid usage limits. This is a material financial problem for AI platforms, because giving away API calls and inference credits costs the platform directly.

• API abuse: Accounts on AI platforms typically come with API access and usage quotas. Attackers who take over accounts can exhaust those quotas, resell access, or use the computing resources for their own purposes. All of this creates infrastructure costs for the platform.

• Support burden: Customers whose accounts have been compromised require a lot of support. Password resets, fraud disputes, and account recovery workflows consume time, money, and other resources.

• Reputational damage: Users whose accounts are compromised on your platform might blame your platform, regardless of where their credentials originally leaked.

How can platforms fight credential stuffing attacks without damaging the login experience?

Credential stuffing attack prevention relies on layering multiple defenses so that each one raises the cost of an attack. Here are some of your options.

Nuanced rate limiting

If you set up blanket rate limiting by IP, attackers might circumvent it using proxy rotation. More effective approaches rate-limit by account (e.g., how many failed attempts did this specific account have in the last 10 minutes?), device fingerprint, or behavioral pattern. A combination is even better.

Bot detection and traffic analysis

Deploy a bot management solution that analyzes behavioral signals (e.g., mouse movement patterns, keystroke timing, request cadence, JavaScript challenge responses). Tools such as Cloudflare Bot Management or Akamai Bot Manager score traffic even before it reaches your authentication logic.

Compromised credential screening

The k-anonymity model allows users to check if their password has been compromised without sharing the full password. Services such as Have I Been Pwned’s Pwned Passwords tool use it to check whether a user’s password has appeared in known breach datasets.

Conditional multifactor authentication (MFA)

Rather than requiring them on every login, reserve MFA prompts for anomalous signals. If a login attempt comes from a new device, a new country, or after a long period of account dormancy, it’s a good time to require a second factor. This keeps the experience efficient for regular users while catching possible credential stuffing scenarios.

Mandatory MFA for high-value actions

Even if you don’t require MFA at login, it’s advisable that you require it before a user can change their email address, add a new payment method, or access API keys. This limits damage if a takeover does occur.

How Stripe Radar can help

Stripe Radar uses AI models to detect and prevent fraud, trained on data from Stripe’s global network. It continuously updates these models based on the latest fraud trends, protecting your business as fraud evolves.

Stripe also offers Radar for Fraud Teams, which allows users to add custom rules addressing fraud scenarios specific to their businesses and access advanced fraud insights.
Radar can help your business:

• Prevent fraud losses: Stripe processes over $1 trillion in payments annually. This scale uniquely enables Radar to accurately detect and prevent fraud, saving you money.

• Increase revenue: Radar’s AI models are trained on actual dispute data, customer information, browsing data, and more. This enables Radar to identify risky transactions and reduce false positives, boosting your revenue.

• Save time: Radar is built into Stripe and requires zero lines of code to set up. You can also monitor your fraud performance, write rules, and more in a single platform, increasing efficiency.

Learn more about Stripe Radar, or get started today.