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Credential Stuffing Attacks

Credential stuffing is a type of account takeover cyber attack where attackers use automated tools to try large sets of usernames and passwords on various online platforms. These credentials often come from previous data breaches. Unlike brute force attacks, which aim to guess login details, credential stuffing relies on the fact that people reuse passwords across multiple services.

Once an attacker gains access to a user account, they can steal sensitive information, make fraudulent purchases, or commit other malicious acts. This type of attack is especially effective for consumer-focused platforms like online shopping websites and streaming services, where individuals are more likely to use the same credentials they've used elsewhere.

Basic Steps in Credential Stuffing

1. Data Collection: Attackers accumulate credentials from previous leaks, often trading or buying them on the dark web.
2. Automation: They use automated tools, like bots, to enter the credentials into various websites quickly.
3. Verification: Successful logins indicate that the credentials are valid for that particular service.
4. Exploitation: Attackers access the accounts to steal information, money, or even lock the original user out.

Risks for Companies

Companies are at risk of reputational damage and potential legal action if they can't protect their users from credential stuffing. Not only do they have to worry about data loss, but they also need to consider the increased infrastructure costs due to the high volume of automated login attempts.

Defensive Measures

To defend against these attacks, organisations can take several measures:

1. Multi-Factor Authentication (MFA): Requires users to verify their identity through more than one method, making it harder for attackers to gain access.
2. Rate Limiting: Limits the number of login attempts from connections with certain characteristics within a set timeframe, eg same IP address, TLS Fingerprint .
3. CAPTCHA: Tests to confirm that the user is human, which can slow down automated login attempts.
4. Residential Proxy Detection: determine whether the login attempt is coming over a residential proxy.
5. Monitoring and Alerting: Real-time analysis of login patterns to identify and flag suspicious activity.
6. Credential Screening: Regularly cross-referencing user credentials against known data breaches to prompt password changes for compromised accounts.
7. User Education: Educate users on the importance of unique and strong passwords for each online service they use.
8. Anomaly Detection: Use machine learning to identify unusual patterns in login activity, such as multiple login attempts from various geographical locations in a short time.