How does Credential Stuffing Differ from Other Account Takeover Attacks?

Account Takeover Attacks

December 27, 2024

Credential stuffing is a type of account takeover attack where hackers use large volumes of stolen username-password pairs to try logging into multiple websites.

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Unlike other account takeover attacks, such as brute force, credential stuffing relies on the fact that many people reuse the same login credentials across different sites.

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Revisiting Credential Stuffing

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Credential stuffing is a specific attack method that takes username-password pairs obtained from previous data breaches and tests them on different sites or services. The idea is straightforward: many people reuse their credentials across platforms.

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So, if a hacker has your email and password from a leaked gaming site, they’ll try the same combination on banking, social media, or e-commerce sites.

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Credential Stuffing in Action

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Stolen Credentials: Hackers obtain credentials from publicly available breach dumps or sell them on dark web forums.
Automation Tools: Using bots or tools like SentryMBA or OpenBullet, attackers automate login attempts across multiple services.
Successful Attempts: If you’ve reused your credentials, they gain access to your accounts.

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Credential Stuffing vs. Other Account Takeover Attacks

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Credential stuffing is one of many account takeover attacks, but its unique reliance on breached credentials and high automation sets it apart from other methods:

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Aspect	Credential Stuffing	Brute Force	Phishing	Keylogging	Session Hijacking	Man-in-the-Middle (MitM)
Method	Reuses breached username-password pairs across multiple websites.	Attempts to guess passwords through brute force or dictionary attacks.	Tricks users into submitting credentials via fake login pages or emails.	Captures credentials using malware to log keystrokes.	Steals session cookies or tokens to impersonate users without needing credentials.	Intercepts communication to steal credentials during login.
Data Used	Pre-existing, breached credentials from past data leaks.	No pre-existing data; relies on password guessing.	Newly stolen credentials obtained through user deception.	Credentials directly stolen during user activity.	Session tokens or cookies intercepted during active user sessions.	Data captured during communication, often in plaintext if encryption is absent.
Interaction with Authentication Mechanism	Directly interacts with login systems using valid credentials.	Repeatedly attempts to log in with different password guesses, causing failures.	Bypasses authentication by tricking users into revealing credentials.	No direct interaction; focuses on stealing data locally from infected devices.	Bypasses authentication by leveraging stolen session tokens.	Captures login data during transit but often avoids direct interaction.
Automation Level	Highly automated using tools like SentryMBA or OpenBullet for massive login attempts.	Automated but requires significantly more computational effort due to guessing.	Low automation for phishing setup, but scalable via automated distribution tools.	Minimal automation; requires deploying malware manually.	Minimal automation; relies on real-time access to active sessions.	Moderate automation using sniffing tools to capture network traffic.
Success Criteria	Success depends on password reuse across platforms and bypassing basic protections.	Success depends on weak or predictable passwords (e.g., "123456").	Success requires users falling for deceptive tactics and submitting credentials willingly.	Requires successful malware installation on the victim’s device.	Depends on access to session tokens, often through unprotected network activity.	Success depends on capturing unencrypted data or bypassing encryption during transmission.
Detection & Evasion	Uses IP rotation, CAPTCHA evasion, and behavioral mimicry to evade detection.	Generates high failure rates, making it easier to detect with rate-limiting or anomaly detection.	Detection focuses on identifying phishing domains, emails, or links.	Endpoint security tools or antivirus software can detect keyloggers.	Unusual session duplication or behavior can trigger alerts.	Easily detected with encrypted traffic like HTTPS (TLS).
Impact on Systems	Overwhelms login systems with high volumes of traffic, degrading performance or locking users out.	Similar system strain due to excessive failed login attempts but is often more obvious.	Minimal impact on systems unless phishing campaigns are deployed at scale.	No strain on systems; focuses on compromising individual devices.	Minimal impact on systems but leads to targeted account takeovers.	Minimal system-wide impact but can affect multiple users on vulnerable networks.
Effort Level	Low (credentials are pre-obtained; relies on automation).	High (requires extensive attempts to guess passwords).	High (requires crafting convincing phishing lures and fake sites).	High (requires deploying and managing malware).	High (requires real-time interception of sessions).	High (requires access to networks or physical interception points).
Examples	Using a leaked Netflix password to access Spotify or banking accounts.	Trying thousands of passwords on one account until successful (e.g., guessing "password123").	Fake PayPal or Microsoft email redirecting users to a phishing site for credentials.	Malware logging your keystrokes while typing into a bank’s website.	Stealing a session token during a public Wi-Fi login to impersonate a user.	Capturing login credentials via an insecure public Wi-Fi network.

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Why Credential Stuffing Is Rising While Other Attacks Are Evolving

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Credential stuffing has surged in popularity due to:

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Massive Data Breaches: The growing frequency of breaches provides attackers with a steady supply of credentials.
Readily Available Tools: Credential stuffing tools are widely accessible, lowering the barrier for attackers.
Password Fatigue: Users overwhelmed by password complexity requirements often reuse credentials.

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Other attacks, like phishing and MitM, have become more targeted, while credential stuffing remains a volume-based game leveraging systemic issues in password management.

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Challenges in Detecting Credential Stuffing vs. Other Attacks

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While all account takeover attacks can cause security breaches, detecting credential stuffing poses unique challenges due to its stealth and automation:

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Challenge	Credential Stuffing	Other Attacks
Failed Login Patterns	Mimics legitimate failed login attempts, often spread across many IPs.	Brute force generates high volumes of failed logins, triggering easier anomaly detection.
Rate-Limiting Circumvention	Uses proxy networks to evade rate-limiting mechanisms.	Session hijacking or phishing rarely involves repeated attempts; no rate-limiting evasion.
Behavioral Analysis Evasion	Mimics user behavior, including delays between login attempts.	Keylogging and MitM don’t interact directly with login systems, avoiding such analysis.