Stop account takeover fraud with ready-made detection

Account takeover (ATO) is unauthorized access to and use of an existing user account, where someone logs in as the real account owner using stolen credentials. Unlike account creation fraud, no new account is involved, the existing user relationship is exploited.

• Entry method: stolen credentials, phishing, or session hijacking.
• Goal: unauthorized transactions, data theft, or reselling access.
• Difference vs. account creation fraud: the account already exists and belongs to a real user.

ShieldLabs detects account takeover directly as a High-Risk Event when a login comes from an unrecognized device or connection, even when valid credentials are used.

Account takeover fraud is account takeover carried out for financial gain, turning unauthorized account access into money or resaleable data. It is one of the most common forms of online fraud and costs platforms on two fronts.

• Direct cost: unauthorized purchases, fund transfers, and chargebacks.
• Indirect cost: loss of user trust and reputation damage.
• Typical outcomes: data theft, resale of account access, fraudulent transactions.

ShieldLabs scores every visitor and user with risk signals and a 0–100 Risk Score, and flags suspicious access before the session is established.

The clearest red flags for account takeover are a login from an unfamiliar device or connection paired with anomalies the real owner wouldn't produce. Most ATO attempts show one or more of the following.

• Login from a device or browser never previously associated with the account.
• Access through a VPN, proxy, or other anonymized connection.
• OS or browser fingerprint inconsistencies (a cross-layer mismatch).
• Logins from geographically impossible locations within a short window.
• Bulk credential testing from the same IP or egress range.

ShieldLabs surfaces all of these through 300+ signals analyzed on every login attempt, and detects account takeover and impossible travel directly as High-Risk Events.

ShieldLabs detects account takeover by assigning a persistent identifier to every visitor and user, then flagging logins that come from a device or connection never previously tied to that account. The identifier survives cookie clearing and IP changes, so a returning fraudster can't look like a fresh user.

• Persistent identification: VisitorID and DeviceID link the session to the real account history.
• High-Risk Event: a login from an unrecognized device or connection is detected as account takeover, with Medium or High confidence.
• Signal layer: 300+ signals check for VPN, proxy, anti-detect browsers, timezone mismatch, and other risk signals on every login.

ShieldLabs delivers the resulting Risk Score and signal breakdown via the analytics dashboard, API, and Webhooks, so you can act on the result in your backend.

Credential stuffing is an automated method where stolen username and password pairs from data breaches are tested against a target platform at scale. It works because people reuse the same passwords across multiple services, so a fraction of stolen pairs unlock accounts on the target.

• Source: leaked credential lists from unrelated data breaches.
• Method: high-volume automated login attempts, often from rotating IPs.
• Why it works: password reuse across different services.

ShieldLabs flags credential stuffing through risk signals on the bulk login attempts and anonymized connections behind it, and detects any resulting account takeover as a High-Risk Event.

ShieldLabs flags risky logins and helps block fraudulent and abusive traffic. On every login it returns a Risk Score, risk signals and High-Risk Events, and you choose the action for each case.

• Suspicious score (30-59): require 2FA or another step-up check.
• Dangerous score (60-100): deny the session or hold it for review.
• Trusted score (0-29): let the real user in with no added friction.

ShieldLabs includes 5,000 free identifications, so you can see the results on your own traffic first.

MFA and ShieldLabs solve different parts of the login problem: MFA verifies the person holds a second factor, while ShieldLabs assesses whether the login attempt itself looks suspicious. They are complementary, not alternatives.

• MFA: confirms possession of a second factor (code, app, key) after credentials are entered.
• ShieldLabs: scores the device, connection, and session context on every login, before authentication.
• Together: ShieldLabs flags high-risk attempts so you can trigger MFA only when context warrants it.

ShieldLabs decides when to trigger MFA, rather than replacing it, keeping low-risk logins frictionless and reserving the extra step for sessions that need it.

Common account takeover examples are credential stuffing, phishing, session hijacking, and SIM swapping, each ending with a login from a device or connection the real owner never used. The mechanics differ but the outcome is the same: someone other than the owner gains access.

• Credential stuffing: breached password lists tested at scale against banking or e-commerce logins.
• Phishing: users redirected to fake login pages that harvest their credentials.
• Session hijacking: a valid authentication token stolen and reused after login.
• SIM swapping: porting a phone number to bypass SMS-based 2FA.

ShieldLabs detects these the same way regardless of method, as an account takeover High-Risk Event, by recognizing the unrecognized device or connection behind the login, often paired with fraudulent purchases or fund transfers right after access.

Account compromise detection is the practice of identifying when a legitimate account is being used by someone other than its owner. Unlike fraudulent-transaction detection, it operates at the login level rather than waiting for a payment to go wrong.

• Device recognition: is this a device the account has used before?
• Connection analysis: VPN, proxy, anti-detect browser, or residential ISP egress IP.
• Session context: timezone, OS, and browser consistency against the account's history.

ShieldLabs detects account compromise on every login by analyzing 300+ device, OS, browser, IP, and network signals and flagging sessions that deviate from the account's established identity.

Account takeover losses reached 15.6 billion USD in the US in 2024, with 2025 projections near 17 billion USD, making ATO one of the fastest-growing categories of online fraud. The trend lines all point in the same direction.

• Losses: 15.6B USD (2024) → ~17B USD projected (2025).
• Incidence: suspected ATO fraud rates rose 37% from 2024 to 2025.
• Credential stuffing: volume grew 148% year-over-year through Q4 2025.
• Prevalence: ATO has surpassed ransomware as the top enterprise security concern, with 83% of organizations reporting at least one incident.

ShieldLabs works at the device and signal level, surfacing the login anomalies that credential-only defenses miss.

ATO prevention is the set of technical measures that keep unauthorized users out of existing accounts, and it sits within broader account fraud prevention. The defining trait is that it works at the login level, not after a transaction.

• Beyond credentials: evaluates the device, connection, and session context of every login.
• Continuous: applied to each login attempt, not just at account creation.
• Risk-based: lets you reserve friction (like 2FA) for attempts that look anomalous.

ShieldLabs implements ATO prevention by assigning persistent identification to every visitor and scoring 300+ signals on every login, flagging suspicious access on the first visit.

Account takeover protection is the ongoing capability to detect unauthorized account access across all login attempts, so suspicious logins can be challenged or denied. It differs from one-time verification by monitoring every login continuously, not just at account creation.

• Continuous: every login is evaluated, not only the first one.
• Identity-aware: compares each attempt against the user's established device and connection history.
• Actionable: returns a Risk Score, signal breakdown and High-Risk Events to act on in your backend.

ShieldLabs provides account takeover protection by scoring every login from 0 to 100 and detecting account takeover as a High-Risk Event when an attempt deviates from the user's established identity.