What is account sharing?

Account sharing, also referred to as sharing accounts, occurs when one user's login credentials are used by more than one person, ranging from casual household sharing to organized credential resale. Casual sharing: household members or teammates using a single paid login.; Plan limit bypass: one account serving several users to avoid buying more seats.; Credential resale: logins distributed or sold on credential marketplaces. For SaaS and subscription businesses, this quietly reduces revenue and distorts usage metrics. ShieldLabs surfaces patterns indicating account sharing by linking every session to a persistent identity, so you can see when one account is used by many distinct devices.

What is account sharing detection?

Account sharing detection is the process of identifying when a single account is being used by multiple people, simultaneously or across different devices and locations. It works by assigning a stable identity to each session rather than trusting the login alone: Persistent identification: every visit is tied to a device and visitor identifier that survives cleared cookies and rotated IPs.; Cross-session linking: distinct identities accessing one account are correlated over time.; Pattern signals: multiple devices, impossible travel, and anonymity tools indicating shared access. The result is a view of how many distinct people sit behind one login, even when their sessions never overlap.

How to detect account sharing?

To detect account sharing, track which devices and connections access each account over time and watch for activity that no single user could produce. The key signals are: Multiple device identities on one account that persist across cleared cookies and rotated IPs.; Impossible travel: logins from geographically distant locations within an unreachable timeframe.; Anonymity tools: VPN, proxy, or anti-detect browsers used to mask shared access. ShieldLabs surfaces these patterns automatically through 100+ device, OS, browser, IP and network signals collected on every session, with a Risk Score and signal breakdown on the first visit.

How do streaming platforms detect account sharing?

Streaming platforms detect account sharing by tracking which devices and IP addresses access each account over time and flagging activity outside a single household. They typically combine: Household IP checks: grouping a primary location and counting outside connections.; Device fingerprinting: counting distinct devices linked to one account.; Anonymity signals: VPNs or proxies used to disguise out-of-household access. Netflix, for example, pairs household-level IP checks with device fingerprinting to enforce its account sharing policy. ShieldLabs gives any platform the same building blocks as a ready-made service: 100+ device, OS, browser, IP and network signals per session, anonymity signals, and a Risk Score on the first visit, without building a streaming-scale engineering team.

What is password sharing detection?

Password sharing detection is a specific form of account sharing detection focused on identifying when one set of login credentials is being used by multiple people. It never inspects the password itself. Device-based, not password-based: it relies on persistent device and visitor identity, not credential contents.; Distinct-device counting: too many separate devices on one account in a short window is the core trigger.; Severity, not blocking: each pattern carries a risk level (Suspicious or Dangerous) for your code to act on. ShieldLabs surfaces password sharing by linking each session to a persistent device identity: when multiple distinct devices access one account, a pattern indicating account sharing fires with a risk level you can route into your own rules.

How does ShieldLabs detect account sharing?

ShieldLabs assigns an encrypted persistent user link on every session that connects each visit to the account in your system, then surfaces patterns when one account behaves like many users. Identity linking: multiple distinct identities on one account across different devices, OS profiles, or locations fire a pattern indicating account sharing.; Anonymity checks: VPN, proxy, and anti-detect browser signals run on every session, since shared access is often masked.; Impossible travel: a pattern fires when the same account logs in from locations that cannot be reached in the elapsed time. ShieldLabs returns these patterns with a Risk Score and signal breakdown on the first visit, so your team can decide how to respond without ShieldLabs blocking anyone.

What is impossible travel detection?

Impossible travel detection flags when one account is accessed from two geographically distant locations within a timeframe that makes physical travel between them impossible. What it measures: the distance and time gap between consecutive logins on the same account.; What it indicates: either account sharing across people, or account takeover from a new location.; Example: a login in New York followed by a login in Singapore 30 minutes later. ShieldLabs surfaces this automatically through a pattern indicating impossible travel and returns a risk severity rating, so you can challenge or re-authenticate the session in your own flow.

Will ShieldLabs automatically block shared accounts?

No. ShieldLabs never blocks anyone automatically; it returns a Risk Score and signal breakdown, and your code decides what to do. Common responses teams build on top: Show a plan upgrade prompt when a sharing pattern appears in your traffic.; Restrict the number of concurrent sessions on the account.; Require re-authentication from an unrecognized device. ShieldLabs gives you the free tier's 5,000 identifications to test this logic on real traffic before you enable any production rules.

How is this different from concurrent session limits?

A concurrent session limit is a hard cap on simultaneous logins; it is a business rule, not a detection system, and it only sees sessions that overlap in time. Concurrent limits: count sessions active at the same moment, and miss sharing that takes turns.; Identity-based detection: counts distinct device identities on an account over time, even when sessions never overlap.; Coverage: catches casual, sequential sharing that a session cap lets through. ShieldLabs identifies the number of distinct device identities accessing an account over time, so it surfaces even take-turns sharing, such as family members using one login at different times.

What is password sharing?

Password sharing occurs when one set of login credentials is used by multiple people who each have their own device and identity. It is the everyday form of account sharing on subscription platforms. Household: one family member's streaming login used across the home.; Team: one colleague's SaaS seat shared instead of buying more.; Group: one course or membership login passed around a cohort. For platforms this means lost subscription revenue and distorted usage metrics. ShieldLabs surfaces a pattern indicating password sharing by identifying when multiple distinct device identities access the same account over time.

Account Sharing Prevention

Account sharing detection that protects subscription revenue

Prevent credential sharing, plan limit bypass, and unauthorized access without adding friction for paying customers, through persistent identification and real-time risk scoring

Start Free

Maximize subscription revenue

Identify shared accounts and convert sharers into paying seats

Detect unauthorized account access

Flag one account used from multiple sessions or locations simultaneously

Stop plan limit bypass

Catch one account serving multiple users that should be on a higher plan

Keep paying customers friction-free

Minimize false positives and let trusted single users move freely

How ShieldLabs prevents account sharing

ShieldLabs recognizes the real visitor behind every anonymous session, so your team can catch account sharing and unauthorized multi-user access in real time

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, rotated IP and incognito mode

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, geolocation spoofing, IP reputation and other anonymity signals on every visit, with up to 99% accuracy

Risk Score

A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it

Ready-Made Patterns

Detection of the hidden connections between accounts that may signal account sharing, impossible travel, and more

Real-Time Analytics

See how much of your traffic is masked, with an overall quality score and a breakdown of the sources sending your visits

Coverage for every subscription model

Stop revenue leakage from credential sharing before it distorts metrics, undermines pricing, or exposes user data

Detect multi-device account access

Accurate Identification helps flag one account accessed from multiple devices

Surface impossible travel patterns

In-depth pattern analytics help identify same-account logins from impossibly distant locations

Expose anonymity-masked sharing

Anonymity signals reveal shared accounts hiding behind VPN, proxy, or anti-detect browsers

Keep legitimate users friction-free

Ready risk score helps apply friction only to sessions that deviate from single-user patterns

Start preventing account sharing in 5 minutes

Easily integrate into any login or session flow

1
Create your account
Get 5,000 free identifications
2
Add the snippet
It identifies every visitor and returns their anonymity signals and a risk score
3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
4
Use the API and Webhooks
Apply the risk score and anonymity signals in your rules to stop fraud and abuse