Ban evasion is when a user who has been banned or suspended from a platform creates a new account to get back in and circumvent the enforcement decision. To standard signup checks the new account looks brand-new, because it arrives with changed surface details: a fresh email address or phone number; a rotated IP from a consumer VPN or residential proxy; a cleared browser, incognito session, or new device The person behind the account is the same one who was banned, so platforms treat ban evasion as a return of the same policy violator and an erosion of platform integrity ShieldLabs surfaces patterns indicating a returning banned user through persistent visitor and user identification that holds across sessions regardless of email, IP, or cleared cookies, so a repeat signup is recognizable on the first visit

How do banned users evade bans?

Banned users combine several techniques to look like a new person at signup, mostly aimed at resetting the identifiers a platform checks. The most common are: residential proxies and consumer VPNs to rotate the IP away from the banned one; anti-detect browsers that spoof device, operating system, and browser parameters; cleared cookies and incognito mode to defeat cookie-based tracking; a fresh email address or phone number for the new account; a new or factory-reset device, or a virtual machine, to escape device-level bans In gaming, evaders go further to escape hardware bans by spoofing hardware identifiers ShieldLabs links the new signup back to one persistent visitor and user through device fingerprinting, anti-detect browser detection, IP intelligence, and other anonymity signals, so the same person is recognizable on every signup even when the cosmetic identity has changed

Is ban evasion illegal?

In most jurisdictions ban evasion itself is not a criminal offense, but it is a Terms of Service violation. The legal exposure depends on what surrounds it: Terms of Service: platform terms typically prohibit creating a new account to circumvent a ban, making it grounds for permanent removal; Paired conduct: ban evasion can cross into legal territory when it accompanies the conduct that earned the original ban, such as harassment or fraud; Regulatory restrictions: it can breach a binding rule, for example an iGaming self-exclusion the operator is obligated to enforce ShieldLabs surfaces the evidence trail behind a return, the linked accounts, the shared device and network signals, and the timing, so the enforcement decision is grounded in data rather than guesswork

How does ShieldLabs detect ban evasion?

ShieldLabs runs through a single JavaScript snippet on your signup or account creation page and returns, on the first visit, the signals that reveal whether a new account belongs to an already-banned person. Each signup comes back with: a 0–100 risk score with a per-signal breakdown; persistent visitor and user identification that holds across cleared cookies, rotated IPs, and fresh accounts; anonymity signals such as VPN, proxy, Tor, anti-detect browser, and IP reputation; active patterns in analytics, including multi-accounting and account farms When a new signup matches an identity already linked to a banned account, the pattern surfaces it ShieldLabs delivers the risk score and signals via API and Webhook, so your code can block, hold for review, or apply any other downstream rule on the same request

How do I prevent ban evasion on my platform?

Preventing ban evasion comes down to identifying the person at signup rather than trusting the surface details of a new account, then holding that signup before it regains access. In practice it has three layers: Identity signals at signup: device fingerprinting, IP intelligence, anti-detect browser detection, and IP reputation that recognize a device or identity linked to a banned account; Pre-built patterns: patterns indicating multi-accounting and account farms that surface when one person controls many accounts; Enforcement-delay rules: hold or review a new signup that carries the signals of a previously banned identity, before it regains full access With a risk score and signal breakdown on each signup, a team can apply additional verification, a hold for review, or an auto-allow rule before a new account is granted access

How is ban evasion different from multi-accounting?

Ban evasion is the narrower case: the person has already been banned and creates a new account specifically to circumvent that enforcement decision, while multi-accounting is parallel volume abuse with no ban involved. The distinction: Multi-accounting: one person runs many accounts at once to farm rewards, bypass limits, or amplify reach; Ban evasion: one person who was already banned returns under a new identity to undo the enforcement; Shared root: both rely on the same person looking like a new one, so both surface through the same persistent visitor and user identification ShieldLabs covers both with one snippet. Pick ban evasion prevention when the concern is repeat offenders returning after a ban, or multi-accounting detection when it is parallel accounts gaming your system. Many platforms run both

Does ban evasion detection work for gaming and iGaming?

Yes. The mechanism is universal because it recognizes the person, not the platform, and two verticals have a sharper version of the problem: Gaming: bans are often tied to hardware, and a banned player tries to return by spoofing hardware identifiers or moving to a new device, sometimes called HWID ban evasion. ShieldLabs flags the returning player at account signup so the operator can keep the ban in force; iGaming: ban evasion overlaps with self-exclusion, where a player who has self-excluded tries to open a new account and keep playing. ShieldLabs flags the returning self-excluded player at signup so the operator can enforce the exclusion ShieldLabs applies the same web-layer identification across marketplaces, social platforms, dating apps, and communities, so any platform that issues bans can recognize a banned user returning under a new account

Will ShieldLabs automatically block banned users?

No. ShieldLabs blocks nothing automatically. It returns a risk score and a full signal breakdown, and your code decides what to do. You set your own thresholds against the score, for example: a mid-range score triggers additional verification before granting access; a high score holds the signup for moderator review or links it to the prior banned account; a low score lets a legitimate new user through with no extra steps ShieldLabs includes 5,000 free identifications so you can tune those thresholds on real traffic before enabling rules in your own code

Ban Evasion Prevention

Prevent ban evasion and make your bans stick

Make every ban final, and stop one banned user from quietly returning under a new account

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Stop ban evasion at signup

Recognize a banned user the moment they create a new account

Cut repeat ban work

Catch the evader at signup instead of finding and banning them again

Act on evidence, not guesswork

Link a new signup to a banned account with a clear signal trail

Keep real users friction-free

Legitimate new signups join without extra verification or hold steps

How ShieldLabs prevents ban evasion

ShieldLabs recognizes the real visitor behind every anonymous session, so your team can catch a banned user the moment they return under a new account and protect platform integrity

Accurate Identification

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

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, 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 multi-accounting, account farms, 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

Start preventing ban evasion in 5 minutes

Easily integrate into any signup, login, or account creation 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

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "public_ip": { "ip": "62.197.149.124", "country": "United States" },
  "local_ip": { "ip": "45.83.91.7", "country": "United States" },
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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