LLM relay gateways put an intermediary between the person using an AI model and the model provider. If the gateway sends requests through its own pooled API key or logged-in account, the provider may see the gateway’s credential and IP address—not the end user’s identity or network origin. Team Cymru reported more than 80,000 relay-related tags in a later, expanded aggregation, but that figure is not a count directly comparable to its initial scan of confirmed transfer stations.
How do LLM relay gateways hide who is using an AI model?
A relay gateway accepts a request from a downstream user, then forwards it to an AI provider using credentials controlled by the gateway operator. The user may sign in to the gateway, while the provider authenticates the gateway’s API key, OAuth token, subscription session, or other upstream credential. The provider consequently has a weaker view of who actually requested a response and where that person or workload connected from.
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What changes at each hop
- User to gateway: The consumer authenticates to the gateway, which may apply its own user management, billing, or access rules.
- Gateway to provider: The gateway forwards the request using an upstream credential and network connection associated with the gateway or its operator.
- Provider records: The provider can log the credential and source IP it sees. Those details may identify the gateway account and infrastructure rather than the person consuming the answer.
This separation can make it harder to attribute usage to a particular person, enforce per-user limits, identify abuse, or apply regional availability rules based on the consumer’s own connection. It describes a capability and a control risk; it does not mean every gateway operator or user is acting improperly.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTeam Cymru’s September 22, 2026 report describes Claude Relay Service (CRS 1.x) and its successor, sub2api (also called CRS 2.0). It says sub2api includes user management, per-user billing, subscription-to-API conversion, and prompt auditing. Those are toolkit capabilities, not evidence that a particular deployment used them for misuse.
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What does the 80,000 relay figure actually count?
The figure refers to Team Cymru’s later, expanded aggregation of gateway and relay tags, not a re-count of the confirmed CRS/sub2api transfer stations in its initial scan. The report dates the active volumes for the listed tags to September 21, 2026. A tag total should not be read as a directly comparable count of confirmed, unique running stations.
| Reported measure | What it represents |
|---|---|
| 10,867 confirmed transfer stations | Team Cymru’s initial eight-day scan in 2026. |
| 9,456 sub2api generation 2.0 stations | Count reported for that scan; Team Cymru, 2026. |
| 1,353 CRS generation 1.x stations | Count reported for that scan; Team Cymru, 2026. |
| 457 distinct ASNs | Network-level spread reported for the initial scan; Team Cymru, 2026. |
| More than 80,000 relay-related tags | Later, broader aggregation; listed tag active volumes dated September 21, 2026, by Team Cymru. |
The two initial software subtotals add to 10,809, which is 58 fewer than the reported total of 10,867. The figures as presented do not explain that difference, so the subtotals should not be treated as a complete breakdown of the total.
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What controls can a relay weaken?
- Account attribution: A provider-side credential may identify the gateway or account holder rather than each downstream user.
- Usage metering and rate limits: Multiple users may consume a shared credential or account, obscuring individual usage unless the gateway preserves and shares reliable per-user records.
- Abuse detection: Provider signals based on account behavior or source network may describe the relay’s activity rather than the end user’s.
- Regional controls: The provider may see a gateway’s network location, which can differ from the user’s location. That weakens controls that depend on the source IP visible to the provider; it does not by itself establish that a restriction was bypassed.
Team Cymru reported a broad hosting distribution and said no single hosting provider accounted for more than about 11% of the initial transfer-station population. It also listed 26 commercial sponsors on the sub2api GitHub page: 15 API relay resellers, seven residential proxy vendors, two AI account providers, one relay-optimized CDN, and one media-generation API. Those categories and counts do not establish the sponsors’ intent or show that any sponsor participated in a specific misuse.
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Did Team Cymru prove that relays stole credentials or distilled a model?
No. The report discusses credential sharing or resale, regional restriction bypass, and model distillation as possible uses or consequences. The secondary incident synthesis published September 23, 2026, says prompt contents were not visible and the actual purpose of the observed activity was not identified. It also says the provenance of credentials was unknown. Credential theft and model distillation therefore remain unverified possibilities, not findings about all observed relays.
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How to interpret the traffic figures
The secondary synthesis reports that, over the eight-day observation window, about 4,000 China/Hong Kong IP addresses and 304 U.S.-based transfer stations were observed, alongside about 14 TB uploaded and more than 7 TB downloaded. Those quantities describe reported transfers involving stations; they are not direct measurements of traffic to frontier-model providers. The synthesis also reports 81 GB uploaded and 1.4 GB downloaded for 17 relays connecting to Anthropic, but that traffic volume does not establish prompt content or purpose.
Bytes transferred alone cannot show that a model was extracted, that credentials were stolen, or that a provider’s terms were violated. Team Cymru says it contacted relevant AI vendors and shared discovered IPs; its geographic and policy conclusions are the report’s analysis, not a court or regulator finding.
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How can a company identify unauthorized AI gateways?
Do not rely on one indicator, such as an unfamiliar IP address or a usage spike, as proof of compromise. Correlate what endpoints and networks are doing with identity-provider records, credential issuance, and provider-side logs. Treat unusual patterns as leads to validate against approved applications and legitimate sharing arrangements.
Investigation checklist
- Inventory approved access: List AI accounts, API keys, OAuth grants, sessions, and applications. Identify which gateways and integrations are authorized, and check whether credentials are being sent to an unapproved one.
- Review provider records: Compare usage, source IPs, regions, token volumes, billing, rate limits, and audit logs with credential issuance records and known sharing arrangements.
- Inspect endpoints: Look for AI credentials in browsers, command-line tools, extensions, environment variables, configuration files, and local settings. Correlate any discovery with outbound network connections.
- Look for patterns: Investigate a credential appearing across many IPs or ASNs, geographically inconsistent use, sharp consumption spikes, or connections to known relay infrastructure. Confirm context before deciding that a credential is compromised.
- Contain with evidence: After validating suspicious use, rotate or revoke affected keys and sessions. Separate credentials by person and use case, keep their scope narrow, and limit their lifetime.
- Assess separate risks separately: Determine whether there was gateway activity, successful upstream authentication, unexpected quota consumption, credential compromise, policy evasion, or possible model extraction. Evidence for one does not automatically prove the others.
Are all AI gateways the same as relay infrastructure?
No. A gateway is an architectural pattern, not proof of malicious activity. A legitimate organizational gateway can route requests among providers, translate APIs, fail over, and record model, token, and dollar cost per request. Vercel’s official architecture material describes controls for attributing requests by user, feature, or key, applying budgets, and injecting provider credentials at routing time. That is one vendor’s account of its product and should not be generalized to every gateway.
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When assessing a managed or self-hosted gateway, check whether its design preserves the identity of the actual user or workload through each hop, not just whether it authenticates the gateway itself.
- Identity propagation: Can each request be tied to a person or workload?
- Credential controls: Are upstream keys isolated, scoped, and kept out of application code?
- Auditability: Do records connect the request to the user, model, credential, region, and spend?
- Regional policy: Can the organization enforce its allowed geography and provider rules in routing?
- Spend and rate controls: Are budgets and limits available per key or user?
- Failover behavior: Could a fallback change the provider, region, credentials, or billing behavior?
A gateway that answers these questions clearly can support centralized control. If identity, credential ownership, and request-level records disappear at the gateway boundary, provider-side logs alone may not tell an organization which person or workload was responsible.
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