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Silicon DNA Classifier
x402
Silicon DNA Classifier is a paid API for AI agents from rtt.phoenix-ai.work, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Classifies a client as HUMAN, LEGIT_AGENT, or MALICIOUS_BOT by analyzing IP address, HTTP headers, TLS fingerprint, timing jitter, behavioral entropy, and NIC fingerprinting.
Price / call$0.01
Success—
StatusUnknown
How this service works
Silicon DNA agent identity classification. Behavioral timing analysis: CPU jitter entropy (L0), Shannon entropy (L2), Spearman rank correlation (L3) to classify as HUMAN, LEGIT_AGENT, MALICIOUS_BOT, SYBIL, or UNKNOWN (no evidence either way). Per paper Section 5.1. (GET variant: parameters in the query string.)
Output
Returns a classification label — one of HUMAN, LEGIT_AGENT, or MALICIOUS_BOT — derived from multi-signal analysis of the submitted IP address and HTTP headers, including TLS fingerprint characteristics, timing jitter, behavioral entropy, and physical-layer NIC fingerprinting data.
Example use cases
Gate API access by traffic type
“Before letting this request through, check whether the caller at IP 198.51.100.17 with these headers is a human, a trusted agent, or a malicious bot — I only want to serve real users and legit agents.”
Flag bot traffic in fraud review pipeline
“Run these request headers and the source IP 192.0.2.88 through your Silicon DNA classifier so I can tag any malicious bots hitting our checkout endpoint before they complete a transaction.”
Audit mixed inbound traffic for agent fleet
“I'm seeing a mix of traffic sources — can you classify each connection by IP and headers to separate legitimate AI agents from humans and any malicious bots so I can route them appropriately?”
Technical details
Endpoint
GEThttps://rtt.phoenix-ai.work/api/v1/classify?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need real-time, network-layer bot/human/agent classification that goes beyond user-agent string inspection — particularly when you want TLS fingerprint, timing jitter, and NIC-level signals factored into the decision. Best suited for access-gating, fraud prevention pipelines, or traffic routing where distinguishing legitimate AI agents from malicious bots is critical.
Side effects
- Missing or malformed IP address returns a validation error
- Empty or incomplete headers object may reduce classification accuracy or return an error
- Ambiguous or spoofed signals may result in low-confidence classifications
- Service unavailability returns a non-200 status; agents should implement retry logic
- VPN or proxy-masked IPs may reduce classification reliability
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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