Seneschal Counter-MEV Risk Feed is a paid API for AI agents from api.seneschal.space, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Returns a risk-scored feed of malicious ERC-20 approval spenders, honeypot/bait tokens, and dangling live approvals detected on Ethereum mainnet
Counter-MEV / approval-risk feed: the full risk-scored set of (1) malicious spenders harvesting ERC-20 approvals to transferFrom-drain them (the JaredFromSubway $7.5M pattern), (2) honeypot/bait tokens — fake fWETH/fUSDC wrappers, fee-on-transfer and sell-revert traps, (3) live dangling approvals at risk. Each row ships its flags + score factors; sourced from our own mainnet scanners. Filters: ?category=spenders|tokens|approvals|bleeding_bots&min_score&limit.
A JSON object with an as_of_ms timestamp and arrays of risk-scored records per category: malicious_spenders (addresses harvesting ERC-20 approvals), risky_tokens (honeypots, fake wrappers, fee-on-transfer/sell-revert traps), live dangling approvals, and bleeding bots — each row includes its risk score and the contributing score factors.
GEThttps://api.seneschal.space/v1/premium/counter-mevUse this endpoint when you need real-time, mainnet-sourced threat intelligence specifically about ERC-20 approval exploits, honeypot tokens, and MEV-related wallet-drain patterns. Prefer it over generic token scanners when you need pre-scored, multi-category risk data with granular score factors rather than simple blocklists. Best for DeFi security agents, wallet protection systems, or trading bots that need to screen approvals and tokens before execution.
| Field | Type | Description |
|---|---|---|
| properties | string |
{
"type": "json",
"example": {
"as_of_ms": 1765532000000,
"risky_tokens": [
{
"flags": [
"lookalike_symbol",
"event_balance_mismatch"
],
"symbol": "fWETH",
"address": "0xf00…",
"risk_band": "critical",
"risk_score": 92
}
],
"malicious_spenders": [
{
"flags": [
"transferfrom_sweeps",
"drains_bluechips",
"mass_approvals"
],
"address": "0xbad…",
"risk_band": "critical",
"risk_score": 97,
"distinct_victims": 31
}
]
}
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