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Data Poisoning Detector MCP
x402
Data Poisoning Detector MCP is a paid API for AI agents from api.m2mcent.com, paid per call via x402, $0.22/call, status unknown (last checked 2026-09-15).
Analyzes input data to detect signs of data poisoning attacks or adversarial manipulation in datasets or model training inputs
Price / call$0.22
Success—
StatusUnknown
How this service works
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Output
Returns an assessment of whether the submitted data contains signs of data poisoning, adversarial manipulation, label flipping, or backdoor injection, likely including a verdict, confidence level, and details about detected anomalies or suspicious patterns.
Example use cases
Verifying ML training data integrity
“I'm about to train a model on this collected dataset — can you run it through the data poisoning detector to make sure no one has tampered with or injected adversarial samples into it? Here's the serialized data: 'sample_id,label,value\n1,cat,0.45\n2,dog,0.88\n3,cat,99.1'”
Detecting backdoor injection in NLP corpus
“Check this text corpus for signs of data poisoning or backdoor triggers before I use it for fine-tuning: 'The product is great. The product is great CF. The service was excellent. The service was excellent CF.'”
Auditing third-party data supply chain
“I received this dataset from a third-party vendor and I want to verify it hasn't been poisoned before ingesting it into our pipeline. Can you run a data poisoning detection check on it? Data: 'user_id,score,category\n101,0.9,A\n102,0.1,B\n103,999.0,A'”
Technical details
Endpoint
POSThttps://api.m2mcent.com/data-poisoning-detector-mcp/api/processWhen to prefer
Use this endpoint when you need to programmatically verify that a dataset or training corpus has not been tampered with through data poisoning techniques before using it in machine learning pipelines. Prefer this over manual inspection when dealing with untrusted third-party data sources, automated data pipelines, or when building AI safety and integrity guardrails into an agentic workflow.
Side effects
- Empty or malformed data string returns validation error
- Extremely large data payloads may time out or be rejected
- Non-string or unsupported data formats may cause parsing failures
- Payment failure (402) if USDC balance is insufficient
- Ambiguous or benign anomalies may produce false positives
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| payload | string |
Output
Schema · Response
{
"type": "json",
"example": {
"success": true
}
}Tags
x402
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