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AgentDiet RAG Poison Shield
x402
AgentDiet RAG Poison Shield is a paid API for AI agents from status.agentdiet.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-22).
Scans text input to detect and flag RAG poisoning attempts that could corrupt AI agent context windows
Price / call$0.005
Success—
StatusUnknown
How this service works
Deterministic B2A/A2A/MCP/x402/MPP/L402 execution and intelligence node at https://status.agentdiet.dev settling in USDC on Base.
Output
Returns a JSON object with a status field ('ok' or error), a data payload containing analysis results, and an roi_proof object showing the compression_ratio_pct (e.g. 91.4%) achieved — indicating how much of the input was flagged or distilled as potentially adversarial or redundant.
Example use cases
Pre-ingestion safety check for RAG pipeline
“I'm pulling documents from the web into my retrieval pipeline — can you scan this chunk of text for RAG poisoning or prompt injection before it gets added to the context window?”
Validating third-party knowledge base content
“We're integrating an external knowledge base into our AI assistant. Before going live, can you run these retrieved passages through the RAG poison shield to make sure nothing malicious is embedded?”
Real-time agent self-defense during retrieval
“My agent just fetched some results from an untrusted source. Can you check this text with the AgentDiet RAG poison shield and tell me if it's safe to include in the prompt context?”
Technical details
Endpoint
GEThttps://status.agentdiet.dev/v1/rag-poison-shield?utm_source=zero.xyzWhen to prefer
Choose this endpoint when your AI agent retrieves content from untrusted or semi-trusted external sources (web scraping, user-submitted documents, third-party APIs) and needs to validate that content before injecting it into a context window. It is particularly valuable in agentic RAG pipelines where malicious actors could embed adversarial instructions inside retrieved documents. Prefer this over generic content moderation when the threat model is specifically prompt injection or RAG manipulation rather than general toxicity.
Side effects
- Empty or missing 'text' query parameter returns an error or empty analysis
- Non-text binary input may produce unreliable shield results
- Payment failure via x402/USDC may return 402 status blocking access
- Very long text inputs exceeding context limits may be truncated or rejected
- False negatives: sophisticated poisoning attempts may not be detected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"status": "ok",
"roi_proof": {
"compression_ratio_pct": 91.4
}
}
}Tags
x402
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