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A2A Indirect Prompt-Injection & Jailbreak Firewall
A2A Indirect Prompt-Injection & Jailbreak Firewall is a paid API for AI agents from llms-generator.agentdiet.dev, paid per call via MPP, $0.005/call, status unknown (last checked 2026-10-21).
Scans untrusted text, HTML, emails, MCP responses, or A2A payloads for hidden prompt injections and jailbreak attempts, returning sanitized output.
Price / call$0.005
Success—
StatusUnknown
How this service works
Scans untrusted web pages, emails, MCP responses, or A2A payloads for hidden prompt injections and returns sanitized text. (Live x402 v2, IETF MPP & MCP endpoint at https://llms-generator.agentdiet.dev/v1/tool-poisoning-shield)
Output
Returns a JSON object containing the sanitized version of the input text with detected prompt injection or jailbreak payloads removed or flagged, along with threat detection metadata indicating whether malicious content was found.
Example use cases
Screening scraped web content for agents
“I'm building a research agent that fetches web pages — can you scan this HTML I just scraped before I feed it to the LLM? I want to make sure there are no hidden prompt injections buried in it.”
Validating MCP tool responses before use
“My agent just got back a response from an external MCP tool. Can you check this payload for embedded jailbreak instructions or adversarial content before I let the agent act on it?”
Filtering phishing emails in an AI pipeline
“I'm running emails through my AI assistant — can you scan this email body for any hidden prompt injection attempts that might try to hijack my agent's behavior?”
Technical details
Endpoint
GEThttps://llms-generator.agentdiet.dev/v1/tool-poisoning-shield?utm_source=zero.xyzWhen to prefer
Use this endpoint when your AI agent ingests content from untrusted external sources — scraped web pages, emails, MCP tool responses, or A2A payloads — and you need to guard against indirect prompt injection attacks before that content influences agent behavior. Prefer this over generic content filters when your threat model is specifically adversarial LLM manipulation rather than spam or toxicity.
Side effects
- Empty or missing 'text' parameter returns a validation error
- Extremely long inputs may be truncated or time out
- False negatives possible — novel or obfuscated injections may not be caught
- Response schema is loosely typed (generic object), so structure of results may vary
- Payment failure via x402 may result in a 402 status and no scan result
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | Untrusted text, HTML, or tool output to scan for hidden prompt injection or jailbreak payloads |
Output
Schema · Response
{
"type": "object"
}Reviews
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