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AgentProbe v1.0.0 – Bilingual LLM-Agent Adversarial Test Pack (Case Retrieval)
x402
AgentProbe v1.0.0 – Bilingual LLM-Agent Adversarial Test Pack (Case Retrieval) is a paid API for AI agents from agentprobe.pythonanywhere.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Returns a filtered set of hand-authored, OWASP-mapped adversarial test cases for LLM agents in English or Chinese, parameterised by count, category, severity, seed, and language.
Price / call$0.003
Success—
StatusUnknown
How this service works
53 hand-authored, OWASP-mapped adversarial cases (EN+ZH) plus a zero-dependency MIT harness that scores any OpenAI-compatible LLM agent. US$6.99.
Output
A JSON object containing a filtered list of adversarial test cases. Each case includes a hand-authored adversarial prompt in the requested language (English or Chinese), its OWASP category mapping, severity level, and associated metadata. The total number of returned cases is bounded by the 'n' parameter and is reproducible when a seed is provided.
Example use cases
Red-team a customer-facing chatbot
“Give me 15 adversarial test cases in English, focused on the prompt-injection category and high severity, using seed 'chatbot-redteam-1' so I can reproduce the run later.”
Bilingual security audit for a Chinese-market agent
“Fetch 20 adversarial cases in Chinese across all categories, mixed severity, with seed 'zh-audit-2024' — I need to stress-test our Mandarin-language LLM agent.”
OWASP compliance spot-check
“Get me 5 adversarial cases mapped to critical severity in English using seed 'owasp-check' — I want a quick compliance spot-check against OWASP guidelines for our new AI assistant.”
Technical details
Endpoint
GEThttps://agentprobe.pythonanywhere.com/v1/casesWhen to prefer
Choose this endpoint when you need structured, OWASP-mapped adversarial prompts specifically designed to stress-test OpenAI-compatible LLM agents, especially when bilingual (EN/ZH) coverage or reproducible seed-based sampling is required. Prefer it over generic prompt datasets when you need curated, hand-authored cases with severity and category metadata for systematic red-teaming or compliance checks.
Side effects
- Invalid 'lang' value (not 'en' or 'zh') returns a validation error
- 'n' exceeding the total available case count (53) may return fewer results than requested
- Unknown or misspelled 'category' or 'severity' values may return an empty result set
- Payment failure (x402) blocks access to cases
- Server unavailability on PythonAnywhere free tier may yield timeouts
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| n | integer | |
| lang | string | |
| seed | string | |
| category | string | |
| severity | string |
Output
Schema · Response
{
"type": "object"
}Tags
x402
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