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Agent Workflow Red Team Analyzer
x402
Agent Workflow Red Team Analyzer is a paid API for AI agents from api.iamjuliand.com, paid per call via x402, $0.12/call, status unknown (last checked 2026-09-14).
Performs automated red-team security analysis of an AI agent workflow, identifying prompt injection risks, policy gaps, and high-impact tool vulnerabilities.
Price / call$0.12
Success—
StatusUnknown
How this service works
A public journal about ideas, systems, experiments, projects, and learning.
Output
Returns a JSON object containing: a risk score (0-100), a natural language summary of vulnerabilities found, arrays of discovered attacks and findings, static analysis results (policy gaps, approval gaps, high-impact tools), suggested mitigations, regression test cases, and metadata about the model used, token usage, cost, and whether external tools were executed or secrets were redacted.
Example use cases
Audit customer support agent pipeline
“Red-team my customer support agent — it reads user-submitted tickets and can call send_email and update_ticket tools. Tell me if untrusted ticket content could be mistaken for instructions, flag any policy gaps, and give me a risk score.”
Pre-deployment security check for coding agent
“Before I deploy my coding agent that reads GitHub issues and can run shell commands, can you analyze it for prompt injection risks and tell me which tools are high-impact and what mitigations I should put in place?”
Ongoing regression testing for AI workflow
“I've updated my agent workflow and want to make sure I haven't introduced new vulnerabilities — run a red-team analysis and generate regression tests so I can catch prompt injection issues in CI.”
Technical details
Endpoint
POSThttps://api.iamjuliand.com/v1/agent-workflow/red-teamWhen to prefer
Choose this endpoint when you need automated, LLM-powered red-team analysis of an AI agent workflow specifically for prompt injection and instruction-confusion vulnerabilities. It is purpose-built for agentic pipelines with tool use and untrusted content ingestion, returning structured risk scores and actionable mitigations — prefer it over generic security scanners or manual review when you want a fast, quantitative security assessment of an AI agent.
Side effects
- Workflow definition is malformed or missing required fields — returns error with missing field details
- Ambiguous or underspecified policy rules lead to incomplete static analysis — findings array may be empty or misleading
- Non-deterministic LLM behavior means repeated calls may yield different risk scores or findings
- High complexity workflows may hit token limits, truncating analysis
- External provider (OpenRouter) unavailability causes request failure
Output
Schema · Response
{
"type": "json",
"example": {
"model": "minimax/minimax-m3",
"usage": {
"costUsd": 0.00737,
"promptTokens": 1850,
"completionTokens": 920
},
"attacks": [],
"summary": "The workflow can confuse untrusted ticket content with instructions.",
"findings": [],
"riskScore": 72,
"mitigations": [],
"dataHandling": {
"toolsExecuted": false,
"secretsRedacted": 0,
"externalProvider": "OpenRouter"
},
"deterministic": false,
"productVersion": "1.0.0",
"staticAnalysis": {
"policyGaps": [
"No explicit rule separates untrusted content from instructions."
],
"approvalGaps": [],
"highImpactTools": [
"send_email"
]
},
"regressionTests": []
}
}Tags
x402
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