The Indie Founder AI Agent Red Team Playbook
Ship AI agents that survive real adversarial attacks before users find them
Built for indie founders shipping AI-powered products who skip security testing because they assume it needs a big team. This playbook gives you an adversarial testing pipeline, a semantic monitoring layer, and production hardening — all runnable solo before your first real user touches the product.
Goal
Identify and patch adversarial vulnerabilities in AI agents before shipping to production users
Who this is for
Indie founders and solopreneurs building AI-powered SaaS products or autonomous agents who handle user data or decisions
When to use
When you're about to launch an AI agent or LLM-powered feature and haven't done any adversarial or safety testing yet
When NOT to use
When your AI product has no user-facing inputs, no data handling, or is a purely internal tool with no security surface
How to set it up
Run your first red team session
Point Agent Red Team at your AI agent's endpoint or prompt chain. Run the standard adversarial suite covering prompt injection, data exfiltration, and jailbreak scenarios. Export the findings report.
Install the LLM firewall
Add Senthex's one-line firewall wrapper to your LLM API calls in your agent codebase. Verify it intercepts a test injection payload without adding noticeable latency.
Enable semantic monitoring
Configure Imladri to monitor your agent's output stream. Set semantic guardrails based on the failure categories your red team report surfaced in step 1.
Scan for codebase drift
Run VibeDrift across your agent codebase to identify any architectural gaps or security regressions introduced by AI-generated code changes since your last review.
Set up production root cause analysis
Connect Kelet to your production LLM app. Trigger a simulated failure from your red team report and confirm Kelet traces and surfaces the root cause correctly.
Test AI agents for adversarial attacks before production
The core safety engine — runs structured adversarial scenarios against your agent so you find prompt injection, jailbreaks, and edge cases before users do.
Enforce cryptographic safety and monitor AI behavior
Watches your agent's outputs semantically and enforces behavioural constraints cryptographically so runtime violations are caught and blocked automatically.
Protect LLM API calls with sub-16ms security layer
Wraps every LLM API call with a sub-16ms security filter — one line of code that stops injection and data exfiltration at the network layer.
AI diagnoses and fixes failures in LLM apps and agents in production
When red team tests or monitoring flags a failure, Kelet automatically traces the root cause through your LLM app stack so you fix the right thing fast.
Scan AI-generated codebases for architectural drift and security gaps
Scans your AI-generated agent code for architectural drift and security gaps that accumulate silently between red team sessions.
Expected outcome
A documented adversarial test report, a live semantic monitoring layer, and a hardened LLM API firewall protecting your production AI agent
Related playbooks
The Indie Founder Agent Identity & Security Playbook
Launch AI agents that are secure, identifiable, and resistant to prompt injection or credential leaks
The Indie Founder Agentic QA Hardening Playbook
Ship AI-generated code to production with confidence and zero critical regressions
The Solo Founder AI Agent Cost Forecasting Playbook
Forecast, monitor, and control AI agent infrastructure costs before they exceed revenue
The Indie Founder EU AI Act Readiness Sprint Playbook
Produce verifiable EU AI Act compliance documentation and adversarial test evidence from your existing codebase
Was this playbook useful?
This playbook is a curated starting point, not a definitive recommendation. Pricing and features change — always verify on each tool's official website. Tools marked "affiliate link" may earn this site a commission at no extra cost to you.