README.md

metadata

title: DNS-Env
emoji: 🌐
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
tags:
  - openenv
  - reinforcement-learning
  - dns
  - infrastructure
  - devops
pinned: false

DNS-Env: DNS Zone File Debugging Environment

Overview

DNS-Env is a reinforcement learning environment where AI agents learn to debug and configure DNS zone files. Built for the Meta PyTorch OpenEnv Hackathon x Scaler School of Technology, it provides a realistic simulation of DNS administration tasks ranging from fixing broken records to configuring complex multi-zone delegations.

Motivation

• "It's always DNS" -- DNS misconfiguration is the #1 cause of internet outages. Yet there is no structured RL environment for training agents to diagnose and repair DNS issues.
• 80% of domains have misconfigured DMARC records -- Email authentication (SPF, DKIM, DMARC) is critical for deliverability and security, but remains error-prone even for experienced engineers.
• No existing RL environment or agent benchmark covers DNS debugging -- DNS-Env fills this gap with grounded, deterministic tasks that mirror real-world operational scenarios.

Environment Description

The environment simulates DNS zone file editing and validation. An agent is presented with one or more zone files containing deliberate misconfigurations. The agent must use the available commands to inspect, diagnose, and repair the zone files, then submit for grading.

Action Space

Command	Arguments	Description
view_zone	zone (optional)	View current zone file with indexed records
add_record	zone, name, rtype, rdata, ttl (optional)	Add a new DNS record
edit_record	zone, index, name/rtype/rdata/ttl (partial)	Edit record at index
delete_record	zone, index	Delete record at index (cannot delete SOA)
check_zone	zone (optional)	Validate zone file for errors
dig	qname, qtype, zone (optional)	Simulate DNS resolution
submit	(none)	Submit for grading

Observation Space

Each step returns an observation containing:

• output -- Command result text (zone file contents, validation errors, dig results, etc.)
• task_description -- Current task description and objectives
• zone_names -- List of available zone files
• available_commands -- Available actions the agent can take
• done -- Whether the episode is complete
• reward -- Score (0.0--1.0) returned on submission

Tasks

Task 1: fix_single_record (Easy)

Fix broken records in an example.com zone file. Bugs include missing trailing dots on FQDNs, invalid IP addresses, and malformed MX records.

• Expected difficulty: Easy
• Max steps: 15

Task 2: configure_mail (Medium)

Configure complete email delivery for acme.co: MX records, SPF, DKIM, and DMARC from a natural language specification.

• Expected difficulty: Medium
• Max steps: 25

Task 3: debug_delegation (Hard)

Debug a broken DNS delegation between parent.org and dev.parent.org across two zone files. Fix NS records, glue records, and SOA serial numbers.

• Expected difficulty: Hard
• Max steps: 30

Reward Function

score = 0.3 * (structural_validity) + 0.5 * (resolution_correctness) + 0.2 * (no_regressions)

Component	Weight	Description
Structural validity	0.3	Zone file has no validation errors
Resolution correctness	0.5	Required DNS queries resolve correctly
No regressions	0.2	Pre-existing correct records are preserved

Setup & Usage

Local Development

pip install -r requirements.txt
uvicorn server.app:app --host 0.0.0.0 --port 7860 --reload

Docker

docker build -t dns-env .
docker run -p 7860:7860 dns-env

API Usage

import requests

BASE = "http://localhost:7860"

# Reset with a specific task
obs = requests.post(f"{BASE}/reset", json={"options": {"task_id": "fix_single_record"}}).json()

# View the zone file
obs = requests.post(f"{BASE}/step", json={"action": {"command": "view_zone", "args": {}}}).json()
print(obs["output"])

# Edit a record
obs = requests.post(f"{BASE}/step", json={
    "action": {"command": "edit_record", "args": {"index": 4, "rdata": "example.com."}}
}).json()

# Submit for grading
obs = requests.post(f"{BASE}/step", json={"action": {"command": "submit", "args": {}}}).json()
print(f"Score: {obs['reward']}")

Baseline Scores

Task	Score	Steps
fix_single_record	~0.85	5--8
configure_mail	~0.50	10--15
debug_delegation	~0.20	15--25

Running the Baseline

export API_BASE_URL="https://api.openai.com/v1"
export MODEL_NAME="gpt-4o-mini"
export OPENAI_API_KEY="your-key"
export ENV_URL="http://localhost:7860"
python inference.py

Technical Details

• Built with FastAPI + Pydantic for typed request/response models
• Pure Python DNS validation -- no external DNS tools (bind-utils, dig) required
• Deterministic grading via zone file parsing and record matching
• Supports concurrent sessions via session_id parameter
• Runs on 2 vCPU / 8 GB RAM within 20 minutes

OpenEnv Spec Compliance

• step(action) returns observation, reward, done
• reset() returns initial observation
• state() returns episode metadata
• openenv.yaml with spec_version 1
• Typed Pydantic models for all request/response schemas
• Containerized with Docker
• Deployed to HuggingFace Spaces