---
title: Code Security Auditor Environment
emoji: "🛡️"
colorFrom: yellow
colorTo: red
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
  - openenv
  - security
  - code-review
  - reinforcement-learning
---

# Code Security Auditor Environment

A real-world OpenEnv benchmark where agents perform security auditing on pull-request style code snapshots.

The agent inspects files, submits vulnerability findings, and finalizes a report. The environment scores by deterministic graders over true vulnerability ground truth with partial credit and anti-reward-hacking penalties.

## Why this is a real-world task

Security reviewers and AppSec engineers routinely audit code for vulnerabilities before deployment. This environment models that workflow with concrete exploit classes:

- SQL injection
- command injection
- insecure deserialization
- weak authentication / auth bypass
- SSRF
- path traversal
- hardcoded secrets

## OpenEnv Compliance

- Typed models: CodeSecurityAction, CodeSecurityObservation, CodeSecurityState
- Core API: reset(), step(), state()
- OpenEnv manifest: openenv.yaml
- FastAPI runtime via server.app:app

## Action Space

Action model: CodeSecurityAction

- action_type: inspect_file | submit_finding | submit_final_report
- filename: target file to inspect or report against
- line_start, line_end: suspected vulnerable range
- vuln_type: one of supported vulnerability classes
- severity: low | medium | high | critical
- confidence: [0.0, 1.0]
- evidence, summary: free-form context

### Action semantics

- inspect_file: returns full line-numbered file content.
- submit_finding: grades the finding with deterministic partial credit.
- submit_final_report: ends the episode and returns final score in [0.0, 1.0].

## Observation Space

Observation model: CodeSecurityObservation

Key fields:

- task_id, task_title, difficulty, objective
- available_files
- focused_file, file_excerpt
- findings_so_far
- steps_remaining
- last_feedback
- score_hint in [0, 1]
- reward, done, metadata

## Tasks and Difficulty

The environment includes 3 deterministic tasks:

1. easy: Legacy Flask Patch Review
2. medium: Payment Webhook Service
3. hard: Enterprise Multi-Tenant API

Each task has:

- realistic multi-file code snapshot
- hidden vulnerability ground truth
- deterministic grader with score in [0.0, 1.0]

## Reward Design

Reward shaping is trajectory-aware and resistant to reward hacking:

- inspect_file gives small positive signal for novel, relevant file exploration
- submit_finding gives partial credit ladder (file -> type -> line -> severity -> confidence calibration)
- duplicate/low-quality findings reduce quality_multiplier and final score
- false positives and over-submission reduce precision and final score
- final score combines weighted recall, precision, structural quality, and calibration

This creates control and symmetry: spamming findings can increase step count but lowers precision and quality, preventing easy reward exploitation.

## Baseline Scores

With deterministic tasks and a simple tool-using model loop, expected baseline tendencies are:

- easy: high recall, moderate precision
- medium: moderate recall, moderate precision
- hard: lower recall, stricter penalties for noisy findings

Run inference.py to generate reproducible per-task scores for your selected model setup.

## Setup

### Option A: Run in-repo (OpenEnv monorepo)

From repository root:

```bash
docker build -t code-security-auditor-env:latest -f envs/code_security_auditor_env/server/Dockerfile .
docker run -p 8000:8000 code-security-auditor-env:latest
```

### Option B: Run standalone

From this directory:

```bash
docker build -t code-security-auditor-env:latest .
docker run -p 8000:8000 code-security-auditor-env:latest
```

## Baseline Inference

The required script is inference.py in project root (this directory).

Required env vars:

- API_BASE_URL
- MODEL_NAME
- HF_TOKEN

Optional env vars:

- LOCAL_IMAGE_NAME (for from_docker_image mode)
- ENV_BASE_URL (for connecting to an already-running server)
- TASK_IDS (comma-separated task ids, default: easy,medium,hard)
- MAX_STEPS

Run:

```bash
export HF_TOKEN=your_token
export API_BASE_URL=https://router.huggingface.co/v1
export MODEL_NAME=Qwen/Qwen2.5-72B-Instruct
export LOCAL_IMAGE_NAME=code-security-auditor-env:latest
python inference.py
```

The script prints only [START], [STEP], and [END] log lines per task.

## Hugging Face Spaces Deployment

Space repository:

- https://huggingface.co/spaces/Drac0528/CodeSecure

Recommended deploy flow (git push to Space repo):

```bash
git clone https://huggingface.co/spaces/Drac0528/CodeSecure
cd CodeSecure
cp -R /path/to/code_security_auditor_env/* .
rm -f .env
git add .
git commit -m "Deploy Code Security Auditor OpenEnv"
git push
```

Notes:

- Keep README frontmatter and Dockerfile at Space repo root.
- Use Space Settings to set runtime secrets/variables:
  - HF_TOKEN (Secret)
  - API_BASE_URL (Variable)
  - MODEL_NAME (Variable)
- Ensure Space tags include `openenv`.

Verify API endpoint after build:

```bash
curl -X POST https://drac0528-codesecure.hf.space/reset -H 'Content-Type: application/json' -d '{}'
```

## Validation

Use validate-submission.sh before submitting:

```bash
chmod +x validate-submission.sh
./validate-submission.sh https://drac0528-codesecure.hf.space .
```