# Safety, Ethics & Responsible Use

This document outlines the safety considerations, ethical guidelines, and responsible use practices for the Cross-Model Verification Kernel (CMVK).

## Table of Contents

1. [Overview](#overview)
2. [Sandbox Security](#sandbox-security)
3. [Prompt Injection Defenses](#prompt-injection-defenses)
4. [Dual-Use Considerations](#dual-use-considerations)
5. [Responsible Disclosure](#responsible-disclosure)
6. [Limitations](#limitations)

---

## Overview

CMVK executes LLM-generated code in a verification loop. This introduces inherent risks:

- **Code Execution Risk**: Generated code could be malicious
- **API Abuse**: Verification loops could be exploited for resource exhaustion
- **Prompt Injection**: Adversarial inputs could manipulate Generator or Verifier behavior
- **Dual-Use**: Better code generation could enable malicious applications

We take these risks seriously and implement multiple layers of defense.

---

## Sandbox Security

### Current Implementation

The `sandbox.py` module provides basic isolation for code execution:

```python
# Current safeguards
- Timeout limits (default: 30 seconds)
- Memory limits (configurable)
- Restricted imports (no os, subprocess, socket by default)
- Output size limits
```

### ⚠️ Known Limitations

**The current sandbox is NOT production-grade.** It provides basic protection for research use but should NOT be trusted for:

- Untrusted user inputs
- Production deployments
- High-security environments

### Recommended Production Hardening

For production use, implement additional isolation:

#### 1. Docker-based Isolation

```bash
# Run sandbox in isolated container with no network
docker run --rm \
    --network none \
    --memory 512m \
    --cpus 0.5 \
    --read-only \
    --security-opt no-new-privileges \
    --cap-drop ALL \
    cmvk:sandbox python3 -c "YOUR_CODE"
```

#### 2. gVisor/Firecracker

For stronger isolation, use gVisor or Firecracker:

```bash
# With gVisor
docker run --runtime=runsc --network none ...

# With Firecracker (requires setup)
firecracker-containerd ...
```

#### 3. Resource Limits

Always enforce:
| Resource | Recommended Limit |
|----------|------------------|
| CPU Time | 30 seconds |
| Memory | 512 MB |
| Disk | Read-only or 100 MB |
| Network | None (blocked) |
| Processes | 10 max |
| File Descriptors | 100 max |

#### 4. Seccomp Profiles

Restrict system calls:

```json
{
  "defaultAction": "SCMP_ACT_ERRNO",
  "syscalls": [
    {"names": ["read", "write", "exit", "brk", "mmap"], "action": "SCMP_ACT_ALLOW"}
  ]
}
```

---

## Prompt Injection Defenses

### Attack Vectors

1. **Generator Injection**: User task contains instructions to generate malicious code
2. **Verifier Bypass**: Malicious code tricks verifier into approving it
3. **Cross-Model Attacks**: Exploiting differences between Generator and Verifier models

### Current Mitigations

1. **System Prompt Isolation**: Generator and Verifier have separate, hardened system prompts
2. **Adversarial Design**: Verifier is explicitly instructed to be hostile and suspicious
3. **Runtime Testing**: All solutions must pass executable tests before acceptance
4. **Model Diversity**: Different model families reduce correlated vulnerabilities

### Recommended Additional Defenses

```python
# Input sanitization
def sanitize_task(task: str) -> str:
    """Remove potentially dangerous patterns from user input."""
    dangerous_patterns = [
        r"ignore previous instructions",
        r"disregard your system prompt",
        r"you are now",
        r"new instructions:",
    ]
    for pattern in dangerous_patterns:
        task = re.sub(pattern, "[FILTERED]", task, flags=re.IGNORECASE)
    return task
```

### Output Validation

```python
# Validate generated code before execution
def validate_code(code: str) -> bool:
    """Check for obviously dangerous patterns."""
    dangerous_imports = ["os", "subprocess", "socket", "shutil", "sys"]
    dangerous_calls = ["eval", "exec", "compile", "__import__", "open"]

    for imp in dangerous_imports:
        if f"import {imp}" in code or f"from {imp}" in code:
            return False

    for call in dangerous_calls:
        if f"{call}(" in code:
            return False

    return True
```

---

## Dual-Use Considerations

### The Capability Dilemma

CMVK improves code generation quality. This capability could be misused for:

- **Malware Development**: Better code = better malware
- **Exploit Generation**: Automated vulnerability exploitation
- **Spam/Fraud**: Generating phishing or scam content
- **Academic Dishonesty**: Automated assignment completion

### Our Position

We believe the benefits of transparent, verifiable AI code generation outweigh the risks:

1. **Defense > Offense**: The same verification techniques help detect malicious code
2. **Transparency**: Open research enables community oversight
3. **Incremental Risk**: CMVK doesn't create fundamentally new capabilities
4. **Responsible Disclosure**: We commit to responsible handling of vulnerabilities

### Usage Guidelines

**DO:**
- Use CMVK for legitimate software development
- Use for research and education
- Report vulnerabilities responsibly
- Credit the project in publications

**DON'T:**
- Generate malware or exploits
- Use for academic dishonesty
- Bypass content policies of underlying LLMs
- Deploy without proper security review

---

## Responsible Disclosure

### Reporting Security Issues

If you discover a security vulnerability:

1. **DO NOT** open a public GitHub issue
2. Email: [security@example.com] (replace with actual contact)
3. Include:
   - Description of the vulnerability
   - Steps to reproduce
   - Potential impact
   - Suggested fix (if any)

### Response Timeline

- **Acknowledgment**: Within 48 hours
- **Initial Assessment**: Within 7 days
- **Fix/Mitigation**: Within 30 days for critical issues
- **Disclosure**: Coordinated after fix is available

---

## Limitations

### What CMVK Cannot Guarantee

1. **Perfect Security**: No system is 100% secure
2. **Correct Code**: Verification reduces but doesn't eliminate bugs
3. **Safe Code**: Generated code could still have security vulnerabilities
4. **Ethical Use**: We cannot control how users apply the tool

### Known Weaknesses

| Weakness | Mitigation Status | Priority |
|----------|------------------|----------|
| Basic sandbox | Document limitations | High |
| No input sanitization | TODO | Medium |
| Token exhaustion attacks | Rate limiting recommended | Medium |
| Same-family model correlation | Use diverse models | Low |

### Research Disclaimers

This is a **research prototype**. It is:

- NOT audited for production security
- NOT intended for critical systems
- NOT a replacement for human code review
- NOT guaranteed to catch all bugs

---

## Compliance

### AI Safety Guidelines

CMVK development follows:

- [Anthropic's Responsible Scaling Policy](https://www.anthropic.com/index/anthropics-responsible-scaling-policy)
- [OpenAI Usage Policies](https://openai.com/policies/usage-policies)
- [Google AI Principles](https://ai.google/principles/)

### Academic Standards

For research use:

- Disclose LLM usage in publications (per NeurIPS/ICLR guidelines)
- Include hardware and runtime specifications for reproducibility
- Make datasets and code available for verification

---

## Contact

For safety-related questions:

- GitHub Issues (non-sensitive): [Link to issues]
- Security Issues: [security email]
- General Inquiries: [contact email]

---

*Last updated: January 2026*
*Version: 1.0*
