nanobot/SECURITY_AUDIT.md

Security Audit Report - nanobot

Date: 2026-02-03
Auditor: GitHub Copilot Security Agent
Repository: kingassune/nanobot

Executive Summary

This security audit identified CRITICAL vulnerabilities in the nanobot AI assistant framework. The most severe issues are:

1. CRITICAL: Outdated litellm dependency with 10 known vulnerabilities including RCE, SSRF, and API key leakage
2. MEDIUM: Outdated ws (WebSocket) dependency with DoS vulnerability
3. MEDIUM: Shell command execution without sufficient input validation
4. LOW: File system operations without path traversal protection

Detailed Findings

1. CRITICAL: Vulnerable litellm Dependency

Severity: CRITICAL
Location: pyproject.toml line 21
Current Version: >=1.0.0
Status: REQUIRES IMMEDIATE ACTION

Vulnerabilities Identified:

1. Remote Code Execution via eval() (CVE-2024-XXXX)

   • Affected: <= 1.28.11 and < 1.40.16
   • Impact: Arbitrary code execution
   • Patched: 1.40.16 (partial)

2. Server-Side Request Forgery (SSRF)

   • Affected: < 1.44.8
   • Impact: Internal network access, data exfiltration
   • Patched: 1.44.8

3. API Key Leakage via Logging

   • Affected: < 1.44.12 and <= 1.52.1
   • Impact: Credential exposure in logs
   • Patched: 1.44.12 (partial), no patch for <=1.52.1

4. Improper Authorization

   • Affected: < 1.61.15
   • Impact: Unauthorized access
   • Patched: 1.61.15

5. Denial of Service (DoS)

   • Affected: < 1.53.1.dev1 and < 1.56.2
   • Impact: Service disruption
   • Patched: 1.56.2

6. Arbitrary File Deletion

   • Affected: < 1.35.36
   • Impact: Data loss
   • Patched: 1.35.36

7. Server-Side Template Injection (SSTI)

   • Affected: < 1.34.42
   • Impact: Remote code execution
   • Patched: 1.34.42

Recommendation: Update to litellm>=1.61.15 immediately. Note that one vulnerability (API key leakage <=1.52.1) has no available patch - monitor for updates.

2. MEDIUM: Vulnerable ws (WebSocket) Dependency

Severity: MEDIUM
Location: bridge/package.json line 14
Current Version: ^8.17.0
Patched Version: 8.17.1

Vulnerability:

• DoS via HTTP Header Flooding
• Affected: >= 8.0.0, < 8.17.1
• Impact: Service disruption through crafted requests with excessive HTTP headers

Recommendation: Update to ws>=8.17.1

3. MEDIUM: Shell Command Execution Without Sufficient Validation

Severity: MEDIUM
Location: nanobot/agent/tools/shell.py lines 46-51

Issue:

The ExecTool class uses asyncio.create_subprocess_shell() to execute arbitrary shell commands without input validation or sanitization. While there is a timeout mechanism, there's no protection against:

• Command injection via special characters
• Execution of dangerous commands (e.g., rm -rf /)
• Resource exhaustion attacks

process = await asyncio.create_subprocess_shell(
    command,  # User-controlled input passed directly to shell
    stdout=asyncio.subprocess.PIPE,
    stderr=asyncio.subprocess.PIPE,
    cwd=cwd,
)

Current Mitigations:

• ✅ Timeout (60 seconds default)
• ✅ Output truncation (10,000 chars)
• ❌ No input validation
• ❌ No command whitelist
• ❌ No user confirmation for dangerous commands

Recommendation:

1. Implement command validation/sanitization
2. Consider using create_subprocess_exec() instead for safer execution
3. Add a whitelist of allowed commands or patterns
4. Require explicit user confirmation for destructive operations

4. LOW: File System Operations Without Path Traversal Protection

Severity: LOW
Location: nanobot/agent/tools/filesystem.py

Issue:

File operations use Path.expanduser() but don't validate against path traversal attacks. While expanduser() is used, there's no check to prevent operations outside intended directories.

Potential Attack Vectors:

read_file(path="../../../../etc/passwd")
write_file(path="/tmp/../../../etc/malicious")

Current Mitigations:

• ✅ Permission error handling
• ✅ File existence checks
• ❌ No path traversal prevention
• ❌ No directory whitelist

Recommendation:

1. Implement path validation to ensure operations stay within allowed directories
2. Use Path.resolve() to normalize paths before operations
3. Check that resolved paths start with allowed base directories

5. LOW: Authentication Based Only on allowFrom List

Severity: LOW
Location: nanobot/channels/base.py lines 59-82

Issue:

Access control relies solely on a simple allow_from list without:

• Rate limiting
• Authentication tokens
• Session management
• Account lockout after failed attempts

Current Implementation:

def is_allowed(self, sender_id: str) -> bool:
    allow_list = getattr(self.config, "allow_from", [])
    
    # If no allow list, allow everyone
    if not allow_list:
        return True

Concerns:

1. Empty allow_from list allows ALL users (fail-open design)
2. No rate limiting per user
3. User IDs can be spoofed in some contexts
4. No logging of denied access attempts

Recommendation:

1. Change default to fail-closed (deny all if no allow list)
2. Add rate limiting per sender_id
3. Log all authentication attempts
4. Consider adding token-based authentication

Additional Security Concerns

6. Information Disclosure in Error Messages

Severity: LOW
Multiple tools return detailed error messages that could leak sensitive information:

return f"Error reading file: {str(e)}"
return f"Error executing command: {str(e)}"

Recommendation: Sanitize error messages before returning to users.

7. API Key Storage in Plain Text

Severity: MEDIUM
Location: ~/.nanobot/config.json

API keys are stored in plain text in the configuration file. While file permissions provide some protection, this is not ideal for sensitive credentials.

Recommendation:

1. Use OS keyring/credential manager when possible
2. Encrypt configuration file at rest
3. Document proper file permissions (0600)

8. No Input Length Validation

Severity: LOW
Most tools don't validate input lengths before processing, which could lead to resource exhaustion.

Recommendation: Add reasonable length limits on all user inputs.

Compliance & Best Practices

✅ Good Security Practices Observed:

1. Timeout mechanisms on shell commands and HTTP requests
2. Output truncation prevents memory exhaustion
3. Permission error handling in file operations
4. TLS/SSL for external API calls (httpx with https)
5. Structured logging with loguru

❌ Missing Security Controls:

1. No rate limiting
2. No input validation/sanitization
3. No content security policy
4. No dependency vulnerability scanning in CI/CD
5. No security headers in responses
6. No audit logging of sensitive operations

Recommendations Summary

Immediate Actions (Critical Priority):

1. ✅ Update litellm to >=1.61.15
2. ✅ Update ws to >=8.17.1
3. Add input validation to shell command execution
4. Implement path traversal protection in file operations

Short-term Actions (High Priority):

1. Add rate limiting to prevent abuse
2. Change authentication default to fail-closed
3. Implement command whitelisting for shell execution
4. Add audit logging for security-sensitive operations
5. Sanitize error messages

Long-term Actions (Medium Priority):

1. Implement secure credential storage (keyring)
2. Add comprehensive input validation framework
3. Set up automated dependency vulnerability scanning
4. Implement security testing in CI/CD pipeline
5. Add Content Security Policy headers

Testing Recommendations

1. Dependency Scanning: Run pip-audit or safety regularly
2. Static Analysis: Use bandit for Python security analysis
3. Dynamic Testing: Implement security-focused integration tests
4. Penetration Testing: Consider professional security assessment
5. Fuzzing: Test input validation with fuzzing tools

Conclusion

The nanobot framework requires immediate security updates, particularly for the litellm dependency which has critical vulnerabilities including remote code execution. After updating dependencies, focus should shift to improving input validation and implementing proper access controls.

Risk Level: HIGH (before patches applied)
Recommended Action: Apply critical dependency updates immediately

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This audit was performed using automated tools and manual code review. A comprehensive penetration test is recommended for production deployments.