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Security Policy

Supported Versions

We take security seriously and actively maintain the following versions:

Version Supported
1.1.x ✅
1.0.x ✅
< 1.0 ❌

Reporting a Vulnerability

We appreciate your efforts to responsibly disclose security vulnerabilities. Please follow these guidelines:

🔒 Private Disclosure (Recommended)

For serious security vulnerabilities, please use one of these private channels:

  1. GitHub Security Advisories (preferred)

    • Go to Security Advisories
    • Click "Report a vulnerability"
    • Provide detailed information about the vulnerability

  2. Direct Contact

    • Create a draft security advisory on GitHub
    • We will respond within 48 hours

📢 Public Disclosure

For low-severity issues or security improvements, you may:

What to Include in Your Report

Please provide the following information:

  1. Vulnerability Description: Clear explanation of the issue
  2. Affected Versions: Which versions are vulnerable
  3. Impact Assessment: What could an attacker do with this vulnerability?
  4. Reproduction Steps: Detailed steps to reproduce the issue
  5. Proof of Concept: Code or commands demonstrating the vulnerability (if applicable)
  6. Suggested Fix: If you have ideas for remediation (optional)
  7. CVE Information: If a CVE has been assigned (optional)

Our Commitment

When you report a vulnerability, we commit to:

  • Acknowledge your report within 48 hours
  • Provide an initial assessment within 5 business days
  • Keep you informed about our progress toward a fix
  • Credit you in the security advisory (if desired)
  • Follow coordinated disclosure practices

Security Best Practices for Users

When using Agent Control Plane in production:

1. Access Control

# Always use strict permission policies
control_plane = ControlPlane(
    agent_id="production-agent",
    allowed_tools=["search", "read_only"],  # Minimal permissions
    denied_tools=["execute_code", "file_write"],  # Explicitly deny dangerous tools
)

2. Rate Limiting

# Configure appropriate rate limits
control_plane.policy_engine.configure_rate_limit(
    max_requests=100,
    time_window=60  # per minute
)

3. Audit Logging

# Enable comprehensive audit logs
control_plane.enable_audit_logging()

# Regularly review audit logs
audit_trail = control_plane.get_audit_trail()

4. Constraint Graphs

# Use constraint graphs to enforce safety boundaries
from agent_control_plane import DataConstraint, PolicyConstraint

control_plane.add_constraint(
    DataConstraint(allowed_data_types=["public"])
)
control_plane.add_constraint(
    PolicyConstraint(require_approval=True)
)

5. Shadow Mode Testing

# Test agent behavior in shadow mode first
control_plane.enable_shadow_mode()
# Validate behavior before enabling real execution
control_plane.disable_shadow_mode()

6. Supervisor Agents

# Use supervisor agents for high-risk operations
from agent_control_plane import SupervisorAgent

supervisor = SupervisorAgent(
    agent_id="supervisor",
    supervised_agents=["agent-1", "agent-2"],
    anomaly_detection=True
)

7. Regular Updates

  • Keep Agent Control Plane updated to the latest version
  • Subscribe to security advisories via GitHub Watch
  • Review CHANGELOG.md for security-related updates

8. Environment Isolation

  • Run agents in sandboxed environments (Docker containers, VMs)
  • Use network segmentation
  • Apply principle of least privilege

9. Input Validation

# Always validate and sanitize inputs
from agent_control_plane import JailbreakDetector

detector = JailbreakDetector()
if detector.is_jailbreak_attempt(user_input):
    # Reject the request
    raise SecurityError("Potential jailbreak detected")

10. Compliance Frameworks

# Enable relevant compliance frameworks
from agent_control_plane import ComplianceEngine

compliance = ComplianceEngine()
compliance.enable_framework("eu_ai_act")
compliance.enable_framework("soc2")

Known Security Considerations

1. LLM-Specific Risks

  • Prompt Injection: Use JailbreakDetector and input validation
  • Data Exfiltration: Enforce data constraints and output monitoring
  • Model Manipulation: Use constitutional AI principles

2. Multi-Agent Risks

  • Privilege Escalation: Supervisor agents monitor for anomalies
  • Agent Collusion: Isolation boundaries enforced by kernel
  • Resource Exhaustion: Rate limiting and quotas enforced

3. Integration Risks

  • Third-Party APIs: Validate all external inputs/outputs
  • Dependency Chain: Regular security audits of dependencies
  • Configuration Errors: Use provided secure defaults

Security Features

Agent Control Plane provides multiple layers of security:

  1. Deterministic Enforcement: Policy violations blocked at kernel level
  2. Jailbreak Detection: 60+ attack pattern recognition
  3. Anomaly Detection: Behavioral analysis of agent actions
  4. Audit Trail: Complete SQLite-based action logging
  5. Compliance Engine: EU AI Act, SOC 2, GDPR support
  6. Constitutional AI: Value alignment framework
  7. Multimodal Safety: Content moderation for vision/audio
  8. Zero-Trust Architecture: Default deny, explicit allow

Security Research and Bug Bounty

We currently do not have a formal bug bounty program, but we:

  • Welcome security research on the project
  • Credit security researchers in advisories (with permission)
  • Consider significant contributions for acknowledgment
  • Follow responsible disclosure practices

CVE Process

For vulnerabilities assigned CVEs:

  1. We will work with you on coordinated disclosure
  2. A security advisory will be published on GitHub
  3. Affected versions will be documented
  4. Fixed versions will be released promptly
  5. Users will be notified through multiple channels

Questions?

For general security questions (non-vulnerability):

Thank you for helping keep Agent Control Plane secure!