This document outlines the security considerations, threat model, and safety mechanisms for AutoOps Architect.

AutoOps Architect is a meta-agent system that executes workflows in response to operational goals. Because it can interact with production systems, execute commands, and potentially make changes, security is a critical concern.

Risk Level: HIGH

Description: The LLM planner may generate workflows containing dangerous operations such as:

• Service restarts in production
• Database modifications
• Configuration changes
• Rollbacks without proper validation

Mitigations:

• All remediation actions are disabled by default (enable_remediation=False)
• High-risk node types require explicit human approval
• Workflows are validated before execution
• LLM prompts include explicit safety constraints

Configuration:

from autoops_architect.planner import PlannerConfig

config = PlannerConfig(
    enable_remediation=False,  # Disable dangerous actions
    default_constraints=[
        "Prioritize investigation before remediation",
        "All remediation actions require human approval",
    ]
)

Risk Level: HIGH

Description: Tools may execute shell commands, potentially allowing:

• Command injection attacks
• Privilege escalation
• Data exfiltration
• System compromise

Mitigations:

• Custom script tools run with explicit approval
• Shell commands are not directly exposed in built-in tools
• Tool registry controls which tools are available
• Input parameters are validated and sanitized

Configuration:

from autoops_architect.safety import SafetyConfig

safety = SafetyConfig(
    allowed_tools=["log_collector", "metric_query", "summary"],  # Whitelist
    blocked_tools=["custom_script"],  # Blacklist
)

Risk Level: HIGH

Description: Credentials may be exposed through:

• Logging of parameters or outputs
• Memory storage without encryption
• Transmission to LLM providers
• Tool parameters in workflow JSON

Mitigations:

• Credentials should be stored in environment variables
• Sensitive fields are redacted in logs
• Memory backend supports encryption option
• Workflow JSON should not contain actual secrets

Best Practices:

# DO: Use environment variables
os.environ["DATADOG_API_KEY"] = "..."

# DON'T: Put secrets in workflow params
workflow_params = {
    "api_key": "SECRET_VALUE"  # BAD!
}

Risk Level: MEDIUM

Description: Attackers or misconfigured workflows may cause:

• Resource exhaustion (memory, CPU)
• Excessive API calls
• Long-running operations blocking the system
• Network saturation

Mitigations:

• Per-node timeout limits
• Global workflow timeout
• Rate limiting for LLM and external API calls
• Concurrency limits for parallel execution

Configuration:

from autoops_architect.executor import ExecutorConfig

config = ExecutorConfig(
    default_timeout=60,      # Per-node timeout (seconds)
    workflow_timeout=600,    # Total workflow timeout
    max_concurrency=5,       # Parallel execution limit
    rate_limit_llm=10,       # LLM calls per minute
)

Risk Level: MEDIUM

Description: Malicious input in goal descriptions may:

• Manipulate LLM to generate dangerous workflows
• Override safety constraints
• Inject unwanted instructions

Mitigations:

• User input is sanitized before inclusion in prompts
• System prompts include explicit safety boundaries
• Generated workflows are validated
• Approval requirements cannot be bypassed

Input Sanitization:

from autoops_architect.safety import sanitize_goal

safe_goal = sanitize_goal(user_input)
# Strips dangerous patterns, validates length, escapes special chars

Risk Level: MEDIUM

Description: Attackers with file system access may:

• Modify stored workflows
• Inject malicious memory entries
• Corrupt the memory database
• Access sensitive historical data

Mitigations:

• Memory files have restricted permissions
• SQLite backend supports encryption
• Memory entries are validated on load
• File paths are validated to prevent traversal

Risk Level: MEDIUM

Description: Malicious tools may be registered:

• Through dynamic imports
• Via configuration files
• By modifying the registry at runtime

Mitigations:

• Tool registration is explicit
• Tool classes are validated
• Dynamic tool loading requires approval
• Registry changes are logged

Risk Level: LOW-MEDIUM

Description: When running the web UI:

• API endpoints may be exposed
• SSE streams may leak information
• CORS misconfiguration
• Authentication bypass

Mitigations:

• Web UI binds to localhost by default
• CORS is restricted by default
• API endpoints validate input
• SSE streams are per-session

Certain operations require explicit human approval:

Before execution, workflows are validated for:

• Maximum node count limits
• Required approval flags on dangerous nodes
• Valid tool references
• Proper edge connections (no orphans, no cycles)
• Parameter schema compliance

Configure which tools are available:

from autoops_architect.tools import create_default_registry
from autoops_architect.safety import apply_whitelist

registry = create_default_registry()
apply_whitelist(registry, ["log_collector", "metric_query", "summary"])
# Only these tools will be available for workflow execution

Test workflows without executing actual actions:

autoops run workflow.yaml --dry-run

from autoops_architect.executor import WorkflowExecutor, ExecutorConfig

executor = WorkflowExecutor(
    config=ExecutorConfig(dry_run=True)
)

All significant events are logged:

• Workflow creation and execution
• Node execution start/completion
• Tool invocations
• Approval requests and responses
• Errors and failures

import logging

logging.getLogger("autoops_architect").setLevel(logging.INFO)
# Logs include timestamps, user context, and operation details

Create a safety configuration file at ~/.autoops/safety.yaml:

# Safety configuration for AutoOps Architect
version: "1.0"

# Execution controls
execution:
  enable_remediation: false
  max_nodes_per_workflow: 20
  default_timeout_seconds: 60
  workflow_timeout_seconds: 600
  max_concurrency: 5

# Tool controls
tools:
  allowed:
    - log_collector
    - metric_query
    - trace_collector
    - analysis
    - summary
  blocked:
    - custom_script
    - service_restart
  require_approval:
    - autoRCA
    - browserMission

# Node type controls
node_types:
  allowed:
    - log_collection
    - metric_query
    - trace_collection
    - analysis
    - summary
    - rca_call
  blocked:
    - service_restart
    - config_update
    - rollback
    - scale_action
    - custom_script

# Network controls
network:
  allowed_hosts:
    - "*.internal.company.com"
    - "api.datadog.com"
    - "api.openai.com"
  blocked_hosts:
    - "*.external-untrusted.com"

# Approval settings
approval:
  timeout_seconds: 300
  require_for_production: true
  notify_channel: "slack:#ops-approvals"

1. Always review generated workflows before execution
2. Use dry-run mode for new or complex workflows
3. Start with read-only tools (log_collector, metric_query)
4. Enable remediation gradually as you build trust
5. Monitor execution logs for unexpected behavior
6. Keep tools and integrations updated

1. Validate all inputs in custom tools
2. Never execute shell commands with user-provided data
3. Use parameterized queries for database operations
4. Log security-relevant events appropriately
5. Follow the principle of least privilege
6. Review tool implementations for security issues

1. Run behind a reverse proxy with authentication
2. Use HTTPS for all network communication
3. Enable audit logging to a secure destination
4. Implement role-based access control
5. Integrate with your SIEM for security monitoring
6. Regular security assessments of configurations

If you discover a security vulnerability:

1. Do not disclose publicly until fixed
2. Report via email to security@example.com (replace with actual email)
3. Include details: steps to reproduce, impact assessment
4. We will respond within 48 hours
5. Coordinated disclosure after fix is available

Security updates are released as:

• Critical: Immediate patch release
• High: Within 7 days
• Medium: In next scheduled release
• Low: Tracked and prioritized

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