🚨 The Threat Landscape

Why This Matters for Symphony

• Community-developed extensions run with significant privileges
• AI extensions have access to APIs, data, and system resources
• Users trust extensions with their code and sensitive information
• Single malicious extension could compromise entire workflows

Attack Vectors Specific to Symphony

🎯 HIGH-RISK SCENARIOS:

1. **Credential Harvesting**
   - AI extension requests API keys "for better performance"
   - Silently copies keys to remote server
   - User doesn't realize until bills/quotas exceeded

2. **Code Exfiltration**
   - Extension claims to "optimize" user's code
   - Actually sends proprietary code to competitor
   - Looks like normal processing activity

3. **Resource Hijacking**
   - Extension uses AI API quotas for own purposes
   - Mines crypto using user's compute resources
   - Depletes user's paid AI service credits

4. **Workflow Sabotage**
   - Extension subtly corrupts output files
   - Introduces hard-to-detect bugs in generated code
   - Damages user productivity over time

5. **Supply Chain Attack**
   - Popular extension gets compromised in update
   - Thousands of Symphony users affected simultaneously
   - Difficult to detect until widespread damage

🛡️ Multi-Layer Security Strategy

Layer 1: Sandboxing & Isolation

Process Isolation

[security.process_isolation]
each_extension_separate_process = true
no_shared_memory = true
limited_system_calls = true
restricted_file_system_access = true

[security.network_isolation]
extension_network_allowlist = ["declared_apis_only"]
block_unexpected_domains = true
monitor_data_egress = true
encrypt_inter_extension_communication = true

Resource Containerization

# Each extension runs in isolated container
Extension Container:
├── CPU: Limited to declared amount
├── Memory: Hard limit, no swap
├── Network: Only approved endpoints
├── File System: Read-only + temp directory
└── API Access: Only declared endpoints

Layer 2: Runtime Behavior Monitoring

Behavioral Analysis Engine

class ExtensionBehaviorMonitor:
    def __init__(self):
        self.baseline_behavior = {}
        self.anomaly_threshold = 0.7

    def monitor_extension(self, extension_id):
        behavior_metrics = {
            "cpu_usage_pattern": self.track_cpu_usage(),
            "network_requests": self.track_network_calls(),
            "file_operations": self.track_file_access(),
            "api_call_patterns": self.track_api_usage(),
            "memory_allocation": self.track_memory_patterns(),
            "execution_time_variance": self.track_timing()
        }

        anomaly_score = self.calculate_anomaly_score(behavior_metrics)

        if anomaly_score > self.anomaly_threshold:
            self.trigger_security_alert(extension_id, behavior_metrics)

Red Flags for Immediate Quarantine

[security.red_flags]
unexpected_network_destinations = "IMMEDIATE_QUARANTINE"
excessive_resource_usage = "WARN_THEN_QUARANTINE"
file_system_escape_attempts = "IMMEDIATE_QUARANTINE"
credential_harvesting_patterns = "IMMEDIATE_QUARANTINE"
cryptocurrency_mining_signatures = "IMMEDIATE_QUARANTINE"

[security.monitoring_metrics]
network_egress_rate = "bytes_per_minute"
cpu_usage_vs_declared = "percentage_over_manifest"
memory_growth_rate = "mb_per_operation"
api_call_frequency = "calls_per_minute"