Security: Unsafe deserialization of LLM/Agent output

[cloakmaster]

Security Findings Report

We scanned this repository using Inkog, an AI security scanner, and identified 1 HIGH severity vulnerability related to unsafe deserialization.

Summary

• 1 HIGH severity unsafe deserialization of LLM/Agent output
• Governance Score: 83/100
• Issue: Human Oversight MISSING

Findings

Severity	Issue	Location	Notes
HIGH	Unsafe Deserialization of LLM/Agent Output	spans.py:15	Taint source: external_data

Details

The vulnerability involves deserializing data from an external source (LLM or agent output) without proper validation. This can lead to:

• Code execution vulnerabilities
• Data integrity issues
• Potential for malicious payload injection

How to Reproduce

You can verify these findings by running Inkog yourself:

npx -y @inkog-io/cli scan . -deep

Recommendations

1. Validate before deserializing: Implement strict schema validation before deserializing any external data.
2. Use safe deserialization methods: Consider using safer alternatives like JSON schema validation or type checking.
3. Sandbox execution: If deserialized data is used in execution contexts, ensure proper sandboxing.
4. Human oversight: For a production-grade governance score, consider adding human-in-the-loop controls for critical operations.

Learn More

For detailed remediation guidance and best practices for securing AI applications, visit inkog.io.

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This report was generated to help improve the security of your project. We hope you find it useful!