azure-eventgrid-py

The skill azure-eventgrid-py primarily consists of documentation and code examples for interacting with Azure Event Grid using the Python SDK. The SKILL.md file provides installation instructions (pip install azure-eventgrid azure-identity) and various code snippets demonstrating how to publish events. The references/acceptance-criteria.md file further clarifies correct usage and explicitly links to the official Azure SDK for Python GitHub repository (https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/eventgrid/azure-eventgrid).

Threat Category Analysis:

1. Prompt Injection: No patterns indicative of prompt injection were found in either file. The content is instructional and does not attempt to manipulate the AI's behavior.
2. Data Exfiltration: The skill's purpose is to send events to Azure Event Grid, which is its intended function. It uses DefaultAzureCredential for authentication, which is a secure method relying on environment variables or managed identities. There are no commands attempting to read sensitive local files (e.g., ~/.aws/credentials, ~/.ssh/id_rsa) or exfiltrate data to untrusted external domains. The acceptance-criteria.md explicitly warns against hardcoding credentials.
3. Obfuscation: No obfuscation techniques such as Base64 encoding, zero-width characters, homoglyphs, or other encoding methods were detected.
4. Unverifiable Dependencies: The skill instructs pip install azure-eventgrid azure-identity. Both azure-eventgrid and azure-identity are official packages from the Azure SDK for Python, maintained by Microsoft, which is a trusted GitHub organization. The references/acceptance-criteria.md confirms this by linking to the official repository. This is noted as an EXTERNAL_DOWNLOADS category but is considered safe due to the trusted source.
5. Privilege Escalation: No commands like sudo, chmod +x, chmod 777, or attempts to install system services were found.
6. Persistence Mechanisms: No attempts to modify shell configuration files (.bashrc, .zshrc), create cron jobs, or establish other persistence mechanisms were detected.
7. Metadata Poisoning: The skill's metadata (name, description, package) is clean and accurately reflects the skill's purpose, with no hidden malicious instructions.
8. Indirect Prompt Injection: The skill itself does not process external user-controlled content in a way that would lead to indirect prompt injection within its execution context. The risk of indirect injection would lie downstream if the events published by the skill are later processed by an LLM, which is outside the scope of this skill's direct security analysis.
9. Time-Delayed / Conditional Attacks: No conditional logic based on dates, times, usage counts, or specific environment variables to trigger malicious behavior was found.

Conclusion: The skill is well-documented, uses standard and secure practices for authentication, and relies on official, trusted external dependencies. No security vulnerabilities or malicious patterns were identified.