Update GITHUB_TOKEN to MY_GITHUB_TOKEN in code-scan.yml and main.py

[adilshehzad786]

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[adilshehzad786]

Vulnerability Type	File	Line(s)	Description	Recommendation	Reference	Code Improvement
Information Disclosure	.github/workflows/code-scan.yml	21	The GITHUB_TOKEN secret is unnecessarily exposed to the action. While GitHub automatically redacts it from logs, best practice is to minimize access. Furthermore, using two different GitHub tokens (MY_GITHUB_TOKEN and GITHUB_TOKEN) introduces potential confusion and might lead to unintended access.	Use only MY_GITHUB_TOKEN (a Personal Access Token (PAT)) with the minimum required permissions. Remove GITHUB_TOKEN from the workflow.	https://docs.github.com/en/actions/security-guides/automatic-token-authentication#using-the-github_token-in-a-workflow	.github/workflows/code-scan.yml:21: Remove GITHUB_TOKEN and use only MY_GITHUB_TOKEN.
Excessive Permissions	.github/workflows/code-scan.yml	16	The workflow grants "write" access to issues, which is more permissive than needed. This could allow a malicious actor to modify or delete issues if the workflow is compromised.	Grant only the "create" permission for issues. This is sufficient to create comments on pull requests.	https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#permissions	.github/workflows/code-scan.yml:16: Change issues: write to issues: create.
Potential Secrets in Logs	main.py	75	The response.text from the GitHub API is logged in case of an error. This could potentially leak sensitive information in logs if the API returns unexpected data.	Avoid logging raw API responses unless absolutely necessary. If logging is required, sanitize the response to remove potentially sensitive details.	https://owasp.org/www-project-top-ten/OWASP_Top_Ten_2021/A03_Injection.html (Indirectly relevant as it highlights the importance of input validation, which applies to logging as well)	main.py:75: Replace response.text with a generic error message or log only relevant parts of the response after sanitization. Example: logger.error(f"Failed to create a comment on the PR. Status: {response.status_code}, Error: {response.json().get('message', 'Unknown error')}")

Summary:

• Total number of vulnerabilities found: 3
• Number of high severity vulnerabilities: 0
• Number of medium severity vulnerabilities: 2 ⚠️⚠️
• Number of low severity vulnerabilities: 1 ℹ️
• Overall status: "Pass"

It's important to remember that using any third-party AI tool (like Gemini) to analyze code introduces some level of risk, as you're sharing your code with an external service. Ensure the AI provider has strong security practices. Also, double-check the AI's recommendations, as it might sometimes produce false positives or inaccurate advice.