harden: use yaml.safe_load in generate_index_of_sub_section.py...

[anupamme]

Summary

Harden input handling in scripts/generate_index_of_sub_section.py (flagged by semgrep).

Vulnerability

Field	Value
ID	gitlab.bandit.B506
Severity	HIGH
Scanner	semgrep
Rule	gitlab.bandit.B506
File	scripts/generate_index_of_sub_section.py:61
Assessment	Defensive hardening

Description: The application was found using an unsafe version of yaml load which is vulnerable to
deserialization attacks. Deserialization attacks exploit the process of reading serialized
data and turning it back
into an object. By constructing malicious objects and serializing them, an adversary may
attempt to:

• Inject code that is executed upon object construction, which occurs during the
  deserialization process.
• Exploit mass assignment by including fields that are not normally a part of the serialized
  data but are read in during deserialization.

To remediate this issue, use safe_load() or call yaml.load() with the Loader argument
set to
yaml.SafeLoader.

Example loading YAML using safe_load:

import yaml

# Use safe_load to load data into an intermediary object
intermediary_object = yaml.safe_load("""user:
    name: 'test user'"""
)
# Create our real object, copying over only the necessary fields
user_object = {'user': {
        # Assign the deserialized data from intermediary object
        'name': intermediary_object['user']['name'],
        # Add in protected data in object definition (or set it from a class constructor)
        'is_admin': False,
    }
}
# Work with user_object
# ...

For more details on deserialization attacks in general, see OWASP's guide:

• https://cheatsheetseries.owasp.org/cheatsheets/Deserialization_Cheat_Sheet.html

Threat Model Context

This is a web service - vulnerabilities in request handlers are directly exploitable by remote attackers.

Changes

• scripts/generate_index_of_sub_section.py

Behavior Preservation

The change is scoped to 1 file on the vulnerable path; it only tightens handling of untrusted input and leaves valid inputs unaffected.

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This patch removes an exploit primitive — a code pattern that, while not independently exploitable today, could be chained with other weaknesses by automated exploit-development tooling. Proactive removal of such primitives raises the bar against increasingly capable automated attack tools.

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Automated security fix by OrbisAI Security