sc-lang-python

Detect insecure deserialization and pickle misuse in Python projects.

56|5|Updated Apr 8, 2026

Search Tags:#security #vulnerability #python #yaml #sast #pickle #deserialization

Ready to run

npx skills add https://github.com/ersinkoc/security-check --skill sc-lang-python

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sc-lang-python
Source: https://github.com/ersinkoc/security-check/tree/main
Command: npx skills add https://github.com/ersinkoc/security-check --skill sc-lang-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Python applications often suffer from insecure deserialization and unsafe pickle usage, leading to remote code execution and data compromise if untrusted data is deserialized. This skill provides Python-specific checks to identify these patterns across common runtimes and frameworks.

Core Features & Use Cases

Detects insecure deserialization patterns in Python code, including pickle and YAML-based loads.
Analyzes Django, Flask, FastAPI, and other Python applications for risky data-loading paths and gadget chains.
Generates structured findings aligned with the security-check pipeline for subsequent verification and remediation.

Quick Start

Run the Python security scan against this codebase to identify and remediate insecure deserialization patterns.

Frequently Asked Questions about sc-lang-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect insecure deserialization vulnerabilities in Python code?▼

Scan Python code to detect insecure deserialization vulnerabilities by identifying risky pickle loads and unsafe YAML parsing patterns. This analysis reveals remote code execution risks across Django, Flask, and FastAPI projects, yielding structured findings for remediation.

What are common Python pickle misuse patterns that lead to remote code execution?▼

Common pickle misuse patterns involve loading untrusted data streams directly via pickle or unsafe YAML, which can trigger arbitrary code execution. Analyzing data-processing scripts and framework endpoints for these risky serialization paths exposes dangerous gadget chains and data-loading operations.

Does this security scanner work with Django and FastAPI applications?▼

Yes, this security scanning approach works with Django, Flask, and FastAPI applications to identify risky data-loading paths. It analyzes Python projects across these common runtimes and frameworks to find insecure deserialization and pickle misuse patterns within the codebase.

How do I run a SAST scan for unsafe YAML loading in my Python project?▼

Run a SAST scan for unsafe YAML loading by applying Python-specific security checks to your codebase. This static analysis identifies risky data-loading paths and insecure deserialization patterns, generating structured findings aligned with the security-check pipeline for verification.

What is the best way to find risky serialization paths in data-processing scripts?▼

The best way to find risky serialization paths in data-processing scripts is through deep Python security scanning. This approach identifies unsafe pickle usage and insecure deserialization patterns, producing structured findings that integrate with reporting stages for subsequent verification.