Python security vulnerability detection using Bandit SAST with CWE and OWASP mapping. Use when: (1) Scanning Python code for security vulnerabilities and anti-patterns, (2) Identifying hardcoded secrets, SQL injection, command injection, and insecure APIs, (3) Generating security reports with severity classifications for CI/CD pipelines, (4) Providing remediation guidance with security framework references, (5) Enforcing Python security best practices in development workflows.
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name
sast-bandit
description
Python security vulnerability detection using Bandit SAST with CWE and OWASP mapping. Use when: (1) Scanning Python code for security vulnerabilities and anti-patterns, (2) Identifying hardcoded secrets, SQL injection, command injection, and insecure APIs, (3) Generating security reports with severity classifications for CI/CD pipelines, (4) Providing remediation guidance with security framework references, (5) Enforcing Python security best practices in development workflows.
Bandit is a security-focused static analysis tool for Python that identifies common security vulnerabilities and coding anti-patterns. It parses Python code into Abstract Syntax Trees (AST) and executes security plugins to detect issues like hardcoded credentials, SQL injection, command injection, weak cryptography, and insecure API usage. Bandit provides actionable reports with severity classifications aligned to industry security standards.
Quick Start
Scan a Python file or directory for security vulnerabilities:
Create a configuration file .bandit or .bandit.yaml to customize scans:
# .bandit.yamlexclude_dirs:-/tests/-/venv/-/.venv/-/node_modules/skips:-B101# Skip assert_used checks in test filestests:-B201# Flask app run with debug=True-B301# Pickle usage-B601# Shell injection-B602# Shell=True in subprocess
Step 2: Execute Security Scan
Run Bandit against Python codebase:
# Basic scan with severity threshold
bandit -r . -ll # Report only medium/high severity# Comprehensive scan with detailed output
bandit -r . -f json -o report.json -v
# Scan with confidence filtering
bandit -r . -i # Show only high confidence findings# Exclude specific tests
bandit -r . -s B101,B601
>> Issue: [B105:hardcoded_password_string] Possible hardcoded password: 'admin123'
Severity: Medium Confidence: Medium
CWE: CWE-259 (Use of Hard-coded Password)
Location: app/config.py:12
Step 4: Prioritize Findings
Focus remediation efforts using this priority matrix:
Critical: HIGH severity + HIGH confidence
High: HIGH severity OR MEDIUM severity + HIGH confidence
Medium: MEDIUM severity + MEDIUM confidence
Low: LOW severity OR LOW confidence
Step 5: Remediate Vulnerabilities
For each finding, consult the bundled references/remediation_guide.md for secure coding patterns. Common remediation strategies:
Hardcoded Secrets (B105, B106): Use environment variables or secret management services
SQL Injection (B608): Use parameterized queries with SQLAlchemy or psycopg2
Command Injection (B602, B605): Avoid shell=True, use shlex.split() for argument parsing
Weak Cryptography (B303, B304): Replace MD5/SHA1 with SHA256/SHA512 or bcrypt for passwords
Insecure Deserialization (B301): Avoid pickle, use JSON or MessagePack with schema validation
Step 6: Integrate into CI/CD
Add Bandit to CI/CD pipelines to enforce security gates:
Use the bundled script scripts/bandit_analyzer.py for enhanced reporting with OWASP mapping.
Security Considerations
Sensitive Data Handling: Bandit reports may contain code snippets with hardcoded credentials. Ensure reports are stored securely and access is restricted. Use --no-code flag to exclude code snippets from reports.
Access Control: Run Bandit in sandboxed CI/CD environments with read-only access to source code. Restrict write permissions to prevent tampering with security configurations.
Audit Logging: Log all Bandit executions with timestamps, scan scope, findings count, and operator identity for security auditing and compliance purposes.
Compliance: Bandit supports SOC2, PCI-DSS, and GDPR compliance by identifying security weaknesses. Document scan frequency, remediation timelines, and exception approvals for audit trails.
False Positives: Review LOW confidence findings manually. Use inline # nosec comments sparingly and document justifications in code review processes.
Bundled Resources
Scripts (scripts/)
bandit_analyzer.py - Enhanced Bandit wrapper that parses JSON output, maps findings to OWASP Top 10, generates HTML reports, and integrates with ticketing systems. Use for comprehensive security reporting.
References (references/)
remediation_guide.md - Detailed secure coding patterns for common Bandit findings, including code examples for SQLAlchemy parameterization, secure subprocess usage, and cryptographic best practices. Consult when remediating specific vulnerability types.
cwe_owasp_mapping.md - Complete mapping between Bandit issue codes, CWE identifiers, and OWASP Top 10 categories. Use for security framework alignment and compliance reporting.
Assets (assets/)
bandit_config.yaml - Production-ready Bandit configuration with optimized test selection, exclusion patterns for common false positives, and severity thresholds. Use as baseline configuration for projects.
pre-commit-config.yaml - Pre-commit hook configuration for Bandit integration. Prevents commits with HIGH severity findings.
# Exit with error if HIGH severity issues found
bandit -r . -lll -f txt
if [ $? -ne 0 ]; thenecho"HIGH severity security issues detected - blocking merge"exit 1
fi
Pattern 3: Progressive Security Hardening
Incrementally increase security standards:
# Phase 1: Block only CRITICAL (HIGH severity + HIGH confidence)
bandit -r . -ll -i
# Phase 2: Block HIGH severity
bandit -r . -ll
# Phase 3: Block MEDIUM and above
bandit -r . -l
Pattern 4: Suppressing False Positives
Document exceptions inline with justification:
# Example: Suppressing pickle warning for internal serializationimport pickle # nosec B301 - Internal cache, not user inputdefload_cache(file_path):
withopen(file_path, 'rb') as f:
return pickle.load(f) # nosec B301
Integration Points
CI/CD: Integrate as GitHub Actions, GitLab CI, Jenkins pipeline stage, or pre-commit hook. Use scripts/bandit_analyzer.py for enhanced reporting.
Security Tools: Combine with Semgrep for additional SAST coverage, Safety for dependency scanning, and SonarQube for code quality metrics.
SDLC: Execute during development (pre-commit), code review (PR checks), and release gates (pipeline stage). Establish baseline scans for legacy code and enforce strict checks for new code.
Ticketing Integration: Use scripts/bandit_analyzer.py to automatically create Jira/GitHub issues for HIGH severity findings with remediation guidance.
Troubleshooting
Issue: Too Many False Positives
Solution:
Use confidence filtering: bandit -r . -i (HIGH confidence only)
Exclude test files: bandit -r . --exclude /tests/
Customize .bandit.yaml to skip specific tests for known safe patterns
Review and suppress with inline # nosec comments with justification
Issue: Scan Performance on Large Codebases
Solution:
Exclude dependencies: Add /venv/, /.venv/, /site-packages/ to .bandit.yaml exclude_dirs
Use multiprocessing: Bandit automatically parallelizes for directories
Scan only changed files in CI/CD: git diff --name-only origin/main | grep '.py$' | xargs bandit
Issue: Missing Specific Vulnerability Types
Solution:
Check enabled tests: bandit -l (list all tests)
Ensure tests are not skipped in .bandit.yaml
Combine with Semgrep for additional coverage (e.g., business logic vulnerabilities)
