review-bot-automator

Repository on GitHub, which provides public git repositories with URLs.

Repository on GitHub, which uses git. git can track the changes, who made them, and when they were made.

"GitHub repository contains all interim commits between releases. See commit history."

Repository on GitHub, which uses git. git is distributed.

Project uses Semantic Versioning (e.g., 2.0.0, 2.0.1) defined in pyproject.toml

All releases are tagged with git tags (v2.0.0, v2.0.1)

Non-trivial release notes file in repository: https://github.com/VirtualAgentics/review-bot-automator/blob/main/CHANGELOG.md.

No publicly known CVEs have been assigned to this project

GitHub Issues at https://github.com/VirtualAgentics/review-bot-automator/issues

GitHub Issues tracker with labels (bug, enhancement, etc.)

3/4 bug reports closed with resolution; 1 open is documented false positive

All 20+ enhancement issues have responses and are closed

GitHub Issues is publicly archived at https://github.com/VirtualAgentics/review-bot-automator/issues?q=is%3Aissue

https://github.com/VirtualAgentics/review-bot-automator/blob/main/SECURITY.md SECURITY.md documents process (GitHub private reporting + email)

https://github.com/VirtualAgentics/review-bot-automator/blob/main/SECURITY.md#how-to-report

No vulnerabilities reported in last 6 months (SECURITY.md: "Total Reported: 0")

Non-trivial build file in repository: https://github.com/VirtualAgentics/review-bot-automator/blob/main/Makefile.

Non-trivial build file in repository: https://github.com/VirtualAgentics/review-bot-automator/blob/main/Makefile.

he project is built entirely with FLOSS tools: Python 3.12, hatchling build backend, pip for dependencies, pytest for testing, and GitHub Actions for CI/CD. All development tools (Black, Ruff, MyPy, Bandit) are open source. See pyproject.toml and Makefile.

Justification: The project uses pytest as its automated test suite (FLOSS, MIT licensed). Test invocation is documented in README.md, CONTRIBUTING.md, and via Makefile targets. CI runs tests on every PR via .github/workflows/ci.yml. Current: 1888 tests, 89.13% coverage.

Tests are invoked using the standard Python testing conventions: pytest tests/ or make test. Documented in CONTRIBUTING.md sections "Verify setup" and "Development Workflow". Also available via make check-all which runs all quality checks.

Test coverage is 89.13% (exceeds 80% minimum). The test suite includes 1888 tests with 76 subtests across 14 methods. Coverage includes unit tests, integration tests, and property-based fuzzing with Hypothesis. Coverage is enforced in CI and uploaded to Codecov.

GitHub Actions CI runs on every push and PR to main/develop branches. Workflow .github/workflows/ci.yml runs linting, type checking, tests with coverage, and security scans. Additional workflows: security.yml, clusterfuzzlite.yml for continuous fuzzing.

CONTRIBUTING.md section "Testing" states: "Maintain >80% test coverage (strictly enforced in CI)". The document requires unit tests, integration tests, and property-based fuzzing for new functionality. Coverage gate in CI blocks PRs that reduce coverage below threshold.

CONTRIBUTING.md section "Testing" states: "Maintain >80% test coverage (strictly enforced in CI)". The document requires unit tests, integration tests, and property-based fuzzing for new functionality. Coverage gate in CI blocks PRs that reduce coverage below threshold.

CONTRIBUTING.md section "Testing" documents the test policy with specific requirements: unit tests, integration tests, property-based fuzzing, >80% coverage, pytest fixtures, and subtests usage. Additional guidance in docs/testing/TESTING.md and docs/testing/SUBTESTS_GUIDE.md.

he project uses multiple linters and analyzers: Ruff (comprehensive linting rules), MyPy in strict mode (--strict flag), Black for formatting, Bandit for security analysis. All run in pre-commit hooks and CI. See .github/workflows/ci.yml lines 43-57.

All warnings must be addressed before merge. Pre-commit hooks block commits with linting errors. CI jobs fail on any Black, Ruff, or MyPy violations. Security warnings from Bandit are reviewed. Zero tolerance policy enforced via branch protection requiring passing CI.

MyPy runs with --strict flag (maximum strictness). Ruff is configured with comprehensive rules in pyproject.toml. Black enforces consistent formatting with 100-char line length. Bandit scans for security issues. All tools run on both src/ and tests/ directories.

Primary developers demonstrate secure design knowledge through documented security architecture (docs/security-architecture.md) implementing all 8 Saltzer-Schroeder principles plus additional modern principles. Explicitly implemented: economy of mechanism (simple validation APIs), fail-safe defaults (dry-run mode default), complete mediation (all file paths validated), open design (all security code open source), separation of privilege (multi-layer validation), least privilege (read-only Git default), limited attack surface (workspace containment), input validation with allowlists (path and token validation). See SECURITY.md and docs/security/threat-model.md for STRIDE analysis.

Primary developers demonstrate knowledge of common vulnerabilities through explicit mitigations: Path Traversal (CWE-22) prevented by InputValidator.validate_file_path(); Injection (CWE-94) prevented by safe YAML/JSON/TOML parsers; Sensitive Data Exposure (CWE-200) prevented by SecretScanner with 14+ patterns; TOCTOU (CWE-367) prevented by atomic file operations. OWASP Top 10 mapping documented in docs/security/compliance.md. Bandit and CodeQL scan for common Python vulnerabilities in CI.

The project uses only standard, publicly published cryptographic algorithms: SHA-256 (hashlib) for cache keys and content hashing. No proprietary or custom cryptographic algorithms are used. All crypto implementations come from Python's standard library (hashlib) which uses well-reviewed OpenSSL implementations.

The project's primary purpose is PR automation, not cryptography. All cryptographic operations use Python's standard hashlib library for SHA-256 hashing. No custom cryptographic implementations exist. LLM API calls use provider SDKs (Anthropic, OpenAI) which handle TLS/HTTPS internally using standard libraries.

All cryptographic functionality uses Python's built-in hashlib module (part of Python standard library, PSF license) and standard HTTPS/TLS via httpx/requests libraries. No proprietary cryptographic dependencies. PyPI trusted publishing uses OIDC which is implemented via SLSA provenance generator (FLOSS).

The project uses SHA-256 (256-bit hash, exceeds NIST 224-bit minimum). HTTPS connections use TLS 1.2+ with modern cipher suites (handled by httpx/requests libraries). No symmetric encryption keys are generated by the application. GitHub API and LLM provider connections enforce strong TLS.

No broken cryptographic algorithms used. Grep search confirms no MD4, MD5 (for security), single DES, or RC4. Only SHA-256 is used for hashing. No cipher modes used (no encryption performed). HTTPS/TLS handled by standard libraries with secure defaults. Verified: grep -r "MD5|md5|DES|RC4" src/ returns no matches.

No cryptographic algorithms with known weaknesses. SHA-256 is used exclusively for hashing (SHA-1 never used). No SSH connections made by the application. HTTPS uses TLS 1.2+ via standard Python libraries. No CBC mode or other weak modes are configured by the application.

he project does not implement key agreement protocols. All external communications use HTTPS via standard Python libraries (httpx, requests) which support PFS through their TLS implementations. No custom TLS configuration that would disable PFS.

he project does not store passwords for external user authentication. GitHub tokens are provided via environment variables and never stored persistently. LLM API keys are passed through environment variables. No user authentication database exists.

The project uses Python's secrets module for any security-sensitive random values (when needed). Cache key generation uses deterministic SHA-256 hashing of content. No cryptographic nonces or random keys are generated. Standard Python random is only used for non-security-critical purposes (e.g., test data generation with Hypothesis).

ll delivery mechanisms use HTTPS: GitHub repository (https://github.com/VirtualAgentics/review-bot-automator), PyPI package (https://pypi.org/project/review-bot-automator/). Release workflow uses SLSA provenance generation (slsa-framework/slsa-github-generator) for supply chain security. All CI dependencies fetched via HTTPS with hash verification (--require-hashes).

The project uses SLSA Level 3 provenance for releases (see .github/workflows/release.yml). Build artifacts include SHA256 hashes generated in the build job. PyPI publishing uses trusted publishing via OIDC (no long-lived credentials). Hashes are never retrieved over HTTP - all artifact verification happens within GitHub Actions secure environment.

No unpatched vulnerabilities of medium or higher severity. pip-audit runs daily in CI (.github/workflows/security.yml) and blocks PRs with vulnerabilities. Renovate monitors for security updates and creates PRs automatically. Current status: 0 known vulnerabilities (verified via pip-audit --desc). Issue #75 (black PYSEC-2024-48) is a false positive, not an actual vulnerability in our usage.

SECURITY.md defines response timelines: critical vulnerabilities within 72 hours for initial response, 7 days for fix. Daily security scanning via pip-audit, Trivy, and CodeQL. Renovate creates security PRs automatically. Current status: 0 reported vulnerabilities, 0 public advisories. Vulnerability tracking at GitHub Security tab.

TruffleHog scans git history for secrets in CI (.github/workflows/security.yml). SecretScanner with 14+ patterns detects secrets in code. Pre-commit hooks prevent accidental commits. .gitignore excludes .env files. GitHub tokens stored in environment variables, never in code. No sample credentials exist in repository. Verified by TruffleHog v3 with --only-verified flag.

Multiple static analysis tools applied before every release: (1) Ruff - comprehensive Python linter with 13 rule categories including security (S rules from Bandit); (2) MyPy in strict mode - static type checking for type errors; (3) Bandit - Python security linter detecting common vulnerabilities; (4) CodeQL - GitHub's semantic security analysis engine. All tools run on every PR in .github/workflows/ci.yml and .github/workflows/security.yml. Configuration in pyproject.toml (lines 102-159).

Multiple tools specifically designed for vulnerability detection: (1) Bandit - detects Python security issues (CWE patterns, injection, etc.); (2) CodeQL - semantic analysis for security vulnerabilities with Python-specific queries; (3) Ruff S rules - Bandit-equivalent security rules integrated into Ruff (see pyproject.toml line 124: "S"). These tools detect OWASP Top 10 and CWE/SANS Top 25 vulnerabilities including injection, path traversal, and insecure deserialization.

All static analysis findings are addressed before merge. CI workflow blocks PRs with Bandit, Ruff, or MyPy failures. CodeQL alerts are reviewed and resolved. SECURITY.md defines timelines: critical vulnerabilities 72 hours, high severity 1 week. Current status: 0 open CodeQL alerts, 0 unresolved Bandit findings. Branch protection requires all CI checks to pass before merge.

Static analysis runs on every commit via pre-commit hooks and on every PR via GitHub Actions CI. Workflows: .github/workflows/ci.yml runs Ruff, Black, MyPy, Bandit on every push/PR; .github/workflows/security.yml runs CodeQL analysis daily and on every PR. Pre-commit hooks (.pre-commit-config.yaml) run Ruff and Black locally before each commit.

Multiple dynamic analysis tools applied: (1) ClusterFuzzLite - Google's coverage-guided fuzzer using Atheris (.github/workflows/clusterfuzzlite.yml); (2) Hypothesis - property-based testing with automated input generation; (3) pytest - test suite with 89.13% branch coverage (1888 tests). ClusterFuzzLite runs on every PR (2-min quick fuzz) and weekly (30-min deep fuzz). Three fuzz targets in fuzz/: fuzz_input_validator.py, fuzz_secret_scanner.py, fuzz_handlers.py.

ClusterFuzzLite runs with memory safety sanitizers for the Python C extensions and underlying libraries: (1) Address Sanitizer (ASan) - detects buffer overflows, use-after-free; (2) Undefined Behavior Sanitizer (UBSan) - detects undefined behavior. Configuration in .github/workflows/clusterfuzzlite.yml lines 57, 67: sanitizer: [address, undefined]. Fuzzing targets test security-critical components (InputValidator, SecretScanner, file handlers).

ClusterFuzzLite runs with memory safety sanitizers for the Python C extensions and underlying libraries: (1) Address Sanitizer (ASan) - detects buffer overflows, use-after-free; (2) Undefined Behavior Sanitizer (UBSan) - detects undefined behavior. Configuration in .github/workflows/clusterfuzzlite.yml lines 57, 67: sanitizer: [address, undefined]. Fuzzing targets test security-critical components (InputValidator, SecretScanner, file handlers).

All vulnerabilities discovered through dynamic analysis are fixed before release. ClusterFuzzLite crash artifacts are uploaded and triaged. Hypothesis failures in CI block PRs. SECURITY.md defines response timelines. Current status: 0 open crash reports from fuzzing, 0 unresolved dynamic analysis findings. Weekly deep fuzzing (30 minutes) provides ongoing vulnerability detection.