secure coding

Practical, Bash-friendly baseline for securing Python-based AI projects on Linux: lock and audit dependencies with pip-tools and pip-audit; verify model/data integrity and avoid unsafe deserialization; keep secrets out of code with env vars and pre-commit scanning; enforce ruff/black/bandit/mypy; and sandbox execution via firejail or containers—raising your security bar without slowing delivery.

AI-suggested Bash can be fast—and dangerously wrong. This guide flags risky patterns (curl|bash, eval, unquoted vars, sudo sprawl) and gives a practical, automatable checklist: harden/lint scripts (set -euo pipefail, ShellCheck/shfmt), sandbox runs (podman/firejail/bwrap), lock down env/secrets/egress, verify downloads/signatures, and log/audit for reproducibility. For devs/SREs/sysadmins today.