adversarial machine learning

Hands-on, bash-first guide to AI security from a Linux terminal: map the AI attack surface; pin and audit Python deps (pip-audit, bandit); inspect and baseline model API traffic (curl, mitmproxy, tshark/jq); red-team prompts with a simple shell harness; and hash datasets/models/prompts (yara) for provenance—ending with a lightweight CI checklist to catch leaks, tampering, and unsafe behaviors early.

Practical, Bash-first guide to hardening open-source AI on Linux: explains why LLMs widen attack surfaces (prompt injection, poisoned weights/datasets, secret exfiltration, runtime escapes) and delivers a step-by-step playbook: rootless Podman sandboxes, default-deny egress with firewalld, provenance checks (checksums, signatures, Git LFS), observability via auditd/eBPF, safe secrets (Podman secrets/pass), plus AppArmor/SELinux tips and a minimal secure recipe.

Hands-on guide to harden AI systems on Linux using Bash and open tools. Build four quick, production-ready defenses: 1) detect SSH auth anomalies with log features + Isolation Forest; 2) guard datasets/models via auditd, inotify, and minisign-signed manifests; 3) serve models rootlessly with Podman behind Nginx TLS with sane limits; 4) keep secrets out of code using pass/age. Ship one this week.

A hands-on, Bash-first guide to AI security showing how easily systems can leak secrets and how to harden them with reproducible shell steps: sanitize and scan RAG corpora to block prompt injection, lock down exposed model endpoints, detect dataset poisoning with fuzzy hashes and audits, and secure the ML supply chain with pinned, audited deps—complete with apt/dnf/zypper installs and a CI-friendly checklist.

A fast, Bash-only playbook for AI security: using curl, jq, ripgrep, git, OpenSSL, and GnuPG to run CI-ready AI Vulnerability Reviews that inventory and integrity-check model artifacts, audit Python dependencies via OSV, scan repos for leaked secrets, run prompt-injection smoke tests, and add lightweight runtime guardrails—improving provenance, safety, and compliance with minimal tooling.