🛡️ AI-Hacking Self-Audit (MVP v0.1)

A non-intrusive, zero-exfiltration personal security self-audit for macOS.

Why

AI-automated attacks now hit individuals, not just enterprises. This tool shows your security score and exactly what to fix — without attacking anything and without sending a single byte off your machine.

Principles

• Non-intrusive: reads local settings/permissions/git/network only. No exploiting.
• Zero-exfiltration: 100% local. No network calls (no socket/urllib/requests imports — verify it yourself).
• No value leakage: secret values are never recorded — only their location.
• Zero dependencies: Python standard library only → easy single-binary / pip install.

Install & run

pip install ai-hacking-defense
ai-hacking-defense     # or: python3 scan.py → security_report.html

Checks: credentials · network · OS hardening · AI-agent risk · backup

License: open-core (free core, paid Pro). Zero-exfil is verifiable in source.

Docs

• Install (EN) · 설치 (KO)
• FAQ (EN) · FAQ (KO) · Troubleshooting

Feedback becomes releases

ai-hacking-defense feedback builds a masked, fully-previewed report draft — nothing is ever sent automatically; you submit it yourself on GitHub. Issues are collected into a backlog daily and shipped as releases with a public CHANGELOG, so your report visibly becomes the next version.

Platforms (honest note)

macOS: all checks verified. Windows/Linux: experimental — runs, but not yet fully verified.

Known limits (honest)

Detection catches common evasions, not all. Known gaps we have not closed yet:

• SSH private-key detection reads the PEM header in the first 8KB; a key hidden past 8KB, or in a non-PEM container (e.g. PuTTY .ppk), may be missed.
• Backup freshness rejects future-dated and empty commits, but a single trivial commit made just now still counts as a "recent backup" — it does not verify the backup is meaningful or restorable.
• Detection rates (e.g. a 72-cell internal battery) are measured on our own corpora; real-world coverage will differ, and new evasions always exist.

What's in the package (v0.1.5)

Three stdlib-only command-line tools:

• ahd-scan (also ai-hacking-defense) — the 5-area security self-audit + HTML report. Zero network.
• ahd-agent-guard — prompt-injection detector (warn-only, no blocking). On a public prompt-injection evasion corpus it detects 29/30 (96.7%) at 0 false positives in our test. This 96.7% is the injection detector's rate on that one corpus — not an overall "defense rate" across every attack type. Zero network. echo "<text>" | ahd-agent-guard
• ahd-threat-feed — refreshes public vulnerability feeds (OSV, CISA KEV, EPSS) to a local cache. GET-only of public databases from a fixed allowlist — none of your data is ever sent.

"Zero-exfiltration" precisely

ahd-scan and ahd-agent-guard make no network calls at all. ahd-threat-feed only downloads public vulnerability data (GET-only, allowlisted hosts) and uploads nothing. In all cases, none of your files, secrets, or data ever leave your machine — that is what zero-exfiltration means here.

Not in the package (internal-only, not advertised)

This package detects and reports. It does not auto-block, run a background dashboard, or hook into your pipelines. "Warn-only" is literal — see each report card's limits.

Reproduce the detection rate yourself

pip install ai-hacking-defense
python3 reproduce_detection.py   # prints detection % and false-positive % on a public evasion corpus

It reports 29/30 = 96.7% detection at 0 false positives on our standard prompt-injection corpus (one honest miss: full letter-spacing). Scope note: this measures the prompt-injection detector only; other attack types (credential, vault, network) are measured separately and are lower — not rolled into a single "defense rate."