惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

大猫的无限游戏
大猫的无限游戏
云风的 BLOG
云风的 BLOG
小众软件
小众软件
V
V2EX
博客园 - Franky
博客园 - 司徒正美
Apple Machine Learning Research
Apple Machine Learning Research
量子位
博客园 - 【当耐特】
雷峰网
雷峰网
WordPress大学
WordPress大学
Jina AI
Jina AI
Google DeepMind News
Google DeepMind News
N
Netflix TechBlog - Medium
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
阮一峰的网络日志
阮一峰的网络日志
IT之家
IT之家
Blog — PlanetScale
Blog — PlanetScale
Hugging Face - Blog
Hugging Face - Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
Visual Studio Blog
Microsoft Security Blog
Microsoft Security Blog

Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
GitHub - lo2589/AILOCK
yoliliya · 2026-05-31 · via Hacker News - Newest: "AI"

Keep code encrypted on disk, decrypt it only in memory, and still run it normally.

AiLock encrypts files in place so filesystem-level AI access (read_file, grep, cat, codebase indexing) sees only binary ciphertext. At the same time, developers can run encrypted Python code, import encrypted modules, read encrypted data files, and edit locked files through controlled plaintext views. The central idea is memory-only decryption: plaintext is materialized inside the AiLock runtime process, not written back to the working tree.

Disk: ciphertext for AI and ordinary file readers. Runtime: plaintext only inside the controlled execution process.

Tested in Windows and macOS development scenarios.

Chinese README

Why AiLock?

Most encryption tools protect files at rest, but make the code unusable until it is decrypted back onto disk. AiLock is built for a different workflow:

  • AI-opacity: coding assistants that read the working tree see ciphertext.
  • Memory-only execution: ailock run decrypts encrypted Python files inside the process and executes them without restoring plaintext on disk.
  • Transparent imports: encrypted modules can import each other.
  • Transparent file I/O: open(), Path.read_text(), and Path.read_bytes() can return plaintext inside the runtime.
  • GUI plaintext viewport: ailock open lets the developer inspect and edit files without leaving plaintext in the working tree.
  • Recovery path: encrypted backups and optional recovery keys help recover damaged or forgotten-password files.

Requirements

  • Python 3.11 or newer
  • pip
  • Runtime packages installed automatically from pyproject.toml: argon2-cffi, cryptography, and pyzipper
  • tkinter for ailock open; it is bundled with many Python installations, but some Linux distributions package it separately as python3-tk
  • Windows and macOS are the primary tested desktop development environments.

Installation

Install from GitHub:

git clone https://github.com/lo2589/AILOCK.git
cd AILOCK
pip install .

For editable development installs:

git clone https://github.com/lo2589/AILOCK.git
cd AILOCK
pip install -e .

Check the command:

If the command is not on your PATH, use the module entry point:

Quick Start

# Encrypt a file in place.
ailock lock secret.py

# AI/file tools see ciphertext.
cat secret.py
grep "password" .

# You can still use the code.
ailock show secret.py
ailock run secret.py
ailock open .

# Restore plaintext on disk when needed.
ailock unlock secret.py

The key idea:

ailock lock app.py      # app.py becomes ciphertext on disk
ailock run app.py       # app.py is decrypted in memory and executed

Memory-only Execution

ailock run is the core feature. It decrypts the entry file in memory, executes the plaintext inside the Python process, and leaves the working-tree file as ciphertext. No plaintext copy is written next to the encrypted file.

ailock run main.py
ailock run -m mypackage
ailock run app.py -- --port 8080

While the program is running, AiLock installs hooks so application code can behave as if the files were plain:

encrypted .py on disk -> decrypt in memory -> exec/import inside Python
encrypted data file   -> decrypt in memory -> open()/Path.read_text()

Inside your program, no AiLock-specific code is required:

import json
from secret_module import algorithm

with open("config.json") as f:
    config = json.load(f)

print(algorithm(config))

If secret_module.py or config.json is locked, AiLock decrypts it for the runtime while the filesystem still contains ciphertext.

Commands

ailock lock <path>

Encrypt a file or directory in place.

ailock lock secret.py
ailock lock src/
ailock lock secret.py --recovery

Notes:

  • Directories are processed recursively.
  • Already locked files are skipped.
  • Plaintext backups are stored as encrypted ZIP backups under .ailock/backups/ by default.
  • --recovery prints a recovery key. Save it separately; it is not shown again.

ailock run <path>

Run encrypted Python code without writing plaintext back to disk.

ailock run main.py
ailock run -m mypackage
ailock run app.py -- --port 8080

Runtime interception layers:

  • import hook for encrypted Python modules
  • patched builtins.open
  • patched pathlib.Path.read_text and pathlib.Path.read_bytes

ailock open [path]

Open a GUI plaintext viewport/editor for a directory.

ailock open .
ailock open src/

Locked files are decrypted for display. Saving writes encrypted content back to disk.

ailock show <file>

Print decrypted content to stdout without modifying the file.

ailock show secret.py
ailock show secret.py | head

ailock unlock <path>

Decrypt a file or directory back to plaintext on disk.

ailock unlock secret.py
ailock unlock src/ --backup

ailock recover <file>

Recover a locked file using a recovery key generated by --recovery.

ailock freelock [path]

Start a stdin/stdout JSON-RPC workspace server for controlled plaintext access.

Example requests:

{"method": "list_files", "params": {}, "id": 1}
{"method": "read_file", "params": {"path": "main.py"}, "id": 2}
{"method": "grep", "params": {"pattern": "TODO"}, "id": 3}
{"method": "write_file", "params": {"path": "main.py", "content": "..."}, "id": 4}
{"method": "flush", "params": {}, "id": 5}

Other Commands

ailock status file.py
ailock forget
ailock forget --all
ailock config
ailock config backup-dir /path/to/backups
ailock init --as aa

ailock init --as <name> installs a local launcher under a custom command name. This is useful when you want the unlock command to be deployment-specific.

Security Model

AiLock targets filesystem-level AI access. It is designed for coding assistants and indexers that inspect files through ordinary reads. In that model, locked files reveal only ciphertext.

AiLock does not claim to stop a fully informed local adversary who can run arbitrary commands, capture process memory, or trick the user into decrypting files. For stronger isolation, combine AiLock with operating-system execution policy, process isolation, and careful secret handling.

Cryptography

  • Argon2id for password-derived keys
  • ChaCha20-Poly1305 for authenticated encryption
  • independent random file keys
  • password wrapping for file keys
  • optional recovery-key wrapping
  • encrypted ZIP backups for emergency recovery

Project Layout

aloc/
  cli.py        command-line interface
  runner.py     in-memory execution engine
  workspace.py  decrypted workspace API and JSON-RPC handler
  gui.py        tkinter GUI editor
  crypto.py     Argon2id and ChaCha20-Poly1305 helpers
  format.py     locked-file format parser/encoder
  fileops.py    atomic writes and backup helpers
  cache.py      sudo-style password cache
  manifest.py   .ailock manifest and backup management
  recovery.py   recovery key support
  install.py    custom command-name launcher

Dependency Summary

  • argon2-cffi
  • cryptography
  • pyzipper
  • tkinter for the GUI, provided by many Python installations

License

MIT