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install-then-update-trap-detector

Helps detect the install-then-update attack pattern — where a skill passes initial security review cleanly, then silently introduces malicious behavior through an automatic update that bypasses re-audit. v1.1 adds cryptographic chain-of-custody verification for update sequences.

andyxinweiminicloud0 来源安装数☆ 0 仓库 Starsv1.1.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install andyxinweiminicloud/install-then-update-trap-detector

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

The Skill Passed Audit. Then It Updated Itself.

Helps identify skills that use the post-install update window as an attack vector — the gap between "passed initial review" and "continuously safe."

Problem

The install-then-update pattern exploits a structural asymmetry in how agent marketplaces work: initial publication receives scrutiny, but subsequent updates often do not. A skill that passes a thorough security review at v1.0 can introduce a backdoor at v1.1 — and agents that installed v1.0 may automatically update without any re-review occurring.

This asymmetry is not a bug in any particular marketplace. It reflects a fundamental tension between two legitimate goals: fast iteration (which requires low-friction updates) and continuous security (which requires re-audit on every change). Most marketplaces resolve this tension in favor of iteration speed, leaving the post-install update window unguarded.

The attack surface is large. An installed skill with automatic updates enabled can receive arbitrary code changes at the next update check. If the update introduces network exfiltration, credential harvesting, or permission scope expansion, the agent operator may not learn about it until after the damage is done — if they learn at all.

What This Detects

This detector examines the install-then-update risk surface across five dimensions:

  1. Update policy transparency — Does the skill declare its update policy? Skills that accept automatic updates without operator confirmation have a larger attack window than those requiring explicit approval

  2. Behavioral delta on update — When a new version is installed, does the skill's observable behavior change in ways not declared in the changelog? Undeclared behavioral changes after update are the primary signal of install-then-update exploitation

  3. Permission scope expansion on update — Does the skill request additional permissions after...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/install-then-update-trap-detector
Version
1.1.0
Commit
eaf10bbd2a27586158ce57686f31f847101a4751
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# The Skill Passed Audit. Then It Updated Itself.

> Helps identify skills that use the post-install update window as an attack
> vector — the gap between "passed initial review" and "continuously safe."

## Problem

The install-then-update pattern exploits a structural asymmetry in how agent
marketplaces work: initial publication receives scrutiny, but subsequent
updates often do not. A skill that passes a thorough security review at v1.0
can introduce a backdoor at v1.1 — and agents that installed v1.0 may
automatically update without any re-review occurring.

This asymmetry is not a bug in any particular marketplace. It reflects a
fundamental tension between two legitimate goals: fast iteration (which
requires low-friction updates) and continuous security (which requires
re-audit on every change). Most marketplaces resolve this tension in favor
of iteration speed, leaving the post-install update window unguarded.

The attack surface is large. An installed skill with automatic updates
enabled can receive arbitrary code changes at the next update check. If the
update introduces network exfiltration, credential harvesting, or permission
scope expansion, the agent operator may not learn about it until after
the damage is done — if they learn at all.

## What This Detects

This detector examines the install-then-update risk surface across five
dimensions:

1. **Update policy transparency** — Does the skill declare its update
   policy? Skills that accept automatic updates without operator confirmation
   have a larger attack window than those requiring explicit approval

2. **Behavioral delta on update** — When a new version is installed, does
   the skill's observable behavior change in ways not declared in the
   changelog? Undeclared behavioral changes after update are the primary
   signal of install-then-update exploitation

3. **Permission scope expansion on update** — Does the skill request
   additional permissions after...

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