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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 source installs☆ 0 Repository starsv1.1.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

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

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

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...

Source information

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/install-then-update-trap-detector
Version
1.1.0
Commit
eaf10bbd2a27586158ce57686f31f847101a4751
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

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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