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OpenClaw

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 installations selon la source☆ 0 Étoiles du dépôtv1.1.0

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

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

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

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...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/install-then-update-trap-detector
Version
1.1.0
Commit
eaf10bbd2a27586158ce57686f31f847101a4751
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

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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