AST02 — Supply Chain Compromise

Severity: Critical
Platforms Affected: All

Description

Skill registries and distribution channels lack the provenance controls common in mature package ecosystems (npm, PyPI, Cargo). Attackers exploit this absence through coordinated mass uploads, dependency confusion, account takeover, and repository poisoning. Configuration files that were once passive metadata have become active execution paths — the CI/CD pipeline now includes skills as a first-class attack surface.

Why It’s Unique to Skills

The barrier to publishing on ClawHub was a SKILL.md file and a GitHub account one week old. No code signing, no security review, no sandbox by default. Agent skills also inherit execution context from the agent runtime, meaning a compromised skill gains the agent’s full credential set — not just the permissions of a sandboxed package.

Real-World Evidence

  • ClawHub: no automated scanning at time of ClawHavoc; publishers could upload unlimited packages.
  • Claude Code CVE-2025-59536 / CVE-2026-21852: repository configuration files (.claude/settings.json, hooks) become execution paths; simply cloning and opening a malicious repo triggers RCE and API key exfiltration before the user sees any dialog.
  • Dependency confusion: a skill’s package.json or requirements.txt pulls a typosquatted nested dependency containing the actual payload — the surface skill appears clean.
  • Snyk-documented attack: skill named “Summarize YouTube Videos” imports yutube-dl-core instead of a legitimate package; nested dependency installs a backdoor.
  • Trail of Bits (Jun 3, 2026): public skill marketplaces (skills.sh, ClawHub) run a “ship-first, secure-later” model with one-click install and no meaningful vetting — and the scanners meant to backstop them were all bypassed in under an hour (see AST08). Their recommendation is the traditional supply-chain one: curate dependencies in an internal/approved marketplace, pin versions, and control who can publish or update — automated scanning cannot replace that.
  • Air Security, The Story of Skills (Jun 22, 2026): a researcher-built malicious skill entered a ~36K-star community plugin marketplace through an accepted pull request, inheriting its stars and credibility; promoted on social media, it reached over 26,000 agents — including corporate ones — while scanners, stars, and reputation all cleared it.
  • Air Security, The Circus of Skills (Jun 24, 2026): a scan of 142,836 live skills found 17,822 (~12.4%, 6.7M installs) rest on at least one untrusted external resource — sketchy domains, zero-reputation GitHub repos, freshly published packages, free-tier hosts — each an unpinned dependency that can turn malicious without the skill itself changing.
  • Air Security, SkillJacking (Jul 2, 2026): 925 skills serving ~134K agents sit on instantly hijackable sources — deleted GitHub accounts, unregistered packages, expired domains, freed cloud-app slots. Researchers took over the most popular video-generation skill on skills.sh (11,483 installs) by re-registering its deleted owner account; the marketplace listing kept its stars, trust, and installs.

Attack Scenarios

Registry Flooding

Coordinated upload of hundreds of malicious skills to crowd out legitimate alternatives.

Dependency Confusion

Poison a nested dependency, not the top-level skill — bypasses surface-level scans.

Config-File Hijacking

Embed execution instructions in repository config files (hooks, MCP settings, environment overrides) that trigger at project open.

Maintainer Account Takeover

Compromise a trusted skill author’s account, push a backdoored version.

Preventive Mitigations

  1. Implement skill provenance tracking: link each published skill to a verified code-signing identity, and have the signature cover a canonical digest of SKILL.md plus every declared resource file, so any post-publish tampering invalidates it. Standard signing schemes apply (e.g. ES256 / ed25519); until skill formats expose a first-class field, the binding can live in the SKILL.md metadata extension point.
  2. Require transparency logs for all registry operations (publish, update, delete) — similar to Certificate Transparency.
  3. Pin all nested dependencies to immutable hashes (sha256:), not version ranges.
  4. Treat repository configuration files (hooks, .claude/settings.json, ANTHROPIC_BASE_URL) as executable code and apply trust gates accordingly.
  5. Scan recursive dependency trees, not just top-level skill files.
  6. Support an internal skill mirror / allowlist for enterprise deployments.
  7. Provide revocation infrastructure: support revoking a compromised signing key (invalidating every skill signed with it), a single skill version by content digest, or an entire publisher; have hosts consult a revocation endpoint at load time and cache its state within a bounded freshness window.

Code Example: Dependency Pinning

# requirements.txt - BAD (version ranges)
requests>=2.25.0
beautifulsoup4>=4.9.0

# requirements.txt - GOOD (pinned hashes)
requests==2.31.0 --hash=sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7b4300988b709f44e013003f996
beautifulsoup4==4.12.2 --hash=sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da396

Code Example: Transparency Log Verification

import requests
import hashlib

def verify_transparency_log(skill_name: str, expected_hash: str) -> bool:
    """Verify skill exists in transparency log"""
    log_url = f"https://transparency.skillregistry.org/log/{skill_name}"
    response = requests.get(log_url)
    
    if response.status_code != 200:
        return False
    
    # Check if our expected hash is in the log
    log_entries = response.json()
    return any(entry['hash'] == expected_hash for entry in log_entries)

Code Example: SKILL.md Integrity Check

import hashlib

def verify_skill_file(file_path: str, expected_hash: str) -> bool:
    """Verify integrity of SKILL.md"""
    with open(file_path, "rb") as f:
        content = f.read()

    actual_hash = hashlib.sha256(content).hexdigest()
    return actual_hash == expected_hash

OWASP Mapping

  • LLM03 (Supply Chain)
  • ASVS V14.2 (Dependency)
  • CWE-494 (Download of Code Without Integrity Check)

MAESTRO Framework Mapping

MAESTRO Layer Layer Name AST02 Mapping
Layer 7 Agent Ecosystem Registry compromise, marketplace manipulation
Layer 3 Agent Frameworks Compromised components, supply chain attacks
Layer 6 Security & Compliance Policy enforcement, access controls
Layer 4 Deployment & Infrastructure IaC manipulation, runtime environment security

MAESTRO Layer Details

  • Layer 7: Agent Ecosystem - primary for registry provenance and marketplace trust.
  • Layer 3: Agent Frameworks - supply chain and compromised component risk in skill loaders.
  • Layer 6: Security & Compliance - missing governance controls and policy enforcement gaps.
  • Layer 4: Deployment & Infrastructure - compromised deployment pipelines enabling poisoned skill updates.

Reference Materials

Supply Chain Risk Assessment Framework

When evaluating skill supply chain risks, consider these factors:

  1. Publisher Verification
    • Code signing key age and rotation history
    • Publisher account creation date and activity patterns
    • Cross-reference with known malicious actor databases
  2. Dependency Analysis
    • Complete dependency tree mapping
    • Third-party library vulnerability scanning
    • License compatibility and compliance
  3. Registry Security
    • Transparency log implementation
    • Automated malware scanning
    • Two-person rule for emergency updates

Enterprise Supply Chain Controls

For organizations deploying agent skills:

  • Private Mirrors: Host approved skills on internal registries
  • Automated Scanning: Integrate with existing CI/CD security gates
  • Change Management: Require approval for skill updates in production
  • Inventory Management: Track all installed skills across the organization

Detection and Response

Supply chain compromise indicators:

  • Unexpected skill updates or version changes
  • New dependencies in existing skills
  • Publisher account changes
  • Registry outage followed by rapid updates
  • Anomalous download patterns

References


Last updated: June 2026


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Leadership & Founding Members

Project Leadership

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Ken Huang

Ken Huang

Hammad Atta

Hammad Atta

Fabio Cerullo

Fabio Cerullo

Aonan Guan

Aonan Guan

Bhavya Gupta

Bhavya Gupta

Niv Hoffman

Niv Hoffman

Iftach Orr

Iftach Orr

Akram Sheriff

Akram Sheriff

AIVSS Distinguished Review Board

The OWASP AIVSS project’s Distinguished Review Board comprises world-renowned cybersecurity leaders, former government officials, and industry pioneers who provide strategic guidance and expert oversight for the AI Vulnerability Scoring System framework. We thank them for their guidance, several of whom have also supported this project’s work.

Rob Joyce

Rob Joyce

Advisor to PwC and OpenAI, Former Special Assistant to the President and Cybersecurity Coordinator

Jason Clinton

Jason Clinton

Deputy CISO, Anthropic

Amy R. Steagall

Amy R. Steagall

Chief Information Security Officer, Stanford University

Martin Stanley

Martin Stanley

AI Risk Management Framework Lead, NIST

Apostol Vassilev

Apostol Vassilev

Research Supervisor, NIST

Andrew Coyne

Andrew Coyne

CISO, Banner Health, Former CISO, Mayo Clinic

Kevin Rocque

Kevin Rocque

Managing Director/Executive Vice President, Global Technology Risk Officer, TD Bank

Jeff Williams

Jeff Williams

Former Global OWASP Chair, Founder and CTO, Contrast Security

Michael Tran Duff

Michael Tran Duff

University Chief Information Security and Data Privacy Officer, Harvard University

Emil Bender Lassen

Emil Bender Lassen

Standards Lead, AIUC-1

Agentic Skills Top 10 Founding Members

Founding members of the OWASP Agentic Skills Top 10 project itself — project leads, co-leads, and additional contributors — listed alphabetically. Several also contribute to the sibling OWASP AIVSS project listed above.

Ken Huang

Ken Huang

Project Lead, Agentic Skills Top 10

Hammad Atta

Hammad Atta

Co-Lead, Agentic Skills Top 10

Manish Bhatt

Manish Bhatt

Security Researcher, AWS

Fabio Cerullo

Fabio Cerullo

Co-Lead, Agentic Skills Top 10

David Girard

David Girard

Senior Director, AI Security & AI Alliances, Trend Micro

Aonan Guan

Aonan Guan

Co-Lead, Agentic Skills Top 10

Bhavya Gupta

Bhavya Gupta

Co-Lead, Agentic Skills Top 10

Pamela Gupta

Pamela Gupta

Founder & CEO, OutSecure / Trusted AI

Idan Habler

Idan Habler

Staff AI/ML Security Researcher, Intuit

Niv Hoffman

Niv Hoffman

CTO, Air Security

Charles Iheagwara

Charles Iheagwara

AI/ML Security Leader, AstraZeneca

Sushmitha Janapareddy

Sushmitha Janapareddy

Director - Security Integrations, American Express

Edward Lee

Edward Lee

Vice President, Lead AI Security, JP Morgan

KJ Lian

KJ Lian

Senior Manager, Data & AI (Public Sector), AWS

Vineeth Sai Narajala

Vineeth Sai Narajala

Application Security, AWS

Iftach Orr

Iftach Orr

Co-Lead, Agentic Skills Top 10

Kanna Sekar

Kanna Sekar

Cyber Security, Google

Akram Sheriff

Akram Sheriff

Co-Lead, Agentic Skills Top 10

Dennis Xu

Dennis Xu

Research VP, AI, Gartner

OWASP AIVSS Founding Members

The OWASP AIVSS (Agentic AI Vulnerability Scoring System) project is a sibling OWASP initiative focused on scoring the severity of agentic AI vulnerabilities. Its founding members are recognized here as OWASP founding members in the agentic AI security space; many of them have also contributed directly to the Agentic Skills Top 10 project’s research and review process.

Sunil Agrawal

Sunil Agrawal

Chief Information Security Officer, Glean

David Ames

David Ames

Partner, PwC

Michael Bargury

Michael Bargury

Founder and CTO, Zenity

Joshua Beck

Joshua Beck

Application Security Architect, SAS

Manish Bhatt

Manish Bhatt

Security Researcher, Amazon Kuiper Security

Mark Breitenbach

Mark Breitenbach

Security Engineer, Dropbox

Anat Bremler-Barr

Anat Bremler-Barr

Professor of Computer Science, Tel Aviv University

Siah Burke

Siah Burke

HIPAA Security Officer, Siah.ai

David Campbell

David Campbell

AI Security, Scale AI

Ying-Jung Chen

Ying-Jung Chen

AI safety researcher, PhD, Georgia Institute of Technology

Anton Chuvakin

Anton Chuvakin

Security Solution Strategy, Google

Jason Clinton

Jason Clinton

CISO, Anthorphic

Adam Dawson

Adam Dawson

Staff AI Security Researcher, Dreadnode

Leon Derczynski

Leon Derczynski

Principal Research Scientist, NVIDIA

Walker Lee Dimon

Walker Lee Dimon

AI Security Researcher, MITRE

Marissa Dotter

Marissa Dotter

AI Security Researcher, MITRE

Dan Goldberg

Dan Goldberg

ISO Market Lead, Omnicom

David Haber

David Haber

CEO, Lakera

Idan Habler

Idan Habler

Staff AI/ML Security Researcher, Intuit

Jason Haddix

Jason Haddix

Founder, Arcanum Information Security

Keith Hoodlet

Keith Hoodlet

Director of AI/ML & AppSec, Trail of Bits

Ken Huang

Ken Huang

AIVSS Project Lead, OWASP

Chris Hughes

Chris Hughes

CEO, Aquia

Charles Iheagwara

Charles Iheagwara

AI/ML Security Leader, AstraZeneca

Krystal Jackson

Krystal Jackson

Researcher, Center for Long-Term Cybersecurity, UC Berkeley

Sushmitha Janapareddy

Sushmitha Janapareddy

Director - Security Integrations, American Express

Rob Joyce

Rob Joyce

Former Cybersecurity Director of NSA, Advisor to PwC, PwC

Diana Kelley

Diana Kelley

CISO, Noma Security

Prashant Kulkarni

Prashant Kulkarni

Lead AI Security Research Engineer, Google Cloud

Mahesh Lambe

Mahesh Lambe

Founder, MIT, Unify Dynamics

Edward Lee

Edward Lee

Vice President, Lead AI Security, JP Morgan

Nate Lee

Nate Lee

CEO, Cloudsec.ai

Vishwas Manral

Vishwas Manral

CEO, Precize.ai

Daniela Muhaj

Daniela Muhaj

Executive-in-Residence for Research & Development, AI 2030

Vineeth Sai Narajala

Vineeth Sai Narajala

Application Security, AWS

Om Narayan

Om Narayan

AI Security Researcher, AWS

Varun Pant

Varun Pant

Engineering and Product Leader, AI applications at the Automated Reasoning Group, AWS

Advait Patel

Advait Patel

Senior Site Reliability Engineer (DevSecOps + Cloud + AIOps), Broadcom, IEEE

Alex Polyakov

Alex Polyakov

CEO, adversa.ai

Ramesh Raskar

Ramesh Raskar

Professor & Director, MIT Media Lab

Ron F. Del Rosario

Ron F. Del Rosario

VP-Head of AI Security, SAP

Tal Shapira

Tal Shapira

Co-Founder & CTO, Reco AI

Akram Sheriff

Akram Sheriff

Senior AI/ML Software Engineering Leader, Cisco

Samantha Siau

Samantha Siau

Security and Compliance, Anthropic

Kevin Simmonds

Kevin Simmonds

Partner on AI Offensive Security, PWC

Martin Stanley

Martin Stanley

NIST AI RMF Lead, Independent

Omar A. Turner

Omar A. Turner

General Manager of Security, Microsoft

Apostol Vassilev

Apostol Vassilev

AI Research Team Supervisor, NIST

Matthew Versaggi

Matthew Versaggi

AI Fellow, White House Presidential Innovation Fellow

David Webb

David Webb

Agency Cybersecurity Officer, Cybersecurity and Infrastructure Security Agency

Dennis Xu

Dennis Xu

Research VP, AI, Gartner

Xiaochen Zhang

Xiaochen Zhang

Executive Director and Chief Responsible AI Officer, AI 2030

Recognition

We extend our gratitude to all founding members who have contributed to establishing this crucial framework for AI security assessment. Their vision and dedication have been instrumental in shaping the Agentic Skills Top 10 project.

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