The next supply chain crisis might not come through a compromised npm package or a malicious PyPI module. It might come through a SKILL.md file.

Researchers published findings in SecurityWeek on May 7, 2026, backed by Snyk’s ToxicSkills report — a scan of 3,984 AI agent skills from registries including ClawHub and skills.sh. The results: 36.8% of scanned skills had security flaws, and 13.4% were rated critical. Seventy-six confirmed malicious skills were identified.

This guide explains how the attack works, what Snyk found, and how to audit your own agent skill dependencies before you’re affected.

How the Attack Works

AI agent frameworks like Claude Code, Gemini CLI, and Cursor use skill files (often named SKILL.md, AGENT.md, or similar) to extend agent capabilities. These files contain instructions the agent follows — essentially adding new “plugins” that tell the agent what tools to use and how to behave.

The attack vector exploits the fact that agents trust these files implicitly. A malicious skill file can:

  1. Include hidden prompt injection instructions embedded in Markdown — invisible or low-visibility to human reviewers but parsed by the agent
  2. Override the agent’s behavior by claiming higher authority than the user’s actual instructions
  3. Exfiltrate data via base64-obfuscated commands that make network requests to attacker-controlled servers
  4. Establish persistence by instructing the agent to modify its own configuration or install additional skills

Snyk named this attack class TrustFall — the agent falls for instructions from a source it shouldn’t trust (a third-party skill file), because the framework doesn’t distinguish between trusted developer-authored instructions and potentially malicious third-party content.

The attack works against Claude Code, Gemini CLI, and Cursor. It’s expected to apply broadly to any agent framework that loads skill files from external registries.

Snyk’s ToxicSkills Report: Key Numbers

  • 3,984 skills scanned across ClawHub and skills.sh registries
  • 36.8% had some form of security flaw
  • 13.4% rated critical severity
  • 76 confirmed malicious skills — including ones designed to exfiltrate credentials and modify agent behavior

Attack techniques observed:

  • Hidden prompt injection in Markdown — using HTML comments, Unicode whitespace, or invisible characters to hide instructions
  • Typosquatting — malicious skills named one character off from popular legitimate ones (e.g., web-serach vs web-search)
  • Base64-obfuscated exfiltration — commands encoded in base64 that the agent decodes and executes, sending data to attacker infrastructure

How to Audit Your Agent Skills

Step 1: Inventory Your Skills

Start by listing every skill file your agent loads. Depending on your framework, these may be in:

  • A skills directory in your agent’s config folder (e.g., ~/.openclaw/skills/)
  • A node_modules equivalent for your agent runtime
  • Configuration files that reference external skill registries

For each skill, note:

  • The source (official vendor skill vs. third-party registry vs. self-authored)
  • The last update date
  • The level of access it requests

Step 2: Visually Inspect SKILL.md Files for Injection

Open each SKILL.md file in a hex editor or a tool that renders invisible characters. Look for:

Red flags in Markdown skill files:

  • HTML comments (<!-- -->) containing instructions — these are hidden from normal rendering but parsed by some agent frameworks
  • Unusually long lines or dense blocks of whitespace
  • Base64-encoded strings (look for long strings of alphanumeric characters with = or == padding)
  • Unicode control characters or zero-width spaces embedded in text
  • Instructions that claim override authority: “Ignore your previous instructions,” “You must follow this,” “This supersedes all other rules”
  • External URL references that aren’t to obvious official documentation

Command to find base64-like strings in a skill file:

# Replace path with your skill file
grep -E '[A-Za-z0-9+/]{40,}={0,2}' ~/.openclaw/skills/your-skill/SKILL.md

This is not conclusive (legitimate content can also be base64), but it flags candidates for closer inspection.

Step 3: Check for Typosquatting

Compare your installed skill names against the official registry. Look for:

  • Skills that are one character off from well-known skills
  • Skills with unusual separators (e.g., web_search vs web-search)
  • Skills that claim to be an “improved” or “fixed” version of an official skill from an unofficial author

Step 4: Use mcp-scan (If Your Framework Supports It)

The mcp-scan tool is designed to audit Model Context Protocol (MCP) servers and skill manifests for known injection patterns. If your agent framework uses MCP, running mcp-scan against your skill manifest can flag known-bad patterns.

# Install and run mcp-scan (refer to official docs for current install command)
# https://github.com/invariantlabs-ai/mcp-scan
npx mcp-scan@latest scan --config ~/.your-agent/config.json

Note: The mcp-scan tool is actively developed; refer to the official repository for the current installation command and supported frameworks.

Step 5: Review Skill Permissions and Scope

For each skill, ask:

  • Does this skill request access to credentials, environment variables, or sensitive files?
  • Does it make outbound network requests? To which domains?
  • Does it request the ability to modify agent configuration or install additional skills?
  • Does the permission scope match what the skill’s stated purpose requires?

A skill that claims to “help you search the web” but requests access to your SSH keys or environment variables is a red flag — regardless of whether it’s in the registry.

Step 6: Pin Skill Versions

Just like pinning npm or pip dependencies, pin your agent skill versions where the framework supports it. This prevents auto-updates from silently introducing new malicious content.

What to Do If You Find Something Suspicious

  1. Remove the skill immediately — do not continue using it until you’ve investigated
  2. Audit recent agent activity — check logs for unusual tool calls, network requests, or data accesses while the suspicious skill was active
  3. Rotate credentials that the agent had access to (API keys, tokens, service account keys)
  4. Report to the registry — if the skill came from a third-party registry like ClawHub or skills.sh, report the finding to the maintainers
  5. Check for persistence — look for modifications to your agent’s configuration files or newly installed skills that you didn’t add

Long-Term: Organizational Practices

  • Only install skills from verified, official sources where possible
  • Self-author skills for sensitive workflows rather than using third-party skill files for tasks that touch credentials or production data
  • Review SKILL.md changes in PRs the same way you review code changes — treat skill files as code, not configuration
  • Set up automated scanning in your CI pipeline if you maintain custom skills for a team

Sources:

  1. SecurityWeek — AI Coding Agents Could Fuel Next Supply Chain Crisis (May 7, 2026)
  2. Snyk ToxicSkills Report — 3,984 skills scanned, 76 confirmed malicious
  3. mcp-scan by Invariant Labs

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260509-2000

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