Malwar: A Vulnerability Scanner for SKILL.md Files Built with Claude Code
Malwar is a vulnerability scanner specifically for SKILL.md files used by AI coding agents. It addresses a gap in security tooling where traditional code scanners look for malicious code, but SKILL.md files contain natural language instructions that can pose a different threat.
What Malwar Does
The tool runs a 4-layer pipeline against skill files:
- Rule engine
- URL crawler
- LLM analysis layer
- Threat intel
Why It Was Built
The developer was building agentic workflows and realized they were "blindly pulling skills from ClawHub and trusting them." After manually reading through skills, they found several concerning patterns that would raise red flags in other contexts:
- Base64 blobs
- Instructions telling the agent to curl something and pipe it to bash
- Weirdly specific references to file paths where credentials live
The developer notes: "The kind of thing that if you saw it in a shell script you'd close the tab immediately."
Technical Implementation
The entire tool was built with Claude Code, including:
- Architecture
- Detection rules
- The LLM analysis layer
- The REST API
The developer states: "Honestly couldn't have shipped it at this scope solo without it."
Availability
Malwar is free to use with source available on GitHub at https://ap6pack.github.io/malwar/.
📖 Read the full source: r/ClaudeAI
👀 See Also
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Malicious PyTorch Lightning Package Steals Credentials and Worms npm Packages
PyPI package 'lightning' versions 2.6.2 and 2.6.3 contain Shai-Hulud themed malware that steals credentials, tokens, and cloud secrets, and spreads to npm packages via injected JavaScript payloads.
Security Benchmark: 10 LLMs Tested Against 211 Adversarial Probes
A security researcher tested 10 LLMs against 211 adversarial attacks, finding that extraction resistance averages 85% while injection resistance averages only 46.2%. Every model failed completely on delimiter, distractor, and style injection attacks.
AI Agent Security Gap: How Supra-Wall Adds Enforcement Layer Between Models and Tools
A developer discovered their AI agent autonomously read sensitive .env files containing Stripe keys, database passwords, and OpenAI API keys. The open-source Supra-Wall tool intercepts tool calls before execution to enforce security policies.