Security Auditor Guide
SkillDatabases & dataThis skill lets your AI audit the security of code, configurations, and infrastructure. Once added, it can uncover common vulnerabilities, exposed secrets, and known weaknesses in your dependencies before they become a problem.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding the skill, ask your AI to audit a codebase, file, or configuration for security issues. It will report the vulnerabilities, secrets, and risky dependencies it finds.
Then ask your AI: use the Security Auditor Guide skill
What your AI can do with it
- Scan code for vulnerabilities such as SQL injection, XSS, command injection, and path traversal
- Detect hardcoded secrets and credentials left in files
- Review how authentication and authorization are set up
- Check dependencies for known CVEs
- Audit configurations and infrastructure for security gaps
What this skill tells your AI
The instructions your AI receives, as published by eigent-ai/eigent in resources/example-skills/skill-security-auditor/SKILL.md and read by ahel’s review.
Overview
This guide covers security auditing workflows for source code, dependencies, and configurations. For detailed vulnerability patterns and detection rules, see references/vulnerability-patterns.md. For secrets detection patterns, see references/secrets-patterns.md.
Quick Start
Run the bundled scan script against a project directory:
python scripts/scan_project.py /path/to/project
This performs a lightweight scan for common issues: hardcoded secrets, dangerous function calls, and insecure patterns. For deeper analysis, follow the workflows below.
Testing the scripts
python scripts/scan_project.py /path/to/some/project --format text
python scripts/scan_secrets.py /path/to/some/project --format text
Audit Workflow
1. Reconnaissance
Before auditing, understand the project:
# Identify languages, frameworks, and entry points
find . -type f -name "*.py" -o -name "*.js" -o -name "*.ts" -o -name "*.go" -o -name "*.java" | head -20
cat package.json pyproject.toml requirements.txt go.mod pom.xml 2>/dev/null
Key questions:
- What frameworks are used? (Express, Django, Flask, Spring, etc.)
- Where are the entry points? (routes, controllers, API handlers)
- How is authentication handled?
- What external services are called?
- Is user input accepted? Where?
2. Secrets Detection
Scan for hardcoded credentials, API keys, and tokens. See references/secrets-patterns.md for the full pattern list.
python scripts/scan_secrets.py /path/to/project
Common patterns to check:
- API keys and tokens in source files
- Database connection strings with embedded passwords
- Private keys or certificates committed to the repo
.envfiles or config files with plaintext secrets- Secrets in CI/CD configuration files
3. Vulnerability Scanning
OWASP Top 10 Checklist
| # | Category | What to Look For |
|---|---|---|
| A01 | Broken Access Control | Missing auth checks, IDOR, privilege escalation |
| A02 | Cryptographic Failures | Weak algorithms, plaintext storage, missing TLS |
| A03 | Injection | SQL, NoSQL, OS command, LDAP, XSS |
| A04 | Insecure Design | Missing rate limits, business logic flaws |
| A05 | Security Misconfiguration | Debug mode, default credentials, verbose errors |
| A06 | Vulnerable Components | Outdated dependencies with known CVEs |
| A07 | Auth Failures | Weak passwords, missing MFA, session issues |
| A08 | Data Integrity Failures | Insecure deserialization, unsigned updates |
| A09 | Logging Failures | Missing audit logs, sensitive data in logs |
| A10 | SSRF | Unvalidated URLs in server-side requests |
Language-Specific Checks
Python
# Dangerous: SQL injection
cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
# Safe: Parameterized query
cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))
# Dangerous: Command injection
os.system(f"ping {hostname}")
# Safe: Use subprocess with list args
subprocess.run(["ping", hostname], capture_output=True)
# Dangerous: Path traversal
open(f"/data/{user_input}")
# Safe: Validate and resolve path
path = pathlib.Path("/data") / user_input
path.resolve().relative_to(pathlib.Path("/data").resolve())
JavaScript/TypeScript
// Dangerous: XSS via innerHTML
element.innerHTML = userInput;
// Safe: Use textContent or sanitize
element.textContent = userInput;
// Dangerous: Prototype pollution
Object.assign(target, JSON.parse(userInput));
// Safe: Validate input structure
const parsed = JSON.parse(userInput);
if (typeof parsed !== 'object' || Array.isArray(parsed)) throw new Error();
const sanitized = Object.fromEntries(
Object.entries(parsed).filter(([k]) => !k.startsWith('__'))
);
// Dangerous: eval or Function constructor
eval(userInput);
// Safe: Never use eval with user input
Go
// Dangerous: SQL injection
db.Query("SELECT * FROM users WHERE id = " + id)
// Safe: Parameterized query
db.Query("SELECT * FROM users WHERE id = $1", id)
// Dangerous: Path traversal
http.ServeFile(w, r, filepath.Join(baseDir, r.URL.Path))
// Safe: Clean and validate path
cleaned := filepath.Clean(r.URL.Path)
full := filepath.Join(baseDir, cleaned)
if !strings.HasPrefix(full, baseDir) { http.Error(...) }
4. Dependency Audit
Check for known vulnerabilities in project dependencies:
# Python
pip audit
safety check -r requirements.txt
# Node.js
npm audit
npx auditjs ossi
# Go
govulncheck ./...
# General (if Trivy is available)
trivy fs --scanners vuln /path/to/project
Review the output and categorize by severity (critical, high, medium, low). Critical and high severity findings should be addressed before deployment.
5. Configuration Review
Check for insecure defaults in configuration files:
# Common misconfigurations to flag:
DEBUG: true # Debug mode in production
ALLOWED_HOSTS: ["*"] # Unrestricted host access
CORS_ALLOW_ALL_ORIGINS: true # Open CORS policy
SECRET_KEY: "default" # Default or weak secret key
SSL_VERIFY: false # Disabled TLS verification
Check infrastructure configs:
- Dockerfiles: Running as root, exposing unnecessary ports
- CI/CD: Secrets in plaintext, overly permissive permissions
- Cloud configs: Public S3 buckets, open security groups
6. Authentication and Authorization Review
Key areas to verify:
- Password hashing uses strong algorithms (bcrypt, argon2, scrypt)
- Sessions have appropriate timeouts and rotation
- JWT tokens are validated properly (algorithm, expiry, signature)
- API endpoints enforce authorization checks
- Role-based access control is consistently applied
- Rate limiting is in place for login and sensitive endpoints
Report Format
When generating a security audit report, use this structure:
# Security Audit Report
## Summary
- **Project**: [name]
- **Date**: [date]
- **Scope**: [what was audited]
- **Risk Level**: [Critical/High/Medium/Low]
## Findings
### [SEVERITY] Finding Title
- **Category**: [OWASP category]
- **Location**: [file:line]
- **Description**: [what the issue is]
- **Impact**: [what could happen if exploited]
- **Recommendation**: [how to fix]
## Statistics
- Total findings: [count]
- Critical: [count] | High: [count] | Medium: [count] | Low: [count]
Next Steps
- For detailed vulnerability patterns and code examples, see references/vulnerability-patterns.md
- For secrets detection regex patterns, see references/secrets-patterns.md
Signals
- GitHub stars
- 15k
- Forks
- 2k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
skill-security-auditor-eigent-ai- Source
- github.com/eigent-ai/eigent