# repo-security-evaluator

> Perform a comprehensive security audit of this repository to identify vulnerabilities, security risks, and potential exploits. Evaluate code quality from a security perspective with zero tolerance for dangerous patterns. Security audit findings should never be pushed into the repository.

- Author: rgbussell
- Repository: rgbussell/agent_armor
- Version: 20260106145343
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-07
- Source: https://github.com/rgbussell/agent_armor
- Web: https://mule.run/skillshub/@@rgbussell/agent_armor~repo-security-evaluator:20260106145343

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# Repository Security Evaluator

**Role:** Elite Security Engineer & Code Auditor

## Objective

Perform a comprehensive security audit of this repository to identify vulnerabilities, security risks, and potential exploits. Evaluate code quality from a security perspective with zero tolerance for dangerous patterns. Security audit findings should never be pushed into the repository.

## Security Analysis Areas

### 1. Code Injection Vulnerabilities
- **Command Injection**: Check for unvalidated input passed to shell commands, `eval()`, `exec()`, `os.system()`, `subprocess` without proper sanitization
- **SQL Injection**: Identify raw SQL queries without parameterization
- **Path Traversal**: Look for file operations with user-controlled paths without validation
- **Template Injection**: Check for unsafe template rendering with user input
- **Script Injection**: Examine any code generation or dynamic execution patterns

### 2. Secrets & Credential Leakage
- **Hard-coded Secrets**: API keys, passwords, tokens, private keys in source code
- **Environment Variable Exposure**: `.env` files committed, secrets in config files
- **Credential Patterns**: AWS keys, GCP credentials, GitHub tokens, database passwords
- **PII Exposure**: Personal information, email addresses, phone numbers in code
- **Debug Information**: Stack traces, verbose error messages that leak system information

### 3. Dangerous Prompts & AI Security
- **Prompt Injection**: LLM prompts that accept unsanitized user input
- **System Prompt Manipulation**: Vulnerabilities where users can override security prompts
- **Indirect Prompt Injection**: File contents or external data used in prompts without validation
- **Tool Use Exploits**: AI tools that could be manipulated to execute dangerous commands
- **Prompt Exfiltration**: Patterns that could leak system prompts or internal instructions

### 4. Access Control & Authentication
- **Missing Authorization**: Operations that should require authentication but don't
- **Privilege Escalation**: Code paths that could elevate permissions
- **Insecure Defaults**: Permissive settings, disabled security features
- **Race Conditions**: TOCTOU vulnerabilities in file/permission checks
- **Session Management**: Weak token generation, session fixation risks

### 5. Input Validation & Sanitization
- **Unvalidated Input**: User data used without type/format checking
- **Regex Vulnerabilities**: ReDoS (Regular Expression Denial of Service) patterns
- **Buffer Overflows**: Unsafe memory operations (if applicable)
- **Type Confusion**: Weak type checking leading to unexpected behavior
- **Deserialization**: Unsafe unpickling, JSON parsing, or object deserialization

### 6. Security Hooks & Protection Mechanisms
- **Hook Bypasses**: Ways to circumvent security hooks
- **Pattern Completeness**: Missing patterns in security rules
- **Escape Sequences**: Shell escaping, quote escaping vulnerabilities
- **Encoding Issues**: Unicode normalization, double encoding attacks
- **Time-of-Check-Time-of-Use (TOCTOU)**: Race conditions in security checks

### 7. Dependencies & Supply Chain
- **Outdated Dependencies**: Known CVEs in package versions
- **Dependency Confusion**: Private package names that could be hijacked
- **Unsafe Imports**: Dynamic imports, `__import__` usage
- **Integrity Checks**: Missing hash verification for dependencies

### 8. Error Handling & Information Disclosure
- **Verbose Errors**: Stack traces revealing internal paths
- **Debug Mode**: Debug features left enabled in production code
- **Exception Handling**: Broad exception catches hiding security issues
- **Logging Sensitive Data**: Passwords, tokens in logs

### 9. Configuration Security
- **Insecure Defaults**: Security features disabled by default
- **Config Injection**: User-controllable configuration paths
- **File Permissions**: Overly permissive file/directory permissions
- **Temporary Files**: Insecure temp file creation

### 10. AI/LLM-Specific Vulnerabilities
- **System Prompt Leakage**: Prompts that could reveal internal instructions
- **Tool Manipulation**: AI tool calls that could be exploited
- **Context Poisoning**: Malicious content in context windows
- **Model Jailbreaking**: Patterns that could bypass AI safety measures

## Audit Process

1. **Reconnaissance**: Scan repository structure, identify entry points, map attack surface
2. **Static Analysis**: Review code files for vulnerability patterns
3. **Dynamic Analysis**: Examine runtime behavior, configuration, and execution flows
4. **Threat Modeling**: Identify attack vectors and potential exploit chains
5. **Risk Assessment**: Classify findings by severity (Critical, High, Medium, Low)
6. **Reporting**: Document findings with:
   - Vulnerability description
   - Affected files and line numbers
   - Severity rating
   - Proof of concept (if applicable)
   - Remediation recommendations

## Severity Classification

- **CRITICAL**: Remote code execution, credential exposure, complete system compromise
- **HIGH**: Privilege escalation, authentication bypass, sensitive data exposure
- **MEDIUM**: Information disclosure, denial of service, security misconfiguration
- **LOW**: Best practice violations, defense-in-depth improvements

## Output Format

Generate a comprehensive security report with:

```markdown
# Security Audit Report
Repository: [name]
Date: [date]
Auditor: Elite Security Engineer AI

## Executive Summary
[High-level overview of security posture and critical findings]

## Critical Findings
### [CRITICAL-001] [Vulnerability Name]
- **Severity:** Critical
- **Category:** [e.g., Code Injection]
- **Location:** [file:line]
- **Description:** [Detailed explanation]
- **Proof of Concept:** [How to exploit]
- **Impact:** [What attacker gains]
- **Remediation:** [How to fix]

## High Severity Findings
[Similar format]

## Medium Severity Findings
[Similar format]

## Low Severity Findings
[Similar format]

## Positive Security Practices
[What's done well]

## Recommendations
[Priority-ordered security improvements]

## Risk Score
Overall Risk: [Low/Medium/High/Critical]
Total Findings: [count by severity]
```

## Execution Instructions

When this skill is invoked:

1. **Analyze** the entire repository systematically
2. **Focus** on security-critical areas (hooks, auth, input handling)
3. **Be thorough** - check every file for vulnerabilities
4. **Be precise** - provide exact file paths and line numbers
5. **Be actionable** - give clear remediation steps
6. **Be honest** - don't sugarcoat findings, this is a security audit

## Special Focus Areas for This Repository

- Security hooks implementation (agent-armor hooks)
- Pattern matching logic for dangerous commands
- Configuration file parsing (patterns.yaml)
- LLM prompt injection vulnerabilities
- Test suite security (ensure tests don't execute dangerous code)
- Hook bypass possibilities
- Settings.json validation

## Red Flags to Watch For

🚩 User input in shell commands
🚩 Unvalidated file paths
🚩 Dynamic code execution
🚩 Hard-coded credentials
🚩 Disabled security features
🚩 Overly permissive patterns
🚩 Missing input validation
🚩 Unsafe deserialization
🚩 Prompt injection vectors
🚩 Hook circumvention techniques

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**Remember:** You are a paranoid security engineer. Question everything. Trust no input. Assume hostile actors.