vulnerability-scanner

Vulnerability Scanner

Think like an attacker, defend like an expert. 2025 threat landscape awareness.

🔧 Runtime Scripts

Execute for automated validation:

Script Purpose Usage scripts/security_scan.py Validate security principles applied python scripts/security_scan.py 📋 Reference Files File Purpose checklists.md OWASP Top 10, Auth, API, Data protection checklists 1. Security Expert Mindset Core Principles Principle Application Assume Breach Design as if attacker already inside Zero Trust Never trust, always verify Defense in Depth Multiple layers, no single point Least Privilege Minimum required access only Fail Secure On error, deny access Threat Modeling Questions

Before scanning, ask:

What are we protecting? (Assets) Who would attack? (Threat actors) How would they attack? (Attack vectors) What's the impact? (Business risk) 2. OWASP Top 10:2025 Risk Categories Rank Category Think About A01 Broken Access Control Who can access what? IDOR, SSRF A02 Security Misconfiguration Defaults, headers, exposed services A03 Software Supply Chain 🆕 Dependencies, CI/CD, build integrity A04 Cryptographic Failures Weak crypto, exposed secrets A05 Injection User input → system commands A06 Insecure Design Flawed architecture A07 Authentication Failures Session, credential management A08 Integrity Failures Unsigned updates, tampered data A09 Logging & Alerting Blind spots, no monitoring A10 Exceptional Conditions 🆕 Error handling, fail-open states 2025 Key Changes 2021 → 2025 Shifts: ├── SSRF merged into A01 (Access Control) ├── A02 elevated (Cloud/Container configs) ├── A03 NEW: Supply Chain (major focus) ├── A10 NEW: Exceptional Conditions └── Focus shift: Root causes > Symptoms

1. Supply Chain Security (A03) Attack Surface Vector Risk Question to Ask Dependencies Malicious packages Do we audit new deps? Lock files Integrity attacks Are they committed? Build pipeline CI/CD compromise Who can modify? Registry Typosquatting Verified sources? Defense Principles Verify package integrity (checksums) Pin versions, audit updates Use private registries for critical deps Sign and verify artifacts
2. Attack Surface Mapping What to Map Category Elements Entry Points APIs, forms, file uploads Data Flows Input → Process → Output Trust Boundaries Where auth/authz checked Assets Secrets, PII, business data Prioritization Matrix Risk = Likelihood × Impact

High Impact + High Likelihood → CRITICAL High Impact + Low Likelihood → HIGH Low Impact + High Likelihood → MEDIUM Low Impact + Low Likelihood → LOW

1. Risk Prioritization CVSS + Context Factor Weight Question CVSS Score Base severity How severe is the vuln? EPSS Score Exploit likelihood Is it being exploited? Asset Value Business context What's at risk? Exposure Attack surface Internet-facing? Prioritization Decision Tree Is it actively exploited (EPSS >0.5)? ├── YES → CRITICAL: Immediate action └── NO → Check CVSS ├── CVSS ≥9.0 → HIGH ├── CVSS 7.0-8.9 → Consider asset value └── CVSS <7.0 → Schedule for later

2. Exceptional Conditions (A10 - New) Fail-Open vs Fail-Closed Scenario Fail-Open (BAD) Fail-Closed (GOOD) Auth error Allow access Deny access Parsing fails Accept input Reject input Timeout Retry forever Limit + abort What to Check Exception handlers that catch-all and ignore Missing error handling on security operations Race conditions in auth/authz Resource exhaustion scenarios

3. Scanning Methodology Phase-Based Approach

4. RECONNAISSANCE └── Understand the target ├── Technology stack ├── Entry points └── Data flows

5. DISCOVERY └── Identify potential issues ├── Configuration review ├── Dependency analysis └── Code pattern search

6. ANALYSIS └── Validate and prioritize ├── False positive elimination ├── Risk scoring └── Attack chain mapping

7. REPORTING └── Actionable findings ├── Clear reproduction steps ├── Business impact └── Remediation guidance

8. Code Pattern Analysis High-Risk Patterns Pattern Risk Look For String concat in queries Injection "SELECT * FROM " + user_input Dynamic code execution RCE eval(), exec(), Function() Unsafe deserialization RCE pickle.loads(), unserialize() Path manipulation Traversal User input in file paths Disabled security Various verify=False, --insecure Secret Patterns Type Indicators API Keys api_key, apikey, high entropy Tokens token, bearer, jwt Credentials password, secret, key Cloud AWS_, AZURE_, GCP_ prefixes

9. Cloud Security Considerations Shared Responsibility Layer You Own Provider Owns Data ✅ ❌ Application ✅ ❌ OS/Runtime Depends Depends Infrastructure ❌ ✅ Cloud-Specific Checks IAM: Least privilege applied? Storage: Public buckets? Network: Security groups tightened? Secrets: Using secrets manager?
10. Anti-Patterns ❌ Don't ✅ Do Scan without understanding Map attack surface first Alert on every CVE Prioritize by exploitability + asset Ignore false positives Maintain verified baseline Fix symptoms only Address root causes Scan once before deploy Continuous scanning Trust third-party deps blindly Verify integrity, audit code
11. Reporting Principles Finding Structure

Each finding should answer:

What? - Clear vulnerability description Where? - Exact location (file, line, endpoint) Why? - Root cause explanation Impact? - Business consequence How to fix? - Specific remediation Severity Classification Severity Criteria Critical RCE, auth bypass, mass data exposure High Data exposure, privilege escalation Medium Limited scope, requires conditions Low Informational, best practice

Remember: Vulnerability scanning finds issues. Expert thinking prioritizes what matters. Always ask: "What would an attacker do with this?"