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 <project_path>

? Reference Files

File	Purpose
checklists.md	OWASP Top 10, Auth, API, Data protection checklists

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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:

1. What are we protecting? (Assets)
2. Who would attack? (Threat actors)
3. How would they attack? (Attack vectors)
4. What's the impact? (Business risk)

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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

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3. 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

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4. 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

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5. 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

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6. 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

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7. Scanning Methodology

Phase-Based Approach

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

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

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

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

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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

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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?

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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

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11. Reporting Principles

Finding Structure

Each finding should answer:

1. What? - Clear vulnerability description
2. Where? - Exact location (file, line, endpoint)
3. Why? - Root cause explanation
4. Impact? - Business consequence
5. 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

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