Patch Validation

Neo performs comprehensive patch validation by re-testing fixed vulnerabilities, actively attempting to bypass applied patches, and analyzing code changes for new security issues. It executes original PoCs, generates bypass variants, performs differential analysis on code changes, and validates that patches are both effective and complete without introducing new attack surfaces.

How it works

Core Validation Workflow

Identify fixes: Neo retrieves closed or resolved tickets from Jira, Linear, or GitHub, correlating them with merged PRs and deployed commits
Retrieve context: Pulls original PoCs, reproduction steps, exploit payloads, and testing history from Files and Memory
Code differential analysis: Fetches git diffs for the patch, analyzing changed lines, modified functions, and new input validation logic
Re-test original exploit: Executes the exact original PoC against the patched system to confirm the specific vulnerability is fixed
Patch bypass testing: Actively attempts to circumvent the patch using variant payloads, encoding schemes, and logic manipulation
Code change vulnerability analysis: Analyzes the patch diff for new vulnerabilities introduced by the fix (e.g., incomplete validation, new code paths, logic errors)
Regression testing: Runs broader security test suites to detect unintended side effects or broken defenses
Report and track: Updates tickets with detailed validation results, bypass attempts, code analysis findings, and reopens issues if the patch is incomplete or bypassable

Patch Bypass Testing Methodology

Neo systematically attempts to bypass patches by:

Payload mutation: Generating variants of the original exploit using different encodings, case variations, and character substitutions
Logic boundary testing: Testing edge cases and boundary conditions not covered by the patch
Alternative attack vectors: Identifying similar endpoints, parameters, or code paths that might exhibit the same vulnerability
Race condition exploitation: Testing if the patch can be bypassed through timing attacks or concurrent requests
Type confusion: Attempting to trigger the vulnerability through unexpected input types or data structures
Context switching: Testing if the vulnerability persists in different application contexts or privilege levels

Code Change Analysis

For each patch, Neo performs:

Diff parsing: Extracts modified functions, added validation logic, and changed control flows from git diffs
Validation completeness check: Verifies that input validation is applied consistently across all entry points
Whitelisting vs blacklisting: Identifies if patches use insecure blacklist-based filtering instead of allowlist approaches
Defense in depth evaluation: Checks if the patch relies on a single point of failure or includes layered defenses
Error handling review: Analyzes if new error paths leak information or create denial-of-service conditions
Dependency impact: Examines if the patch modifies shared code that could affect other components

Prerequisites

Required Artifacts

Issue tracking: Closed or resolved vulnerability tickets in Jira, Linear, or GitHub with linked PR references
Original exploit data: PoCs, reproduction steps, exploit payloads, and request/response traces stored in Files
Code repository access: Git repository access for fetching diffs, commits, and file history
Environment access: Access to patched environments (staging, pre-production, or production) with appropriate testing permissions

Required Integrations

Version control: GitHub, GitLab, or Bitbucket integration for diff retrieval and commit correlation
CI/CD pipeline: Integration with deployment pipelines to identify when patches are deployed
Testing infrastructure: Sandboxed environments or Nuclei integration for safe exploit re-execution
Monitoring: Observability tools to detect patch validation failures and regression alerts

Usage

Basic Validation

Re-test all vulnerabilities marked as 'fixed' in the last sprint. For each patch:
Execute the original PoC
Attempt 5 bypass variants per vulnerability class
Analyze code diffs for new vulnerabilities
Update tickets with detailed validation results

Neo’s execution workflow:

Retrieval phase: Fetches closed tickets, linked PRs, git diffs, and stored PoCs from Files
Original exploit validation: Executes exact PoCs against patched endpoints to confirm the specific issue is addressed
Bypass generation: Creates payload variants based on vulnerability class (XSS: encoding mutations, SQLi: comment injection, SSRF: scheme variations)
Bypass execution: Systematically tests each variant against the patched system
Diff analysis: Parses git diffs to identify validation logic, new code paths, and potential logic flaws
Vulnerability pattern matching: Scans changed code for common anti-patterns (incomplete regex, missing length checks, time-of-check-time-of-use issues)
Regression testing: Runs security test suites against endpoints affected by the patch
Evidence collection: Captures HTTP traces, stack traces, and validation logs for all tests
Ticket updates: Posts detailed results including bypass attempt outcomes, diff analysis findings, and pass/fail status

Advanced Bypass Testing

For ticket SEC-789 (XSS fix in user profile), perform comprehensive bypass testing:
- Test 20+ XSS payload variants (encoding, event handlers, protocol handlers)
- Check if validation is applied to all input vectors (GET, POST, headers, JSON)
- Test HTML context escaping in different rendering contexts
- Verify CSP headers are set correctly
- Attempt DOM-based XSS through secondary sinks
- Document all bypass attempts with HTTP traces

Code Change Security Analysis

Analyze the security fix in PR #3421 for completeness:
- Extract all modified functions and validation logic
- Identify if patch uses blocklist (bad) or allowlist (good)
- Check if validation is applied at all entry points
- Verify error handling doesn't leak sensitive information
- Analyze if new code paths introduce vulnerabilities
- Test edge cases in newly added conditionals
- Verify the patch doesn't break existing security controls

Examples

Post-Release Validation

Validate all security fixes from v2.5 release:
- Retrieve all tickets tagged 'security-fix' closed between June 1-15
- For each CVE patch, execute original PoC and 15+ bypass variants
- Analyze all associated code diffs for introduced vulnerabilities
- Test cross-component impacts (shared libraries, dependencies)
- Verify fixes work across all deployment environments (staging, canary, prod)
- Generate compliance report with CVSS scores, patch effectiveness, and bypass test results

Continuous Regression Testing

Weekly regression validation workflow:
- Re-test all critical/high severity fixes from past 90 days
- For each vulnerability class, attempt class-specific bypass techniques
- Monitor for patch degradation due to subsequent code changes
- Alert security team if any previously fixed vulnerability reappears
- Track patch durability metrics (% of fixes that remain effective over time)
- Auto-reopen tickets if exploits succeed with detailed failure analysis

Deep Single-Issue Verification

Validate SQL injection fix in SEC-456 (user search endpoint):
- Execute original UNION-based payload from Files
- Test 25+ SQLi variants: blind, time-based, stacked queries, second-order
- Analyze PR #2341 diff: verify parameterized queries vs string concatenation
- Check if fix covers all database call sites in the module
- Test similar endpoints for variant vulnerabilities
- Verify ORM usage and input sanitization consistency
- Test boundary conditions: empty strings, null values, max-length inputs
- Attempt polyglot payloads combining SQLi with XSS
- Document: 27 bypass attempts, all blocked; patch uses prepared statements; no new vulns detected

Pre-Production Deployment Gate

Pre-production validation for release v3.1 (deployment gate):
- Scan commits since v3.0 for all security-related changes
- Identify 12 closed security tickets in this release
- Execute validation pipeline:
  * Original PoC re-execution: 12/12 blocked ✓
  * Bypass variant testing: 156 attempts, 0 successful ✓
  * Code diff analysis: 3 potential issues detected ⚠️
  * Regression suite: 248 tests, 2 failures ✗
- BLOCK DEPLOYMENT: 2 test failures indicate broken CORS policy
- Alert: New code in auth middleware bypasses rate limiting
- Recommendation: Revert commit a4f3c2d, re-test before deployment

Vulnerability Class Pattern Analysis

Analyze patch patterns for all XSS fixes in Q2 2024:
- Retrieve 23 XSS fixes from past quarter
- Test each with 30+ encoding and context-specific payloads
- Pattern detected: 4/23 fixes use client-side validation only (vulnerable)
- Common bypass: Unicode normalization not handled in 8/23 fixes
- Recommendation: Implement server-side HTML entity encoding across all endpoints
- Auto-generate regression test suite for future XSS fixes
- Update Memory with bypass patterns for improved future testing

CI/CD Integration

Configure Neo in GitHub Actions:
- Trigger on PR merge to main with label 'security-fix'
- Workflow:
  1. Extract vulnerability ID from PR description
  2. Retrieve original PoC from Files using ticket ID
  3. Wait for deployment to staging environment
  4. Execute PoC and bypass variants against staging
  5. Analyze PR diff for validation logic completeness
  6. Post validation results as PR comment
  7. Block production promotion if bypass successful
  8. Update Jira ticket with validation evidence
- Success criteria: Original exploit blocked AND no bypasses found AND no new vulns in diff

Common Patch Anti-Patterns and Bypass Techniques

Neo automatically detects and tests for these common patch vulnerabilities:

Incomplete Input Validation

Blacklist-Based Filtering

Anti-pattern: Patches that block specific malicious patterns instead of allowing only safe inputs

# Vulnerable patch - blacklist approach
def sanitize_input(user_input):
    if '<script>' in user_input.lower():
        return ''
    return user_input

Bypass techniques Neo tests:

Case variations: <ScRiPt>, <SCRIPT>
Encoding: %3Cscript%3E, \u003cscript\u003e
Alternative tags: <img>, <svg>, <iframe>
Event handlers: <div onload=alert(1)>
Protocol handlers: javascript:alert(1)

Recommended fix: Allowlist HTML entities or use context-aware output encoding

Client-Side Only Validation

Anti-pattern: Security validation performed only in JavaScript/frontend

// Vulnerable patch - client-side validation only
function submitForm() {
    if (validateInput(userInput)) {
        fetch('/api/submit', { body: userInput })
    }
}

Bypass techniques Neo tests:

Direct API calls bypassing frontend (curl, Postman, Burp)
Browser dev tools to modify validation logic
Intercepting and modifying requests before submission

Recommended fix: Always validate on server-side; client-side is UX only

Incomplete Entry Point Coverage

Anti-pattern: Validation applied to some input vectors but not all

# Vulnerable patch - validates GET but not POST/headers
@app.route('/search')
def search():
    query = request.args.get('q')  # Validated
    user_agent = request.headers.get('User-Agent')  # Not validated
    return render_template('results.html', query=query, ua=user_agent)

Bypass techniques Neo tests:

Testing same payload via POST, PUT, PATCH, headers, cookies, JSON body
Exploiting unvalidated secondary inputs
Parameter pollution attacks

Recommended fix: Apply validation to all input sources consistently

Logic and Timing Vulnerabilities

Time-of-Check Time-of-Use (TOCTOU)

Anti-pattern: Race condition between validation and use

# Vulnerable patch - TOCTOU race condition
def process_file(filename):
    if is_safe_path(filename):  # Check
        time.sleep(0.1)  # Delay
        with open(filename, 'r') as f:  # Use
            return f.read()

Bypass techniques Neo tests:

Racing validation with symlink swapping
Concurrent requests to exploit timing window
Filesystem race conditions

Recommended fix: Validate and use in atomic operation; use file descriptors not paths

Partial State Updates

Anti-pattern: Patch updates one part of state but not synchronized copies

# Vulnerable patch - updates session but not cache
def change_password(user_id, new_password):
    db.update_password(user_id, new_password)  # Updated
    # Cache still has old password hash

Bypass techniques Neo tests:

Authenticating with old credentials against cached data
Exploiting desynchronized state across microservices
Using stale tokens or session data

Recommended fix: Update all state atomically or invalidate caches

Cryptographic and Authentication Flaws

Weak Cryptographic Fixes

Anti-pattern: Insufficient cryptographic strength or improper implementation

# Vulnerable patch - weak random token generation
import random
def generate_reset_token():
    return str(random.randint(100000, 999999))  # Predictable

Bypass techniques Neo tests:

Token prediction attacks
Brute forcing short tokens
Seed prediction for weak RNGs

Recommended fix: Use secrets module, minimum 32 bytes entropy, CSPRNG

Authentication Bypass Through Logic Flaws

Anti-pattern: Broken authentication logic in patches

# Vulnerable patch - logic error
def verify_login(username, password):
    user = db.get_user(username)
    if user and check_password(password, user.password_hash):
        return True
    if username == 'admin':  # Logic flaw
        return True
    return False

Bypass techniques Neo tests:

Testing special usernames (‘admin’, ‘root’, ‘test’)
Null/empty password attempts
SQL injection in username field
Type confusion (array vs string)

Recommended fix: Single authentication path, no special cases, comprehensive testing

Code-Level Vulnerability Introduction

Introduced Information Disclosure

Anti-pattern: New error handling leaks sensitive data

# Vulnerable patch - added error logging exposes data
def process_payment(card_number):
    try:
        charge_card(card_number)
    except Exception as e:
        log.error(f"Payment failed for card {card_number}: {e}")  # Leaks PAN
        return {"error": str(e)}  # Leaks internals

Bypass techniques Neo tests:

Triggering error conditions to extract stack traces
Analyzing error messages for path disclosure
Timing attacks on error vs success responses

Recommended fix: Generic error messages, sanitized logging, no PII in errors

New Injection Points

Anti-pattern: Patch introduces new unsafe data flows

# Vulnerable patch - added logging creates command injection
def authenticate(username):
    if validate_user(username):
        os.system(f"logger 'User {username} logged in'")  # New injection
        return True

Bypass techniques Neo tests:

Command injection via username: user; rm -rf /
Log injection: user\nroot:authenticated
Format string attacks

Recommended fix: Never pass user input to shell commands; use parameterized logging

Next steps

Store PoCs, exploit payloads, and reproduction steps to Files for reuse across validation cycles
Configure Memory with vulnerability-class-specific bypass testing strategies
Integrate Neo into CI/CD pipelines to validate patches before production deployment
Set up automated ticket management to reopen issues when bypasses succeed

COMMON WORKFLOWS

​How it works

​Core Validation Workflow

​Patch Bypass Testing Methodology

​Code Change Analysis

​Prerequisites

​Required Artifacts

​Required Integrations

​Usage

​Basic Validation

​Advanced Bypass Testing

​Code Change Security Analysis

​Examples

​Post-Release Validation

​Continuous Regression Testing

​Deep Single-Issue Verification

​Pre-Production Deployment Gate

​Vulnerability Class Pattern Analysis

​CI/CD Integration

​Common Patch Anti-Patterns and Bypass Techniques

​Incomplete Input Validation

​Logic and Timing Vulnerabilities

​Cryptographic and Authentication Flaws

​Code-Level Vulnerability Introduction

​Next steps

How it works

Core Validation Workflow

Patch Bypass Testing Methodology

Code Change Analysis

Prerequisites

Required Artifacts

Required Integrations

Usage

Basic Validation

Advanced Bypass Testing

Code Change Security Analysis

Examples

Post-Release Validation

Continuous Regression Testing

Deep Single-Issue Verification

Pre-Production Deployment Gate

Vulnerability Class Pattern Analysis

CI/CD Integration

Common Patch Anti-Patterns and Bypass Techniques

Incomplete Input Validation

Logic and Timing Vulnerabilities

Cryptographic and Authentication Flaws

Code-Level Vulnerability Introduction

Next steps