AI-Powered Security Testing: Finding Vulnerabilities Faster

from ai_security import IntelligentFuzzer
import requests

class TestAIFuzzing:
    def setup_method(self):
        self.fuzzer = IntelligentFuzzer(
            model='vulnerability-predictor-v2',
            learning_enabled=True
        )

    def test_api_input_fuzzing(self):
        """AI-guided fuzzing of API endpoints"""

        target_endpoint = "https://api.example.com/users"

        # AI learns from successful exploits
        fuzzing_results = self.fuzzer.fuzz_endpoint(
            url=target_endpoint,
            method='POST',
            base_payload={
                'username': 'testuser',
                'email': 'test@example.com',
                'password': 'password123'
            },
            iterations=10000,
            mutation_strategy='ai_guided'
        )

        # AI identifies promising attack vectors
        critical_findings = [
            f for f in fuzzing_results.findings
            if f.severity == 'Critical'
        ]

        for finding in critical_findings:
            print(f"Vulnerability: {finding.type}")
            print(f"Payload: {finding.payload}")
            print(f"Response: {finding.response_code}")
            print(f"Evidence: {finding.evidence}")

        # Example findings:
        # - SQL (as discussed in [Security Testing for QA: A Practical Guide](/blog/security-testing-for-qa)) Injection via email field
        # - XSS in username field
        # - Buffer overflow in password
        # - Integer overflow in user_id

        assert len(fuzzing_results.findings) > 0, "No vulnerabilities found"

    def test_smart_mutation_learning(self):
        """AI learns which mutations trigger vulnerabilities"""

        # Traditional fuzzing: random mutations
        # AI fuzzing: learns from crashes and adapts

        initial_payload = {'search': 'test'}

        # AI tracks which mutations cause interesting behavior
        evolution = self.fuzzer.evolve_payloads(
            initial_payload=initial_payload,
            target_url='https://api.example.com/search',
            generations=50,
            population_size=100
        )

        # AI discovers SQL injection pattern
        successful_mutations = evolution.get_successful_patterns()

        assert any("' OR '1'='1" in m for m in successful_mutations)
        assert any("UNION SELECT" in m for m in successful_mutations)

        # AI rates mutation effectiveness
        top_mutation = evolution.ranked_mutations[0]
        assert top_mutation.effectiveness_score > 0.85

from ai_security import MLFuzzer

class TestCoverageGuidedFuzzing:
    def test_intelligent_path_exploration(self):
        """AI maximizes code coverage during fuzzing"""

        fuzzer = MLFuzzer(
            target_binary='./vulnerable_app',
            coverage_tracking=True,
            ml_guidance=True
        )

        # AI predicts which inputs will reach new code paths
        results = fuzzer.run_campaign(
            duration_minutes=30,
            objective='maximize_coverage'
        )

        print(f"Code coverage: {results.coverage_percentage}%")
        print(f"Unique crashes: {results.unique_crashes}")
        print(f"Paths explored: {results.paths_explored}")

        # AI-guided fuzzing finds more unique crashes
        assert results.unique_crashes > 15
        assert results.coverage_percentage > 85

        # AI identifies crash patterns
        for crash_pattern in results.crash_patterns:
            print(f"Pattern: {crash_pattern.signature}")
            print(f"Frequency: {crash_pattern.occurrences}")
            print(f"Severity: {crash_pattern.estimated_severity}")

from ai_security import AIPentester

class TestAutomatedPentest:
    def test_reconnaissance_phase(self):
        """AI performs intelligent reconnaissance"""

        pentester = AIPentester(
            target='https://target-app.example.com',
            scope=['*.example.com'],
            intensity='moderate'
        )

        # AI-driven reconnaissance
        recon_results = pentester.reconnaissance()

        assert recon_results.subdomains_discovered > 0
        assert recon_results.open_ports is not None
        assert recon_results.technologies_detected is not None

        # AI identifies attack surface
        attack_surface = recon_results.analyze_attack_surface()

        print("High-Value Targets:")
        for target in attack_surface.high_value_targets:
            print(f"- {target.url}")
            print(f"  Tech: {target.technology}")
            print(f"  Risk: {target.risk_score}")

    def test_exploitation_phase(self):
        """AI attempts exploitation with learned techniques"""

        pentester = AIPentester(target='https://target-app.example.com')

        # AI tries multiple exploitation techniques
        exploitation_results = pentester.exploit(
            techniques=['sql_injection', 'xss', 'csrf', 'ssrf'],
            max_attempts=1000,
            learning_mode=True
        )

        successful_exploits = [
            e for e in exploitation_results.attempts
            if e.successful
        ]

        for exploit in successful_exploits:
            print(f"Exploit Type: {exploit.type}")
            print(f"Entry Point: {exploit.entry_point}")
            print(f"Payload: {exploit.payload}")
            print(f"Impact: {exploit.impact_assessment}")

            # Generate proof-of-concept
            poc = exploit.generate_poc()
            assert poc.reproducible is True

    def test_lateral_movement(self):
        """AI attempts lateral movement after initial compromise"""

        pentester = AIPentester(target='https://target-app.example.com')

        # Assume initial compromise achieved
        pentester.set_initial_access(
            method='sql_injection',
            access_level='web_application'
        )

        # AI explores lateral movement opportunities
        lateral_results = pentester.explore_lateral_movement(
            max_depth=3,
            stealth_mode=True
        )

        # AI discovers privilege escalation path
        if lateral_results.privilege_escalation_found:
            path = lateral_results.escalation_path
            print("Privilege Escalation Chain:")
            for step in path.steps:
                print(f"{step.from_privilege} -> {step.to_privilege}")
                print(f"Method: {step.method}")
                print(f"Difficulty: {step.difficulty}")

from ai_security import VulnerabilityPredictor

class TestVulnerabilityPrediction:
    def test_predict_sql_injection_risk(self):
        """AI predicts SQL injection vulnerability from code"""

        predictor = VulnerabilityPredictor(
            model='deepcode-security-v3',
            languages=['python', 'javascript', 'java']
        )

        # Analyze code for vulnerabilities
        code_snippet = '''
        def get_user(username):
            query = "SELECT * FROM users WHERE username = '" + username + "'"
            return db.execute(query)
        '''

        prediction = predictor.analyze_code(code_snippet)

        assert prediction.vulnerability_detected is True
        assert prediction.vulnerability_type == 'SQL_INJECTION'
        assert prediction.confidence > 0.90
        assert prediction.line_number == 2

        # AI suggests fix
        suggested_fix = prediction.get_fix_suggestion()
        assert 'parameterized query' in suggested_fix.description
        assert suggested_fix.fixed_code is not None

    def test_detect_authentication_bypass(self):
        """AI identifies authentication logic flaws"""

        predictor = VulnerabilityPredictor()

        code = '''
        function authenticate(username, password) {
            if (username === "admin" || password === "admin123") {
                return true;
            }
            return checkCredentials(username, password);
        }
        '''

        prediction = predictor.analyze_code(code)

        assert prediction.vulnerability_type == 'AUTHENTICATION_BYPASS'
        assert 'logical OR should be AND' in prediction.explanation

    def test_mass_codebase_scanning(self):
        """AI scans entire codebase for vulnerabilities"""

        predictor = VulnerabilityPredictor()

        results = predictor.scan_repository(
            repo_path='/path/to/codebase',
            file_patterns=['**/*.py', '**/*.js', '**/*.java'],
            severity_threshold='medium'
        )

        # AI prioritizes findings
        critical_vulns = results.get_by_severity('critical')
        high_vulns = results.get_by_severity('high')

        print(f"Critical: {len(critical_vulns)}")
        print(f"High: {len(high_vulns)}")

        # AI generates remediation roadmap
        roadmap = results.generate_remediation_plan(
            team_size=5,
            sprint_length_weeks=2
        )

        assert roadmap.estimated_sprints > 0
        assert len(roadmap.prioritized_fixes) > 0

from ai_security import ThreatModeler

class TestThreatModeling:
    def test_generate_threat_model(self):
        """AI generates threat model from architecture"""

        modeler = ThreatModeler()

        # Define system architecture
        architecture = {
            'components': [
                {'name': 'Web App', 'type': 'web_application', 'public': True},
                {'name': 'API Gateway', 'type': 'api', 'public': True},
                {'name': 'Database', 'type': 'database', 'public': False},
                {'name': 'Auth Service', 'type': 'authentication', 'public': False}
            ],
            'data_flows': [
                {'from': 'Web App', 'to': 'API Gateway', 'protocol': 'HTTPS'},
                {'from': 'API Gateway', 'to': 'Auth Service', 'protocol': 'gRPC'},
                {'from': 'API Gateway', 'to': 'Database', 'protocol': 'TCP'}
            ]
        }

        # AI generates threat model using STRIDE
        threat_model = modeler.generate_threat_model(architecture)

        # AI identifies threats for each component
        web_app_threats = threat_model.get_threats_for('Web App')

        assert any(t.category == 'Spoofing' for t in web_app_threats)
        assert any(t.category == 'Tampering' for t in web_app_threats)

        # AI rates threat severity
        critical_threats = threat_model.get_critical_threats()

        for threat in critical_threats:
            print(f"Threat: {threat.name}")
            print(f"Category: {threat.category}")
            print(f"Impact: {threat.impact}")
            print(f"Likelihood: {threat.likelihood}")
            print(f"Mitigation: {threat.suggested_mitigation}")

    def test_attack_path_analysis(self):
        """AI discovers potential attack paths"""

        modeler = ThreatModeler()

        # AI analyzes attack paths
        attack_paths = modeler.find_attack_paths(
            start='Internet',
            target='Database',
            architecture=architecture
        )

        # AI ranks paths by feasibility
        most_likely_path = attack_paths.ranked_by_likelihood[0]

        print("Most Likely Attack Path:")
        for step in most_likely_path.steps:
            print(f"→ {step.component}")
            print(f"  Attack: {step.attack_type}")
            print(f"  Difficulty: {step.difficulty}")

        assert len(attack_paths.paths) > 0

from ai_security import AdversarialTester

class TestAdversarialML:
    def test_adversarial_example_generation(self):
        """Generate adversarial inputs to fool ML models"""

        tester = AdversarialTester()

        # Target: ML-based fraud detection
        ml_model = load_fraud_detection_model()

        # AI generates adversarial examples
        adversarial_examples = tester.generate_adversarial_inputs(
            model=ml_model,
            legitimate_inputs=legitimate_transactions,
            attack_type='evasion'
        )

        for example in adversarial_examples:
            prediction = ml_model.predict(example.input)

            # Adversarial example bypasses fraud detection
            assert prediction == 'legitimate'
            assert example.actual_label == 'fraudulent'

            print(f"Perturbation: {example.perturbation}")
            print(f"Success rate: {example.success_rate}")

# AI finds vulnerabilities, humans validate and prioritize
ai_findings = vulnerability_scanner.scan()
validated_findings = security_team.triage(ai_findings)

# Feed new vulnerabilities back to AI
def update_ml_model(new_vulnerability):
    training_data.add(new_vulnerability)
    model.retrain(training_data)
    model.deploy()