AI-Powered Security Testing: Finding Vulnerabilities Faster

from ai_security import IntelligentFuzzer

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 which mutations trigger vulnerabilities
        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 prioritizes findings by exploitability
        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"Exploitability: {finding.exploitability_score}")

        assert len(fuzzing_results.findings) > 0

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 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 achieves 40% higher coverage than random
        assert results.coverage_percentage > 85
        assert results.unique_crashes > 15

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.technologies_detected is not None

        # AI identifies high-value 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"  Technology: {target.technology}")
            print(f"  Risk Score: {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"Type: {exploit.type}")
            print(f"Entry Point: {exploit.entry_point}")
            print(f"Impact: {exploit.impact_assessment}")

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

from ai_security import VulnerabilityPredictor

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

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

        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

        # AI suggests remediation
        suggested_fix = prediction.get_fix_suggestion()
        print(f"Fix: {suggested_fix.description}")
        print(f"Fixed code:\n{suggested_fix.fixed_code}")

    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 by exploitability
        critical_vulns = results.get_by_severity('critical')

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

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

        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()

        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 STRIDE threat model
        threat_model = modeler.generate_threat_model(architecture)

        # AI identifies threats per component
        for threat in threat_model.get_critical_threats():
            print(f"Threat: {threat.name}")
            print(f"Category: {threat.category}")
            print(f"Likelihood: {threat.likelihood}")
            print(f"Mitigation: {threat.suggested_mitigation}")