Pruebas de Seguridad con IA: Encontrar Vulnerabilidades Más Rápido

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):
        """Fuzzing guiado por IA de endpoints API"""

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

        # IA aprende de exploits exitosos
        fuzzing_results = self.fuzzer.fuzz_endpoint(
            url=target_endpoint,
            method='POST',
            base_payload={
                'username': 'testuser',
                'email' (como se discute en [AI Test Metrics Analytics: Intelligent Analysis of QA Metrics](/blog/ai-test-metrics)): 'test@example.com',
                'password': 'password123'
            },
            iterations=10000,
            mutation_strategy='ai_guided'
 (como se discute en [Will AI Replace QA Engineers by 2030? The Future of Testing Profession](/blog/future-of-qa-profession))        )

        # IA identifica vectores de ataque prometedores
        critical_findings = [
            f for f in fuzzing_results.findings
            if f.severity == 'Critical'
        ]

        for finding in critical_findings:
            print(f"Vulnerabilidad: {finding.type}")
            print(f"Payload: {finding.payload}")
            print(f"Respuesta: {finding.response_code}")

        assert len(fuzzing_results.findings) > 0

from ai_security import AIPentester

class TestAutomatedPentest:
    def test_reconnaissance_phase(self):
        """IA realiza reconocimiento inteligente"""

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

        # Reconocimiento impulsado por IA
        recon_results = pentester.reconnaissance()

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

        # IA identifica superficie de ataque
        attack_surface = recon_results.analyze_attack_surface()

        print("Objetivos de Alto Valor:")
        for target in attack_surface.high_value_targets:
            print(f"- {target.url}")
            print(f"  Tech: {target.technology}")
            print(f"  Riesgo: {target.risk_score}")

from ai_security import VulnerabilityPredictor

class TestVulnerabilityPrediction:
    def test_predict_sql_injection_risk(self):
        """IA predice vulnerabilidad de inyección SQL desde código"""

        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'
 (como se discute en [Test Automation with Claude and GPT-4: Real Integration Cases and Practical Implementation](/blog/claude-gpt4-automation))        assert prediction.confidence > 0.90

        # IA sugiere corrección
        suggested_fix = prediction.get_fix_suggestion()
        assert 'consulta parametrizada' in suggested_fix.description