Collaborative penetration testing suite for emerging generative AI algorithms
This addresses vulnerabilities in generative AI for cybersecurity and quantum threat mitigation, but it appears incremental as it integrates existing tools like DAST, SAST, and blockchain.
The paper tackled securing generative AI models against classical and quantum cyberattacks by developing a collaborative penetration testing suite, resulting in a 70% reduction in high-severity issues within 2 weeks and 100% integrity for quantum-resistant cryptography in tests.
Problem Space: AI Vulnerabilities and Quantum Threats Generative AI vulnerabilities: model inversion, data poisoning, adversarial inputs. Quantum threats Shor Algorithm breaking RSA ECC encryption. Challenge Secure generative AI models against classical and quantum cyberattacks. Proposed Solution Collaborative Penetration Testing Suite Five Integrated Components: DAST SAST OWASP ZAP, Burp Suite, SonarQube, Fortify. IAST Contrast Assess integrated with CI CD pipeline. Blockchain Logging Hyperledger Fabric for tamper-proof logs. Quantum Cryptography Lattice based RLWE protocols. AI Red Team Simulations Adversarial ML & Quantum-assisted attacks. Integration Layer: Unified workflow for AI, cybersecurity, and quantum experts. Key Results 300+ vulnerabilities identified across test environments. 70% reduction in high-severity issues within 2 weeks. 90% resolution efficiency for blockchain-logged vulnerabilities. Quantum-resistant cryptography maintained 100% integrity in tests. Outcome: Quantum AI Security Protocol integrating Blockchain Quantum Cryptography AI Red Teaming.