1. This result extends our post-quantum cryptography methods by highlighting the need to incorporate GenAI-specific threat modeling (e.g., adversarial prompt injection, model inversion) into the security evaluation of PTLE-TLS/ML-KEM-768 deployments, which traditional STRIDE-based analyses may overlook.

Given a reference deployment of PTLE-TLS (Post-quantum TLS-Like Encapsulation) using ML-KEM-768, a security evaluation conducted using only STRIDE-based threat modeling will fail to identify ≥3 distinct exploitable attack vectors (adversarial prompt injection against any GenAI component in the control/orchestration plane, model inversion against any embedded ML model used for key-management decisioning or anomaly detection, and at least one hybrid attack chaining a GenAI vulnerability with a cryptographic side-channel) that a GenAI-augmented threat model (e.g., STRIDE + LINDDUN + a GenAI-specific taxonomy such as MITRE ATLAS or OWASP LLM Top 10) will identify. The hypothesis is falsifiable: if a rigorous, blinded STRIDE-only red-team exercise identifies the same vector set (within a pre-registered equivalence margin) as the GenAI-augmented exercise, the hypothesis is disproven.

Controlled, blinded, two-arm red-team comparison study:

• Arm A (control): 3 independent security teams perform STRIDE-only threat modeling + penetration testing on the reference architecture.
• Arm B (treatment): 3 independent security teams perform STRIDE + GenAI-specific threat modeling (MITRE ATLAS, OWASP LLM Top 10, adversarial ML taxonomy) + penetration testing on the identical architecture.
• Reference architecture: a purpose-built PTLE-TLS testbed with ML-KEM-768 key exchange, an LLM-based certificate-policy/anomaly-detection microservice, and standard TLS termination, deployed identically for both arms.
• Blinding: teams are not told the study hypothesis; only told "perform a security evaluation using method X."
• Outcome measurement: independent panel of 2 senior security architects (blinded to arm) scores each identified vulnerability for validity, exploitability (CVSS-like), and novelty relative to a master vulnerability list compiled post-hoc by the研究 organizers plus external bug-bounty submissions.

Testbed Architecture:
  Client <--TLS 1.3+ML-KEM-768--> TLS Terminator (OQS-OpenSSL)
                                        |
                                  Policy/Anomaly Microservice (LLM, e.g. Llama-3-8B)
                                        |  (handles: cert-renewal decisions,
                                        |   anomalous handshake pattern flagging)
                                  Key Management Service (KMS)

Attack surface enumeration:
  for team in [ArmA_STRIDE_only, ArmB_STRIDE_plus_GenAI]:
      threat_model = generate_threat_model(architecture_diagram, method=team.method)
      vulnerabilities = red_team_engagement(testbed, threat_model, hours=80)
      report = score_vulnerabilities(vulnerabilities, rubric=CVSS_GenAI_extension)
      submit(report, blinded=True)

Panel scoring:
  master_list = merge(all_reports) + planted_vulns
  for report in all_reports:
      validity, exploitability, novelty = blind_panel_score(report, master_list)

Statistical test:
  compare(ArmA.severity_weighted_scores, ArmB.severity_weighted_scores,
          test=MannWhitneyU, alpha=0.05)
  test_blast_radius(genai_vulns, target=TLS_session_integrity)