Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments

Researchers red-teamed ChatGPT and Claude Opus as TEE security advisors, finding both LLMs hallucinate mechanisms and overclaim guarantees in sensitive infrastructure guidance. The study demonstrates some failure patterns transfer across models (up to 12%) and proposes an 80.62% failure reduction through policy gating, retrieval grounding, and verification checks.

This research exposes a critical vulnerability in the growing practice of using LLMs as security advisors for Trusted Execution Environments—a foundational component protecting sensitive computation in enterprise, financial, and cryptographic systems. The findings reveal that state-of-the-art models like ChatGPT-5.2 and Claude Opus generate plausible-sounding but technically incorrect guidance on TEE architecture, attestation mechanisms, and threat modeling, creating real risk when security teams rely on these tools for architecture review and vulnerability assessment.

The convergence of two trends—increasing TEE deployment across cloud and edge infrastructure, coupled with enterprise adoption of LLM assistants for technical decision-making—creates a socio-technical blindspot. Unlike generic hallucinations, false claims about attestation scope or side-channel mitigations directly compromise security posture. The 12% cross-model failure transferability suggests these aren't isolated quirks but reflect shared limitations in how LLMs generalize about specialized, constraint-heavy domains.

The proposed mitigation pipeline (policy gating, retrieval grounding, structured templates, verification checks) achieving 80.62% failure reduction indicates that guardrails work but require substantial engineering investment. For organizations using LLMs in security workflows, this underscores the need for adversarial evaluation before deployment and human verification of high-stakes recommendations.

Looking forward, this research will likely accelerate development of domain-specific LLM evaluation frameworks and increase scrutiny of AI use in critical infrastructure decisions. The cybersecurity industry may shift toward specialized, smaller models fine-tuned on verified technical documentation rather than general-purpose assistants for TEE guidance.

• →ChatGPT and Claude Opus hallucinate TEE mechanisms and overclaim security guarantees when used as advisors for trusted execution environments.
• →Failure patterns transfer across LLM assistants up to 12.02%, indicating shared architectural limitations rather than isolated model quirks.
• →Policy gating, retrieval grounding, structured templates, and verification checks reduce LLM security failures by 80.62% in TEE contexts.
• →Security teams relying on LLMs for high-stakes infrastructure decisions face unquantified risk without adversarial evaluation and human verification.
• →Domain-specific LLM evaluation methodologies like TEE-RedBench may become essential before deploying AI assistants in critical security workflows.