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[2602.19450] Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments
Summary
This article presents a red-teaming study of Claude Opus and ChatGPT as security advisors for Trusted Execution Environments (TEEs), highlighting vulnerabilities and proposing evaluation methodologies to improve their reliability.
Why It Matters
As organizations increasingly rely on AI-driven security advisors, understanding their limitations is crucial for safeguarding sensitive computations. This research addresses potential risks associated with LLMs in TEE contexts, offering a framework to enhance their effectiveness and security.
Key Takeaways
- Red-teaming reveals significant vulnerabilities in LLMs used as TEE security advisors.
- Failures in LLMs can transfer across models, indicating systemic issues.
- A new evaluation methodology, TEE-RedBench, can significantly reduce LLM failures.
- The study emphasizes the importance of groundedness and technical correctness in AI security applications.
- Policy gating and structured templates can enhance the reliability of AI-driven security tools.
Computer Science > Cryptography and Security arXiv:2602.19450 (cs) [Submitted on 23 Feb 2026] Title:Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments Authors:Kunal Mukherjee View a PDF of the paper titled Red-Teaming Claude Opus and ChatGPT-based Security Advisors for Trusted Execution Environments, by Kunal Mukherjee View PDF HTML (experimental) Abstract:Trusted Execution Environments (TEEs) (e.g., Intel SGX and ArmTrustZone) aim to protect sensitive computation from a compromised operating system, yet real deployments remain vulnerable to microarchitectural leakage, side-channel attacks, and fault injection. In parallel, security teams increasingly rely on Large Language Model (LLM) assistants as security advisors for TEE architecture review, mitigation planning, and vulnerability triage. This creates a socio-technical risk surface: assistants may hallucinate TEE mechanisms, overclaim guarantees (e.g., what attestation does and does not establish), or behave unsafely under adversarial prompting. We present a red-teaming study of two prevalently deployed LLM assistants in the role of TEE security advisors: ChatGPT-5.2 and Claude Opus-4.6, focusing on the inherent limitations and transferability of prompt-induced failures across LLMs. We introduce TEE-RedBench, a TEE-grounded evaluation methodology comprising (i) a TEE-specific threat model for LLM-mediated security work, (ii) a structured prompt suite spanning SGX and TrustZone...