TEE.Fail Side-Channel Attack Compromises Confidential Computing on DDR5 Systems
Academic researchers from Georgia Tech, Purdue University, and Synkhronix have developed a side-channel attack named TEE.Fail that enables the extraction of secrets from trusted execution environments (TEEs) in modern CPUs, including Intel's SGX and TDX, AMD's SEV-SNP, and even Nvidia's GPU Confidential Computing. The attack leverages a memory-bus interposition technique on DDR5 systems, using off-the-shelf equipment costing under $1,000, to physically intercept and analyze encrypted memory traffic. This method allows attackers with physical access and root privileges to extract cryptographic keys and forge attestation, undermining the security guarantees of confidential computing environments.
TEE.Fail is the first attack demonstrated against DDR5-based TEEs, extending previous DDR4-focused research such as WireTap and BatteringRAM. The researchers found that architectural changes in recent server-grade CPUs, specifically the adoption of deterministic AES-XTS encryption without memory integrity and replay protections, have introduced exploitable weaknesses. The attack's success highlights significant risks for organizations relying on hardware-based confidential computing, as it enables the compromise of sensitive data and secure workloads even on fully updated, trusted systems.
Timeline
Oct 28, 2025
TEE.Fail attack details are publicly disclosed
Multiple security outlets reported the public disclosure of TEE.Fail, describing how it can break protections in Intel SGX/TDX, AMD SEV-SNP, and related confidential-computing or secure-enclave technologies. Coverage highlighted that the attack threatens secrets stored or processed inside these environments.
Oct 28, 2025
Researchers develop TEE.Fail DDR5 side-channel attack on TEEs
Security researchers created TEE.Fail, a physical side-channel attack using the DDR5 memory bus to extract secrets from confidential-computing environments. Reports say the technique affects trusted execution technologies from Intel, AMD, and NVIDIA and can be carried out with relatively low-cost equipment.
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