Comprehensive Technical Analysis of CVE-2025-25176

CVE ID: CVE-2025-25176 CVSS Score: 9.1 (Critical) Published: January 13, 2026 Source: Imagination Technologies GPU Driver Vulnerabilities

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1. Vulnerability Assessment & Severity Evaluation

Vulnerability Overview

CVE-2025-25176 is a side-channel information disclosure vulnerability affecting secure workloads executed on platforms with Trusted Execution Environments (TEEs) or hardware-enforced isolation mechanisms. The flaw allows an attacker with access to a non-secure environment (e.g., user-space applications, unprivileged processes) to exfiltrate intermediate register values from secure workloads (e.g., cryptographic operations, DRM-protected content, or enclave computations).

Severity Justification (CVSS 9.1)

The Critical (9.1) severity rating is justified by the following CVSS metrics:

• Attack Vector (AV:N) – Exploitation may be possible remotely if the attacker can execute code in the non-secure environment (e.g., via a malicious app or compromised process).
• Attack Complexity (AC:L) – Low complexity; no specialized conditions are required beyond standard execution in the non-secure world.
• Privileges Required (PR:N) – No elevated privileges are needed; exploitation can occur from an unprivileged context.
• User Interaction (UI:N) – No user interaction is required.
• Scope (S:C) – Changes in scope; the vulnerability affects a separate security authority (secure world) from the attacker’s context (non-secure world).
• Confidentiality Impact (C:H) – High; sensitive intermediate data (e.g., cryptographic keys, session tokens) can be leaked.
• Integrity Impact (I:N) – None; the vulnerability does not allow modification of secure workloads.
• Availability Impact (A:N) – None; no disruption to system availability.

Key Risk Factors

• Cross-Domain Leakage: Enables secure-to-non-secure data exfiltration, bypassing hardware-enforced isolation.
• Cryptographic Compromise: Intermediate register values (e.g., from AES, RSA, or ECC operations) can be reconstructed to recover keys.
• DRM & IP Theft: May expose decrypted media streams or proprietary algorithms in secure enclaves.
• Persistence: If exploited, the attack could persist across reboots if the malicious workload remains scheduled.

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2. Potential Attack Vectors & Exploitation Methods

Attack Surface

The vulnerability manifests in heterogeneous computing environments where:

• Secure workloads (e.g., ARM TrustZone, Intel SGX, AMD SEV) execute in an isolated context.
• Non-secure applications (e.g., user-space processes, drivers, or GPU workloads) can schedule tasks that interact with the secure world.

Exploitation Techniques

A. Prime+Probe Side-Channel Attack (Most Likely)

1. Prime Phase:

   • The attacker schedules a non-secure workload (e.g., a GPU compute shader, CPU thread, or DMA operation) that competes for shared resources (e.g., CPU caches, GPU registers, or memory buses).
   • The attacker preloads microarchitectural structures (e.g., CPU cache lines, GPU register files) with known data.

2. Trigger Secure Workload:

   • A secure workload (e.g., cryptographic operation, DRM decryption) executes, modifying intermediate register values.
   • Due to inadequate register state isolation, remnants of these values persist in shared resources.

3. Probe Phase:

   • The attacker re-schedules the non-secure workload and measures access latencies or register state changes.
   • By analyzing timing differences or register value discrepancies, the attacker reconstructs intermediate data (e.g., AES round keys, RSA exponents).

B. GPU-Specific Exploitation (Imagination Technologies Context)

Given the reference to Imagination Technologies GPU drivers, exploitation may involve:

• GPU Compute Shaders: Malicious shaders running in the non-secure world pollute GPU register files before/after secure workload execution.
• DMA-Based Attacks: If the GPU has direct memory access (DMA), an attacker could snoop on register states during secure workload execution.
• Shared Memory Side Channels: If secure and non-secure workloads share GPU memory buffers, residual data may leak.

C. CPU Cache-Based Attacks (ARM TrustZone)

• Flush+Reload: If secure workloads use shared CPU caches, an attacker could flush cache lines and measure reload times to infer register values.
• Spectre-v1 Variant: If the secure workload has branch prediction vulnerabilities, speculative execution could leak register data.

Proof-of-Concept (PoC) Scenario

1. Attacker’s Malicious App:

   • Runs in non-secure user space (e.g., Android app, Linux process).
   • Schedules a GPU compute shader that preloads GPU registers with known values.

2. Victim’s Secure Workload:

   • A Trusted Application (TA) in ARM TrustZone executes an AES-256 encryption operation.
   • Intermediate round keys are stored in GPU registers (if offloaded to GPU).

3. Data Exfiltration:

   • The attacker’s shader re-executes and measures register state changes.
   • By correlating timing and value differences, the attacker reconstructs the AES round keys.

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3. Affected Systems & Software Versions

Likely Affected Platforms

Component	Affected Implementations
GPU Drivers	Imagination Technologies PowerVR GPU drivers (versions prior to patched release).
TEE Implementations	ARM TrustZone (Cortex-A series), Intel SGX, AMD SEV (if GPU offloading is used).
Operating Systems	Android (with GPU-accelerated TEE workloads), Linux (with GPU-based cryptography).
Hardware	SoCs with Imagination PowerVR GPUs (e.g., MediaTek, Samsung Exynos, some Qualcomm chips).
Secure Workloads	Cryptographic libraries (OpenSSL, BoringSSL), DRM (Widevine, PlayReady), enclave applications.

Specific Vulnerable Versions

• Imagination Technologies GPU Drivers:
  • All versions prior to the security patch (exact version TBD; vendor advisory should be consulted).
• ARM TrustZone Implementations:
  • Cortex-A7x, Cortex-A5x series with GPU-accelerated secure workloads.
• Intel/AMD Systems:
  • If GPU offloading is used for secure computations (e.g., SGX enclaves with GPU acceleration).

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4. Recommended Mitigation Strategies

Immediate Actions

Mitigation	Description
Apply Vendor Patches	Update GPU drivers (Imagination Technologies) and TEE firmware to the latest secure version.
Disable GPU Offloading for TEE	If possible, disable GPU acceleration for secure workloads to prevent register leakage.
Isolate Secure/Non-Secure Workloads	Ensure strict scheduling isolation between secure and non-secure GPU/CPU tasks.
Enable Microarchitectural Protections	Enable hardware mitigations (e.g., ARM Speculative Store Bypass Disable (SSBD), Intel IBRS).
Monitor for Anomalous GPU Activity	Deploy GPU telemetry monitoring to detect unusual register access patterns.

Long-Term Defenses

Strategy	Implementation Details
Register State Clearing	Zeroize GPU/CPU registers after secure workload execution to prevent residual data leakage.
Constant-Time Cryptography	Modify cryptographic implementations to avoid data-dependent register usage.
Hardware-Enforced Isolation	Use dedicated hardware (e.g., separate GPU cores for secure workloads) to prevent cross-domain leakage.
Formal Verification of TEE	Apply formal methods to verify that secure workloads do not leak intermediate data via shared resources.
Runtime Integrity Checks	Deploy TEE integrity monitoring (e.g., ARM TrustZone Integrity Measurement Architecture) to detect tampering.

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5. Impact on the Cybersecurity Landscape

Broader Implications

• Erosion of TEE Trust: This vulnerability undermines confidence in hardware-based security (e.g., ARM TrustZone, Intel SGX), which are foundational for mobile payments, DRM, and confidential computing.
• Supply Chain Risks: GPU drivers are often third-party components in SoCs, increasing the attack surface for embedded and IoT devices.
• Cryptographic Compromise: If exploited, this could lead to large-scale key extraction, affecting TLS, VPNs, and disk encryption.
• DRM & Content Protection Bypass: Attackers could extract decrypted media streams from secure DRM implementations (e.g., Widevine L1).

Industry Response

• Vendor Coordination: Imagination Technologies and SoC manufacturers (MediaTek, Samsung, Qualcomm) must release patches and hardware mitigations.
• Regulatory Scrutiny: Governments and standards bodies (e.g., NIST, Common Criteria) may tighten requirements for TEE implementations.
• Research Community: Expect new side-channel attack variants targeting GPU-based TEEs and heterogeneous computing environments.

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6. Technical Details for Security Professionals

Root Cause Analysis

The vulnerability stems from inadequate register state isolation in heterogeneous computing environments, where:

1. Secure workloads (e.g., cryptographic operations) execute in a TEE (e.g., ARM TrustZone).
2. Non-secure workloads (e.g., GPU shaders, CPU threads) can schedule tasks that share microarchitectural resources (e.g., GPU registers, CPU caches).
3. Register State Persistence: After a secure workload completes, intermediate values (e.g., cryptographic round keys) may remain in registers due to lack of explicit clearing.
4. Side-Channel Leakage: An attacker in the non-secure world can probe these registers via timing, power analysis, or direct access (if DMA is misconfigured).

Exploitability Conditions

Condition	Description
Shared Resource Access	The attacker must be able to schedule workloads that share GPU/CPU registers or memory buses with the secure world.
Register State Visibility	The TEE implementation does not clear registers after secure workload execution.
Side-Channel Observable	The attacker must have a mechanism to observe register state changes (e.g., timing, power, or direct memory access).
No Constant-Time Guarantees	The secure workload does not use constant-time algorithms, making intermediate values predictable via side channels.

Detection & Forensics

Detection Method	Implementation
GPU Register Access Monitoring	Log and analyze unusual GPU register access patterns from non-secure workloads.
TEE Integrity Checks	Use ARM TrustZone Integrity Measurement to detect tampering with secure workloads.
Side-Channel Telemetry	Deploy hardware performance counters to detect cache/timing anomalies indicative of side-channel attacks.
Memory Forensics	Analyze GPU memory dumps for residual secure workload data (if DMA is misconfigured).

Advanced Mitigation Techniques

Technique	Description
Register File Partitioning	Dedicate a subset of GPU/CPU registers exclusively for secure workloads.
Hardware Transactional Memory (HTM)	Use HTM (e.g., Intel TSX, ARM TME) to isolate secure workloads from non-secure interference.
Oblivious RAM (ORAM)	Implement ORAM to prevent memory access pattern leakage from secure workloads.
Fuzzing & Formal Verification	Apply fuzz testing and formal methods to verify that secure workloads do not leak intermediate data.

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Conclusion & Recommendations

CVE-2025-25176 represents a critical threat to hardware-enforced security, particularly in GPU-accelerated TEE environments. The vulnerability enables cross-domain data exfiltration, with severe implications for cryptographic security, DRM, and confidential computing.

Immediate Actions for Organizations:

1. Patch Management: Apply GPU driver and TEE firmware updates as soon as they are available.
2. Workload Isolation: Disable GPU offloading for secure workloads where possible.
3. Monitoring: Deploy GPU telemetry and side-channel detection to identify exploitation attempts.
4. Incident Response: Prepare for cryptographic key rotation if compromise is suspected.

Long-Term Security Improvements:

• Hardware Redesign: Future SoCs should enforce stricter register isolation between secure and non-secure domains.
• Formal Verification: Verify TEE implementations to ensure no residual data leakage.
• Industry Collaboration: GPU vendors, TEE providers, and OS developers must collaborate on secure-by-default scheduling policies.

Further Research Directions

• GPU-Specific Side Channels: Investigate new attack vectors in GPU architectures (e.g., shader-based timing attacks).
• Heterogeneous TEE Security: Study secure workload offloading in multi-core, multi-GPU systems.
• Automated Exploit Detection: Develop ML-based anomaly detection for GPU register access patterns.

Final Note: Given the high severity (CVSS 9.1) and broad impact, organizations should treat this vulnerability with urgency, particularly in mobile, IoT, and cloud environments where TEEs are widely deployed.