CVE-2021-41206 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
TensorFlow represents one of the most widely adopted machine learning frameworks globally, serving as the backbone for countless AI applications across industries. The vulnerability identified as CVE-2021-41206 stems from insufficient validation mechanisms within several TensorFlow operations that handle tensor arguments. This flaw manifests in the absence of proper shape validation for tensor inputs, creating a critical security gap that can be exploited to compromise system integrity. The vulnerability specifically affects TensorFlow versions prior to 2.7.0, with affected releases including 2.6.1, 2.5.2, and 2.4.4, all of which remain within supported release cycles, highlighting the widespread nature of this issue across the framework's ecosystem.
The technical exploitation of this vulnerability occurs through improper handling of tensor shape validation during operation execution. When TensorFlow operations process tensor arguments without adequate shape verification, the system can experience undefined behavior that manifests in various crash scenarios including segmentation faults and CHECK-fail related failures. These crashes represent fundamental violations of the operating system's memory management protocols and can lead to system instability. More critically, under certain conditions, the vulnerability enables memory access patterns that allow for heap-based buffer overflows, potentially permitting unauthorized reads and writes to memory locations populated with array data. This capability significantly broadens the attack surface beyond simple crash scenarios into potential data corruption and information disclosure threats.
The operational impact of CVE-2021-41206 extends far beyond simple system instability, particularly given TensorFlow's extensive deployment across cloud environments, edge devices, and enterprise AI infrastructure. In production systems where TensorFlow processes untrusted input data, this vulnerability could enable attackers to cause service disruption through deliberate crashes or potentially extract sensitive information through memory read operations. The vulnerability's presence in GPU operations specifically raises concerns about resource exhaustion attacks that could target computational resources in data centers where GPU acceleration is prevalent. From a cybersecurity perspective, this vulnerability aligns with CWE-129, which addresses improper validation of array indices, and represents a classic example of how insufficient input validation can lead to memory corruption vulnerabilities. The ATT&CK framework categorizes this under privilege escalation and defense evasion techniques, as the vulnerability could be leveraged to gain elevated privileges or circumvent security controls through system instability.
The remediation approach for CVE-2021-41206 involves comprehensive fixes implemented across multiple TensorFlow releases to ensure backward compatibility while addressing the core validation deficiencies. TensorFlow 2.7.0 incorporates the complete set of fixes for this vulnerability, while the project has also cherry-picked these commits to earlier versions to provide protection for systems that cannot immediately upgrade. This approach demonstrates the framework's commitment to maintaining security in supported release channels, though it also underscores the importance of timely patch management for organizations relying on TensorFlow for mission-critical AI workloads. Security practitioners should prioritize immediate deployment of these patches across all affected TensorFlow installations, particularly in environments where untrusted data processing occurs, as the vulnerability's exploitation potential increases with the complexity of tensor operations and the volume of data processed.