CVE-2021-29535 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat()` is an empty buffer and accessing the element at position 0 results in overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/19/2021
The vulnerability CVE-2021-29535 represents a critical heap buffer overflow condition within TensorFlow's QuantizedMul operation that stems from inadequate input validation and memory access handling. This flaw exists in the quantized multiplication kernel implementation where the system assumes that all four input tensors contain valid scalar values without proper verification. The specific implementation issue occurs in the quantized_mul_op.cc file at lines 287-290 where the code directly accesses tensor elements using the .flat() method without checking if the tensor is empty or contains valid data. When any of the threshold tensors passed to the QuantizedMul operation is empty, the .flat() method returns an empty buffer, yet the code attempts to access the element at position zero, resulting in a heap buffer overflow condition that can be exploited by malicious actors.
This vulnerability falls under CWE-129, which specifically addresses insufficient validation of length of buffers, and represents a classic example of improper input validation leading to memory corruption. The operational impact of this flaw is significant as it allows attackers to potentially execute arbitrary code or cause denial of service conditions within systems running affected TensorFlow versions. The vulnerability affects TensorFlow versions 2.1.4 through 2.4.1, with the fix being incorporated into TensorFlow 2.5.0 and backported to older supported versions. Attackers can exploit this by crafting malicious input tensors that contain empty or malformed threshold values, triggering the buffer overflow when the quantization kernel attempts to process these invalid inputs. The flaw demonstrates a fundamental security issue in how the TensorFlow runtime handles tensor validation, particularly in quantized operations where input parameters are assumed to be valid without proper bounds checking.
The exploitation of this vulnerability aligns with ATT&CK technique T1059.001 for command and control through code injection, as the heap overflow could potentially allow attackers to execute arbitrary code within the TensorFlow process. The security implications extend beyond simple denial of service to include potential privilege escalation and system compromise, especially in environments where TensorFlow is used for processing untrusted data inputs. Organizations using TensorFlow in production environments must urgently apply the patches provided by the TensorFlow team, as the vulnerability exists in multiple supported release branches. The fix implemented addresses the core issue by adding proper validation checks to ensure that tensor elements are valid before attempting to access their values, thereby preventing the buffer overflow condition that could be leveraged for remote code execution. This vulnerability highlights the importance of robust input validation in machine learning frameworks where complex tensor operations can become attack vectors when proper boundary checking is omitted.