CVE-2022-41894 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 12/20/2022
The vulnerability CVE-2022-41894 resides within TensorFlow Lite's implementation of the CONV_3D_TRANSPOSE operator, representing a critical buffer overflow condition that stems from improper pointer arithmetic during bias addition operations. This flaw affects the reference kernel implementation specifically, where the data pointer increment logic contains a fundamental mathematical error that directly impacts memory access boundaries. The vulnerability manifests when processing 3D transpose convolution operations, where the number of input channels differs from the number of output channels, creating a scenario where memory corruption becomes possible through crafted malicious models.
The technical root cause of this vulnerability can be traced to CWE-121, which describes stack-based buffer overflow conditions, and CWE-787, which addresses out-of-bounds write vulnerabilities. The flaw occurs in the bias addition logic where the code incorrectly uses `data_ptr += num_channels;` instead of the correct `data_ptr += output_num_channels;` statement. This mathematical error becomes particularly dangerous when the number of input channels exceeds the number of output channels, as the pointer arithmetic advances beyond the allocated buffer boundaries. The mismatch between input and output channel counts creates a condition where attackers can precisely control memory writes beyond intended buffer limits, potentially leading to arbitrary code execution or information disclosure.
The operational impact of this vulnerability extends beyond simple memory corruption, as it provides attackers with a sophisticated means of exploiting machine learning inference environments. The attack vector requires an attacker to craft a specific TensorFlow Lite model with carefully configured input and output channel parameters, but once executed, the vulnerability enables precise buffer overflows that could be leveraged for privilege escalation or denial of service attacks. This vulnerability is particularly concerning in production environments where TensorFlow Lite is used for inference processing, as it could be exploited through malicious model inputs without requiring elevated privileges. The vulnerability affects TensorFlow versions 2.8.4 through 2.11, with the specific patch addressing the issue in the reference kernel resolver implementation.
Mitigation strategies for this vulnerability center around immediate version upgrades to TensorFlow 2.11 or applying the cherry-picked fix to affected versions 2.10.1, 2.9.3, and 2.8.4. Organizations should implement strict model validation procedures and avoid using untrusted models in production environments where TensorFlow Lite inference is performed. The fix implemented in commit 72c0bdcb25305b0b36842d746cc61d72658d2941 corrects the pointer arithmetic by ensuring that the data pointer increment aligns with the output channel dimensions rather than the input channel dimensions. Security teams should also consider implementing runtime monitoring for unusual memory access patterns and conduct thorough vulnerability assessments of all TensorFlow Lite implementations within their infrastructure. The ATT&CK framework categorizes this vulnerability under T1059.001 for command and scripting interpreter and T1203 for Exploitation for Client Execution, as it represents a classic buffer overflow exploit that could be used to execute arbitrary code within the inference environment.