CVE-2020-15201 in TensorFlow
Summary
by MITRE
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
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Analysis
by VulDB Data Team • 11/14/2020
The vulnerability described in CVE-2020-15201 represents a critical heap buffer overflow flaw within TensorFlow's RaggedCountSparseOutput implementation, specifically affecting versions prior to 2.3.1. This issue stems from inadequate input validation mechanisms that fail to properly verify the structural integrity of ragged tensor inputs. The vulnerability manifests when the system processes ragged tensors without ensuring that the splits tensor correctly partitions the values tensor, creating a dangerous condition where memory access operations can extend beyond allocated buffer boundaries.
The technical flaw occurs in the validation logic of the RaggedCountSparseOutput function where the system assumes that input arguments form a valid ragged tensor structure without performing essential checks. The core problem lies in the absence of validation for the splits tensor values, which should ensure that these values create a proper partitioning of the values tensor. When the split_values tensor does not conclude with a value that is at least equal to the number of values, the while loop condition triggers an out-of-bounds read operation. This occurs because the batch_idx variable continues to increment beyond the valid range of the split_values array, causing the system to access memory locations that were never allocated for this data structure.
The operational impact of this vulnerability extends beyond simple memory corruption, as it creates potential attack vectors for remote code execution and system compromise. The heap buffer overflow condition allows malicious actors to manipulate memory layout and potentially execute arbitrary code with the privileges of the TensorFlow process. This vulnerability is particularly concerning in environments where TensorFlow processes untrusted input data, as attackers could craft malicious ragged tensor inputs that trigger the buffer overflow during normal processing operations. The vulnerability affects the core tensor processing capabilities of TensorFlow, potentially compromising any application that relies on ragged tensor operations for data processing or machine learning workflows.
The fix implemented in TensorFlow version 2.3.1 addresses this issue through the commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 which introduces proper validation mechanisms for ragged tensor inputs. This patch ensures that the splits tensor values are verified against the number of values to prevent out-of-bounds memory access. The mitigation strategy involves implementing comprehensive input validation that checks the structural integrity of ragged tensor components before processing, aligning with industry standards for secure coding practices. Organizations should prioritize upgrading to TensorFlow 2.3.1 or later versions to remediate this vulnerability, while also implementing additional input sanitization measures for any applications that process external ragged tensor data. This vulnerability aligns with CWE-129 and CWE-787 categories, representing improper input validation and out-of-bounds read conditions respectively, and falls under ATT&CK technique T1059.001 for execution through command and scripting interpreter, as exploitation could enable arbitrary code execution within the TensorFlow processing environment.