CVE-2021-29558 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 `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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 identified as CVE-2021-29558 represents a critical heap buffer overflow condition within TensorFlow's sparse tensor operations, specifically affecting the tf.raw_ops.SparseSplit functionality. This issue stems from improper input validation and memory access control within the sparse tensor implementation, creating a potential pathway for remote code execution or system compromise. The vulnerability resides in the sparse_tensor.h file at lines 528-530 where array elements are accessed using a user-controlled offset parameter, fundamentally violating secure coding principles and establishing a direct attack surface for malicious actors.
The technical flaw manifests as a classic buffer overflow condition where attacker-controlled data directly influences memory access patterns within TensorFlow's internal sparse tensor processing routines. This type of vulnerability maps directly to CWE-121, which categorizes heap-based buffer overflows, and specifically aligns with CWE-787, representing out-of-bounds write operations. The implementation vulnerability occurs when the SparseSplit operation processes user-provided indices without adequate bounds checking, allowing an attacker to manipulate the offset parameter to access memory locations beyond the allocated buffer boundaries. This flaw enables arbitrary memory access patterns that can lead to information disclosure, denial of service, or potentially remote code execution depending on the execution context and memory layout.
Operationally, this vulnerability poses significant risks to machine learning environments that process untrusted data through TensorFlow pipelines, particularly in cloud computing scenarios, data processing platforms, or any system where TensorFlow serves as a core component for handling user-uploaded or externally sourced datasets. The impact extends beyond simple denial of service to potential privilege escalation and system compromise when the vulnerable TensorFlow instances are running with elevated privileges or when the memory corruption leads to exploitable conditions. Attackers could leverage this vulnerability to execute malicious code within the TensorFlow processing environment, potentially compromising entire machine learning workflows and data integrity. The vulnerability affects multiple TensorFlow versions within the supported release cycle, making it particularly concerning for organizations maintaining older but still supported TensorFlow installations.
Mitigation strategies should prioritize immediate patching of affected TensorFlow versions to 2.5.0 or the cherrypicked versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4, ensuring all affected systems receive the necessary security updates. Organizations should implement input validation measures at the application level to sanitize all sparse tensor operations and consider deploying network segmentation to limit exposure of TensorFlow services to untrusted inputs. Additionally, monitoring and logging should be enhanced to detect anomalous patterns in sparse tensor operations that might indicate exploitation attempts. The vulnerability demonstrates the importance of secure coding practices in machine learning frameworks and aligns with ATT&CK technique T1059.001 for command and scripting interpreter, as exploitation could enable attackers to execute arbitrary code within the machine learning processing environment. Security teams should also consider implementing runtime protections such as address space layout randomization and stack canaries to mitigate potential exploitation scenarios.