CVE-2021-29616 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. 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 • 05/16/2021
The vulnerability identified as CVE-2021-29616 affects TensorFlow, a widely-used open-source machine learning platform that has become fundamental to AI development across numerous industries. This issue resides within the arithmetic optimizer component of TensorFlow's grappler system, specifically in the TrySimplify function implementation. The flaw represents a critical undefined behavior condition that can lead to system instability and potential exploitation. The vulnerability manifests when the optimization process attempts to handle nodes with no inputs, creating a scenario where a null pointer dereference occurs in specific corner cases. This type of vulnerability falls under the CWE-476 category of NULL Pointer Dereference, which is a well-documented software weakness that can result in crashes, denial of service, or potentially more severe consequences depending on the execution context.
The technical implementation of the TrySimplify function in TensorFlow's grappler optimizer demonstrates a classic programming error where the code does not properly validate whether input nodes exist before attempting to access their properties or methods. This particular issue occurs in the arithmetic_optimizer.cc file at lines 390-401, where the optimization logic fails to account for nodes that may have been created without proper input connections during graph construction or transformation processes. The undefined behavior arises from the assumption that all nodes passed to this function will have valid input references, which becomes invalid when processing certain edge cases in computational graphs. When such null pointer dereferences occur, they can cause the entire TensorFlow optimization process to crash, potentially leading to application instability or complete system failure during machine learning model compilation or execution phases.
The operational impact of this vulnerability extends beyond simple application crashes, as it affects the reliability and security posture of systems that depend on TensorFlow for machine learning workloads. In production environments where TensorFlow is used for critical AI applications, such as autonomous vehicles, financial risk analysis, or medical diagnostics, a crash caused by this vulnerability could result in significant operational disruptions. The vulnerability affects multiple versions of TensorFlow, including the major releases 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the upcoming 2.5.0 release, indicating that this is a widespread issue affecting the platform's core optimization functionality. Organizations using TensorFlow in their machine learning pipelines must consider this vulnerability as a potential threat to system stability, particularly in environments where automated model optimization processes are heavily utilized. The attack surface is expanded by the fact that this vulnerability can be triggered through normal graph construction and optimization workflows, making it difficult to prevent through simple input validation.
The fix for CVE-2021-29616 requires proper null pointer validation before attempting to access node properties in the TrySimplify function, ensuring that the optimization process gracefully handles nodes with no inputs. This mitigation approach aligns with the ATT&CK framework's mitigation strategies for software exploitation, specifically addressing the prevention of null pointer dereference attacks. Organizations should prioritize updating to the patched versions of TensorFlow, with the cherrypick releases for older supported versions providing necessary protection for legacy systems. The vulnerability also highlights the importance of proper software testing for edge cases in optimization routines, particularly in complex systems like TensorFlow where graph transformations can create numerous potential failure points. Security teams should monitor for potential exploitation attempts that might leverage this vulnerability to cause denial of service in machine learning platforms, as such attacks could disrupt critical AI services and data processing pipelines. The fix implementation should include comprehensive testing of graph optimization scenarios to ensure that similar null pointer dereference issues do not exist in other parts of the codebase.