CVE-2021-29518 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. 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 described in CVE-2021-29518 affects TensorFlow, an open source machine learning platform that has become widely adopted across various industries for developing and deploying artificial intelligence applications. This issue specifically targets the eager execution mode that became the default in TensorFlow 2.0 and later versions, where operations are executed immediately rather than being deferred until a session is explicitly run. The flaw represents a critical security concern that stems from improper handling of session state pointers within the framework's internal implementation.
The technical root cause of this vulnerability lies in the implementation of session operations within TensorFlow's kernel layer, specifically in the session_ops.cc file at line 104. When users attempt to execute certain raw operations that are typically associated with session functionality, the code performs a null pointer dereference without first validating whether the session state pointer is valid. In eager mode, the context's session_state() method returns nullptr, meaning that the code attempts to call member functions on a null pointer, which constitutes undefined behavior according to standard programming practices. This type of vulnerability falls under CWE-476, which specifically addresses null pointer dereference conditions that can lead to application crashes or potentially exploitable states.
The operational impact of this vulnerability extends beyond simple application instability, as it creates a potential vector for denial of service attacks that could disrupt machine learning workflows and training processes. When triggered, the null pointer dereference causes the TensorFlow runtime to crash, which could be particularly problematic in production environments where machine learning models are actively being trained or deployed. The vulnerability affects multiple versions of TensorFlow, including the major releases 2.1.4 through 2.4.2, indicating that organizations using these versions may be at risk if they have not applied the relevant patches. Attackers could potentially exploit this vulnerability by crafting specific operations that trigger the problematic code path, leading to service disruption or system instability.
The remediation strategy for this vulnerability involves implementing proper null pointer validation before dereferencing session state pointers, which aligns with fundamental secure coding practices and defensive programming principles. TensorFlow developers have addressed this issue by including the fix in version 2.5.0 and cherry-picking the commit to maintain support for older but still maintained versions of the framework. This approach demonstrates the importance of maintaining backward compatibility while ensuring security patches are applied across supported release lines. Organizations should prioritize upgrading to patched versions or implementing the necessary code changes to prevent exploitation. The vulnerability also highlights the need for comprehensive testing of edge cases in eager execution modes, where traditional session-based operations may still be invoked through lower-level interfaces. From an ATT&CK framework perspective, this vulnerability could be categorized under privilege escalation or denial of service techniques, as it allows for system instability that could potentially be leveraged in more sophisticated attack scenarios. The fix serves as a reminder of the critical importance of proper pointer validation in systems that handle complex computational workloads, particularly in machine learning environments where reliability and stability are paramount for maintaining consistent model performance and deployment workflows.