CVE-2021-41208 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
TensorFlow represents one of the most widely adopted machine learning frameworks globally, serving as the foundation for countless artificial intelligence applications across industries. The vulnerability identified as CVE-2021-41208 specifically targets the boosted trees implementation within TensorFlow, a critical component that enables ensemble learning through gradient boosting methods. This particular implementation has been identified as unmaintained and contains significant security flaws that can be exploited by malicious actors to compromise system integrity and availability. The vulnerability affects multiple versions of TensorFlow including 2.4.4, 2.5.2, 2.6.1, and the affected versions prior to 2.7.0, making it a widespread concern across the TensorFlow ecosystem.
The technical flaw resides in the absence of proper input validation within the boosted trees code implementation, creating multiple attack vectors that leverage undefined behavior patterns. When attackers exploit this vulnerability, they can trigger denial of service conditions through null pointer dereferences or check failures that cause the application to crash. More critically, the vulnerability allows for references to be bound to null pointers, which introduces undefined behavior that can be leveraged for more sophisticated attacks. The implementation also permits attackers to read and write to heap buffers, depending on the specific API calls and parameters provided, effectively enabling memory corruption attacks that can lead to arbitrary code execution. This type of vulnerability falls under CWE-476 which specifically addresses null pointer dereference issues, while the undefined behavior aspects align with CWE-682 and CWE-758.
The operational impact of this vulnerability extends beyond simple service disruption, as it represents a significant security risk for organizations relying on TensorFlow's boosted trees functionality. Attackers can exploit these weaknesses to cause system instability, potentially leading to complete system crashes or unavailability of machine learning services. The memory manipulation capabilities present in this vulnerability create additional risks for data integrity and system confidentiality, as attackers can potentially access sensitive information stored in heap buffers or corrupt memory structures. Given that the boosted trees implementation is unmaintained, there are no future security updates planned for this specific component, making the vulnerability particularly concerning for long-term security posture. Organizations using TensorFlow for production machine learning workloads that depend on boosted trees functionality face substantial risk of system compromise.
The recommended mitigation strategy involves immediate deprecation of the affected boosted trees APIs within TensorFlow, as indicated by the TensorFlow team's decision to mark these APIs as deprecated in subsequent releases. Organizations should migrate away from using these vulnerable components and implement alternative machine learning approaches that do not rely on the problematic boosted trees implementation. The fix for this vulnerability has been incorporated into TensorFlow 2.7.0 and backported to the affected versions 2.6.1, 2.5.2, and 2.4.4, ensuring that users can update to these patched versions to eliminate the security risk. Security teams should prioritize updating their TensorFlow installations to the latest patched versions and conduct thorough testing to ensure that the updated versions do not introduce compatibility issues with existing machine learning pipelines. Additionally, organizations should consider implementing additional monitoring and detection measures to identify potential exploitation attempts targeting this vulnerability, particularly in environments where TensorFlow is used for critical machine learning workloads. The ATT&CK framework would categorize this vulnerability under T1499 which addresses network denial of service, and potentially T1059 for command and scripting interpreter usage if attackers attempt to exploit the undefined behavior for code execution.