CVE-2022-21733 in Tensorflow
Summary
by MITRE • 02/03/2022
Tensorflow is an Open Source Machine Learning Framework. The implementation of `StringNGrams` can be used to trigger a denial of service attack by causing an out of memory condition after an integer overflow. We are missing a validation on `pad_witdh` and that result in computing a negative value for `ngram_width` which is later used to allocate parts of the output. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/06/2025
The vulnerability identified as CVE-2022-21733 resides within TensorFlow's StringNGrams implementation, representing a critical denial of service weakness that can be exploited through memory exhaustion. This flaw manifests when the framework processes string n-gram operations without proper validation of input parameters, specifically the pad_width parameter that controls padding behavior during n-gram generation. The absence of input validation creates a scenario where maliciously crafted inputs can trigger integer overflow conditions, leading to unintended negative values being computed for ngram_width. This mathematical error directly impacts memory allocation decisions within the framework's memory management subsystem, ultimately resulting in out-of-memory conditions that can crash applications or render systems unresponsive.
The technical mechanism behind this vulnerability operates through a classic integer overflow attack vector that aligns with CWE-191, which describes unsigned integer underflow or overflow conditions. When the pad_width parameter exceeds certain thresholds, the internal calculation logic produces negative results for ngram_width, a value that should logically remain positive for valid string operations. This negative value subsequently propagates through the memory allocation pipeline, where it gets interpreted as a large positive integer due to two's complement representation. The framework's memory allocator then attempts to reserve memory blocks of these erroneously calculated sizes, leading to rapid memory consumption and eventual system resource exhaustion.
From an operational perspective, this vulnerability presents significant risks to machine learning infrastructure deployments that rely on TensorFlow's text processing capabilities. Attackers can exploit this weakness by crafting specific input strings that trigger the integer overflow condition, causing legitimate applications to consume excessive memory resources. The impact extends beyond simple denial of service as it can affect system stability and availability, particularly in production environments where memory constraints are already tight. This vulnerability affects multiple TensorFlow versions including 2.5.3, 2.6.3, 2.7.1, and requires immediate attention from system administrators and security teams managing machine learning workloads.
The mitigation strategy for CVE-2022-21733 involves implementing proper input validation on the pad_width parameter before it influences ngram_width calculations, ensuring that all computed values remain within expected positive ranges. This approach directly addresses the root cause by preventing the integer overflow condition that leads to negative ngram_width values. The fix incorporates bounds checking and parameter validation that aligns with defensive programming practices recommended in the OWASP Top Ten and NIST cybersecurity guidelines. Organizations should prioritize upgrading to TensorFlow 2.8.0 or applying the cherry-picked patches to affected versions, as these releases contain the necessary validation logic to prevent the exploitation of this memory allocation vulnerability. The remediation process also includes implementing monitoring systems to detect unusual memory consumption patterns that might indicate exploitation attempts, providing an additional layer of defense against similar vulnerabilities in the machine learning pipeline.