CVE-2018-7574 in TensorFlow
Summary
by MITRE
Google TensorFlow 1.6.x and earlier is affected by a Null Pointer Dereference vulnerability. The type of exploitation is: context-dependent.
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Analysis
by VulDB Data Team • 09/06/2023
The vulnerability identified as CVE-2018-7574 affects Google TensorFlow versions 1.6.x and earlier, representing a critical null pointer dereference flaw that can be exploited through context-dependent attack vectors. This issue resides within the machine learning framework's codebase and demonstrates a fundamental programming error where the software attempts to access memory through a null pointer reference. The vulnerability's classification as context-dependent indicates that successful exploitation requires specific conditions or parameters that must be met by the attacker, making it less universally exploitable but still highly concerning given TensorFlow's widespread adoption in machine learning applications.
The technical flaw manifests when TensorFlow processes certain input data structures that trigger improper memory management within the framework's execution pipeline. When the system encounters malformed or unexpected tensor operations, the code path fails to properly validate pointer references before dereferencing them, leading to a segmentation fault or application crash. This behavior aligns with CWE-476, which specifically addresses null pointer dereference vulnerabilities, and represents a classic example of insufficient input validation in software systems. The vulnerability can be triggered through malformed model files, incorrect tensor dimensions, or maliciously crafted input data that causes the framework to attempt operations on uninitialized or improperly allocated memory regions.
The operational impact of CVE-2018-7574 extends beyond simple application crashes, as it can be leveraged to cause denial of service conditions that disrupt machine learning workflows and inference processes. In production environments where TensorFlow serves as a core component for model deployment, this vulnerability could lead to significant service interruptions and potential data processing failures. Attackers could exploit this weakness to cause system instability, particularly in scenarios where TensorFlow is used for real-time inference or batch processing tasks. The vulnerability's exploitation requires specific contextual conditions, making it less suitable for automated attacks but still dangerous in targeted scenarios where attackers can influence the input data flow to the machine learning system.
From a cybersecurity perspective, this vulnerability aligns with ATT&CK technique T1499.004, which covers network disruption attacks through service availability, and demonstrates how machine learning frameworks can become attack vectors when not properly hardened against malformed inputs. The vulnerability underscores the importance of input validation and robust error handling in complex software systems, particularly those handling sensitive data processing tasks. Organizations utilizing TensorFlow for production workloads should prioritize immediate patching of affected versions, as the null pointer dereference could potentially be chained with other vulnerabilities to create more severe attack scenarios. The remediation approach typically involves updating to TensorFlow 1.7.0 or later versions where proper null pointer checks have been implemented, along with implementing additional input sanitization measures to prevent exploitation through malformed data inputs.