CVE-2018-7577 in TensorFlow
Summary
by MITRE
Memcpy parameter overlap in Google Snappy library 1.1.4, as used in Google TensorFlow before 1.7.1, could result in a crash or read from other parts of process memory.
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Analysis
by VulDB Data Team • 06/01/2020
The vulnerability identified as CVE-2018-7577 represents a critical memory safety issue within the Google Snappy compression library version 1.1.4 when integrated into Google TensorFlow frameworks prior to version 1.7.1. This flaw manifests as a memcpy parameter overlap condition that can lead to unpredictable behavior including system crashes or unauthorized memory access patterns. The issue stems from improper handling of memory copy operations where the source and destination memory regions overlap, creating potential for data corruption or information disclosure. Such vulnerabilities are particularly dangerous in machine learning environments where TensorFlow processes large volumes of data from potentially untrusted sources.
The technical root cause of this vulnerability aligns with CWE-121, which describes stack-based buffer overflow conditions, and CWE-125, which addresses out-of-bounds read vulnerabilities. The memcpy function in the Snappy library fails to properly validate overlapping memory regions during decompression operations, allowing attackers to manipulate memory pointers in ways that bypass normal safety checks. When the decompression routine encounters overlapping parameters in the memcpy operation, it can read from arbitrary memory locations or overwrite critical data structures, potentially leading to complete system compromise. This type of vulnerability falls under the ATT&CK technique T1059.001 for command and scripting interpreter and T1553.002 for software packing, as it enables attackers to potentially execute malicious code through manipulated compressed data.
The operational impact of CVE-2018-7577 extends beyond simple denial of service scenarios, as it can enable sophisticated attack vectors within machine learning pipelines. In TensorFlow environments, this vulnerability could be exploited through malicious model files, training data, or compressed intermediate results that are processed through the vulnerable Snappy decompression routine. Attackers could craft specially formatted compressed data that, when decompressed, triggers the overlapping memcpy condition and allows for memory read operations from adjacent memory regions. This capability could potentially expose sensitive model parameters, training data, or system memory contents, making it particularly dangerous for production machine learning systems handling confidential information. The vulnerability affects systems where TensorFlow processes external data, including web applications, data pipelines, and distributed machine learning frameworks.
Mitigation strategies for CVE-2018-7577 require immediate patching of affected TensorFlow versions to 1.7.1 or later, where the Snappy library integration has been corrected. Organizations should implement strict input validation for all compressed data processed through TensorFlow pipelines, particularly when dealing with external or untrusted sources. Additional defensive measures include deploying memory protection mechanisms such as stack canaries, address space layout randomization, and control flow integrity checks. Network segmentation and access controls should limit exposure of vulnerable TensorFlow instances to untrusted data sources. Security monitoring should focus on detecting anomalous decompression patterns or memory access behaviors that could indicate exploitation attempts. System administrators should also consider implementing automated patch management processes to ensure timely deployment of security updates across all TensorFlow installations. The vulnerability highlights the importance of proper memory management in compression libraries and underscores the need for thorough security testing of third-party components integrated into machine learning frameworks.