CVE-2020-15212 in TensorFlow
Summary
by MITRE
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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Analysis
by VulDB Data Team • 11/14/2020
TensorFlow Lite vulnerability CVE-2020-15212 represents a critical buffer overflow flaw that affects versions prior to 2.2.1 and 2.3.1, specifically within the segment sum operation implementation. This vulnerability stems from inadequate validation of segment ids tensor elements, allowing attackers to manipulate buffer boundaries through negative values that cause unauthorized memory writes. The flaw operates by enabling malicious actors with access to segment_ids_data to modify output_index values, subsequently writing data beyond the allocated output_data buffer boundaries. This type of vulnerability falls under CWE-121, heap-based buffer overflow, and aligns with ATT&CK technique T1059.001 for command and scripting interpreter execution, as exploitation could enable arbitrary code execution through memory corruption.
The technical implementation of this vulnerability occurs during model loading and execution phases where TensorFlow Lite processes segment sum operations without proper bounds checking for negative segment ids. When negative values are present in the segment_ids_data tensor, the mathematical calculations used to determine output_index values become invalid, leading to writes that extend beyond the intended buffer boundaries. This memory corruption can manifest as segmentation faults but more critically enables potential remote code execution through chained exploits that leverage additional vulnerabilities. The vulnerability's impact is amplified because it can be combined with other memory corruption issues to create more sophisticated attack vectors, making it particularly dangerous in environments where TensorFlow Lite models are processed with untrusted input data.
The operational impact of this vulnerability extends beyond simple memory corruption to potentially enable complete system compromise when exploited in conjunction with other techniques. Attackers can leverage this flaw to overwrite critical memory locations, potentially including function pointers, return addresses, or other program state information. The vulnerability's exploitation requires access to the segment_ids_data which may be present in various contexts including static model files or dynamic tensor generation during inference. This makes the vulnerability particularly concerning for applications that process external or user-supplied models, as the attacker could manipulate segment ids during model loading or inference execution. Organizations deploying TensorFlow Lite applications must consider both the immediate security implications and the potential for this vulnerability to be used as a stepping stone for more advanced attacks.
The patch implemented in commit 204945b19e44b57906c9344c0d00120eeeae178a addresses the root cause by adding proper validation checks for segment ids tensor elements. The recommended mitigation strategies include implementing custom Verifier code during model loading to ensure segment ids are positive, which effectively handles static model scenarios. For runtime-generated segment ids, similar validation can be applied during inference steps, though this approach becomes ineffective when segment ids are produced as tensor outputs during execution phases. The most robust solution remains upgrading to TensorFlow versions 2.2.1 or 2.3.1, which incorporate the necessary defensive programming measures. This vulnerability exemplifies the importance of input validation in machine learning frameworks and demonstrates how seemingly benign operations like segment sum can become attack vectors when proper bounds checking is absent. The issue also highlights the need for comprehensive security testing of machine learning libraries, particularly in environments where models may be loaded from untrusted sources, as outlined in the OWASP Top 10 for machine learning applications.