TensorFlow up to 1.14 UnsortedSegmentSum Negative Number heap-based overflow
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 3.4 | $0-$5k | 0.00 |
Summary
A vulnerability marked as critical has been reported in TensorFlow up to 1.14. This vulnerability affects the function UnsortedSegmentSum. Performing a manipulation as part of Negative Number results in heap-based overflow.
This vulnerability is cataloged as CVE-2019-16778. It is possible to initiate the attack remotely. There is no exploit available.
It is suggested to upgrade the affected component.
Details
A vulnerability classified as critical has been found in TensorFlow up to 1.14 (Artificial Intelligence Software). This affects the function UnsortedSegmentSum. The manipulation as part of a Negative Number leads to a heap-based overflow vulnerability. CWE is classifying the issue as CWE-122. A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc(). This is going to have an impact on confidentiality, integrity, and availability. The summary by CVE is:
In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0.
The weakness was shared 12/16/2019 (GitHub Repository). The advisory is shared at github.com. This vulnerability is uniquely identified as CVE-2019-16778 since 09/24/2019. It is possible to initiate the attack remotely. A authentication is needed for exploitation. It demands that the victim is doing some kind of user interaction. Technical details are known, but no exploit is available.
Upgrading to version 1.15 or 2.0 eliminates this vulnerability.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Product
Type
Name
Version
License
Website
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔍VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 3.6VulDB Meta Temp Score: 3.4
VulDB Base Score: 5.5
VulDB Temp Score: 4.9
VulDB Vector: 🔍
VulDB Reliability: 🔍
NVD Base Score: 2.6
NVD Vector: 🔍
CNA Base Score: 2.6
CNA Vector (GitHub, Inc.): 🔍
CVSSv2
| AV | AC | Au | C | I | A |
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍
NVD Base Score: 🔍
Exploiting
Class: Heap-based overflowCWE: CWE-122 / CWE-119
CAPEC: 🔍
ATT&CK: 🔍
Physical: No
Local: No
Remote: Yes
Availability: 🔍
Status: Not defined
EPSS Score: 🔍
EPSS Percentile: 🔍
Price Prediction: 🔍
Current Price Estimation: 🔍
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔍
Upgrade: TensorFlow 1.15/2.0
Patch: github.com
Timeline
09/24/2019 🔍12/16/2019 🔍
12/17/2019 🔍
03/12/2024 🔍
Sources
Product: github.comAdvisory: db4f9717c41bccc3ce10099ab61996b246099892
Status: Not defined
CVE: CVE-2019-16778 (🔍)
GCVE (CVE): GCVE-0-2019-16778
GCVE (VulDB): GCVE-100-147249
Entry
Created: 12/17/2019 09:04Updated: 03/12/2024 12:29
Changes: 12/17/2019 09:04 (39), 12/17/2019 09:09 (11), 03/12/2024 12:21 (5), 03/12/2024 12:29 (19)
Complete: 🔍
Cache ID: 216::103
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
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