Apache Arrow R up to 16.1.0 to_data_frame deserialization

| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 7.5 | $0-$5k | 0.00 |
Summary
A vulnerability identified as problematic has been detected in Apache Arrow R up to 16.1.0. Impacted is the function to_data_frame. The manipulation leads to deserialization.
This vulnerability is traded as CVE-2024-52338. There is no exploit available.
You should upgrade the affected component.
Details
A vulnerability was found in Apache Arrow R up to 16.1.0. It has been rated as problematic. This issue affects the function to_data_frame. The manipulation with an unknown input leads to a deserialization vulnerability. Using CWE to declare the problem leads to CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Impacted is confidentiality, integrity, and availability. The summary by CVE is:
Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()). This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0. Users are recommended to upgrade to version 17.0.0, which fixes the issue.
It is possible to read the advisory at github.com. The identification of this vulnerability is CVE-2024-52338 since 11/08/2024. Technical details of the vulnerability are known, but there is no available exploit. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 11/30/2024).
Upgrading to version 17.0.0 eliminates this vulnerability. Applying the patch 801de2fbcf5bcbce0c019ed4b35ff3fc863b141b is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Vendor
Name
Version
License
Website
- Vendor: https://www.apache.org/
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔍VulDB Reliability: 🔍
CVSSv3
VulDB Meta Base Score: 7.6VulDB Meta Temp Score: 7.6
VulDB Base Score: 5.5
VulDB Temp Score: 5.3
VulDB Vector: 🔍
VulDB Reliability: 🔍
CNA Base Score: 9.8
CNA Vector (apache): 🔍
CVSSv2
| AV | AC | Au | C | I | A |
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VulDB Base Score: 🔍
VulDB Temp Score: 🔍
VulDB Reliability: 🔍
Exploiting
Class: DeserializationCWE: CWE-502 / CWE-20
CAPEC: 🔍
ATT&CK: 🔍
Physical: No
Local: No
Remote: Yes
Availability: 🔍
Status: Not defined
EPSS Score: 🔍
EPSS Percentile: 🔍
Price Prediction: 🔍
Current Price Estimation: 🔍
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔍
Upgrade: Arrow R 17.0.0
Patch: 801de2fbcf5bcbce0c019ed4b35ff3fc863b141b
Timeline
11/08/2024 🔍11/28/2024 🔍
11/28/2024 🔍
11/30/2024 🔍
Sources
Vendor: apache.orgAdvisory: 801de2fbcf5bcbce0c019ed4b35ff3fc863b141b
Status: Confirmed
CVE: CVE-2024-52338 (🔍)
GCVE (CVE): GCVE-0-2024-52338
GCVE (VulDB): GCVE-100-286379
Entry
Created: 11/28/2024 17:48Updated: 11/30/2024 03:14
Changes: 11/28/2024 17:48 (57), 11/30/2024 03:14 (12)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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