CVE-2024-52338 (GCVE-0-2024-52338)
Vulnerability from cvelistv5 – Published: 2024-11-28 16:31 – Updated: 2024-11-29 15:09
VLAI
EPSS
VEX
Title
Apache Arrow R package: Arbitrary code execution when loading a malicious data file
Summary
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.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-11-29 15:07 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
apache
CNA
References
3 references
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Apache Software Foundation | Apache Arrow R package |
Affected:
4.0.0 , ≤ 16.1.0
(semver)
|
|
| apache_software_foundation | apache_arrow_r_package |
Affected:
4.0.0 , ≤ 16.1.0
(custom)
cpe:2.3:a:apache_software_foundation:apache_arrow_r_package:*:*:*:*:*:*:*:* |
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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- Confirmed: The vulnerability has been validated from an analyst's perspective.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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