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🛡️ CVE-2024-52338 — arrow

🔴 CVSS 9.8 — Critical ✅ No Known Exploit CWE-502 NVD
9.8
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

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.

How this vulnerability can be exploited

This issue can be reached over the network, attack complexity is low, an attacker needs no privileges on the target. No user interaction is required. The scope is unchanged, so the impact stays within the vulnerable component. Rated impact: confidentiality high, integrity high, availability high.

CVSS metrics in full

The score comes from this vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

  • Attack vector: Network — reachable from anywhere that can route to the service.
  • Attack complexity: Low — the attack works reliably, with no preparation.
  • Privileges required: None — an unauthenticated stranger can try it.
  • User interaction: None — nobody has to be tricked into anything.
  • Scope: Unchanged — the damage stays inside the vulnerable component.
  • Confidentiality impact: High — total loss, or loss the attacker controls.
  • Integrity impact: High — total loss, or loss the attacker controls.
  • Availability impact: High — total loss, or loss the attacker controls.

Weakness class

CVE-2024-52338 is classified as CWE-502: Deserialization of Untrusted Data. Serialised data from an untrusted source is reconstructed into objects, which can trigger code during the process.

Affected software

CVE-2024-52338 is recorded against 2 packages.

  • arrow (from 4.0.0 up to 17.0.0)
  • pyarrow (from 4.0.0 up to 17.0.0)

Timeline and source

Published on 28 November 2024 and last revised on 17 June 2026. No public exploit is currently recorded for this entry. A vendor advisory or fix has been published. Record sourced from NVD.

References

github.com
lists.apache.org
www.openwall.com

Other advisories for this package

arrow has other advisories on record. If you are patching this one, these are worth checking on the same host:

Same weakness in other software

These advisories are the same class of weakness (CWE-502: Deserialization of Untrusted Data) in other software:

Details

Severity CRITICAL
CVSS Score 9.8
CVSS Vector CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE CWE-502
Public Exploit ✅ No
Source NVD
Published 2024-11-28
Updated 2026-08-20
Modified 2026-06-17

Affected Packages

Software From version Fixed in
arrow 4.0.0 17.0.0
pyarrow 4.0.0 17.0.0

Similar Threats

Exploit Protection

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ⓘ Data Notice: The information presented above has been compiled from publicly available internet sources. Boteraser aggregates this data solely for informational purposes and does not independently classify, evaluate, or endorse any findings about the vulnerabilities listed. The accuracy and completeness of this information is the sole responsibility of the original publishers. Boteraser and its operators accept no liability for any decisions made based on this data.

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