GHSA-9fq2-x9r6-wfmf CVE-2019-6446 PYSEC-2019-108

Numpy Deserialization of Untrusted Data

Critical PyPI

Severity: 0.0 / 10; Critical · CVSS 4.0
Exploit chance (EPSS): 17.53%; Chance of exploitation in the next 30 days. Higher than 97.14% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-502 Deserialization of Untrusted Data
Fix: 1.16.3; Upgrade numpy to this version or later

Description

** DISPUTED ** An issue was discovered in NumPy 1.16.2 and earlier. It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources.

Affected versions

PyPI

Package	Vulnerable	Fixed in
numpy	`< 1.16.3`	`1.16.3`
numpy	`< 1.16.1`	`1.16.1`

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

numpy PyPI → `1.16.3`

pip

pip install --upgrade "numpy>=1.16.3"

poetry

poetry add "numpy>=1.16.3"

uv

uv add "numpy>=1.16.3"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Attack vector: Network
Attack complexity: Low
Attack requirements: None
Privileges required: None
User interaction: None
Confidentiality (vulnerable system): High
Integrity (vulnerable system): High
Availability (vulnerable system): High
Confidentiality (subsequent systems): None
Integrity (subsequent systems): None
Availability (subsequent systems): None

CVSS 3.0 9.8

CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Attack vector: Network
Attack complexity: Low
Privileges required: None
User interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: High
Availability: High

Weakness

CWE-502: Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

How to prevent it

Architecture and Design, Implementation: If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Implementation: When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Implementation: Explicitly define a final object() to prevent deserialization.
Architecture and Design, Implementation: Make fields transient to protect them from deserialization. An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.
Implementation: Avoid having unnecessary types or gadgets (a sequence of instances and method invocations that can self-execute during the deserialization process, often found in libraries) available that can be leveraged for malicious ends. This limits the potential for unintended or unauthorized types and gadgets to be leveraged by the attacker. Add only acceptable classes to an allowlist. Note: new gadgets are constantly being discovered, so this alone is not a sufficient mitigation.
Architecture and Design, Implementation: Employ cryptography of the data or code for protection. However, it's important to note that it would still be client-side security. This is risky because if the client is compromised then the security implemented on the client (the cryptography) can be bypassed.
Operation: Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].

References

Advisories

Packages

github.com/numpy/numpy

Timeline

Jan 16, 2019 Published
Jul 6, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-f7c7-j99h-c22f Buffer Copy without Checking Size of Input in NumPy	Medium	Also affects numpy
GHSA-5545-2q6w-2gh6 NumPy NULL Pointer Dereference	High	Also affects numpy
GHSA-fpfv-jqm9-f5jm Incorrect Comparison in NumPy	Medium	Also affects numpy
GHSA-6p56-wp2h-9hxr NumPy Buffer Overflow (Disputed)	Medium	Also affects numpy
GHSA-cw6w-4rcx-xphc Arbitrary file write in NumPy	Medium	Also affects numpy
GHSA-2fc2-6r4j-p65h Numpy arbitrary file write via symlink attack	Medium	Also affects numpy
GHSA-frgw-fgh6-9g52 Numpy missing input validation	High	Also affects numpy
CVE-2026-110879 jackson-databind validates the outer class of a class-name type ID but accepts attacker-selected classes inside its generic arguments, even when those classes…	Critical	Same weakness, CWE-502

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