8.5 HIGH
- CVSS version (CVSS): 4.0
- Attack Vector (AV): Local (L)
- Attack Complexity (AC): Low (L)
- Attack Requirement (AT): None (N)
- Privileges Required (PR): None (N)
- User Interaction (UI): Passive (P)
- Vulnerable System Impact Confidentiality (VC): High (H)
- Vulnerable System Impact Integrity (VI): High (H)
- Vulnerable System Impact Availability (VA): High (H)
- Subsequent System Impact Confidentiality (SC): None (N)
- Subsequent System Impact Integrity (SI): None (N)
- Subsequent System Impact Availability (SA): None (N)
- Modified Attack Vector (MAV): Local (L)
- Modified Attack Complexity (MAC): Low (L)
- Modified Attack Requirement (MAT): None (N)
- Modified Privileges Required (MPR): None (N)
- Modified User Interaction (MUI): Passive (P)
- Modified Vulnerable System Impact Confidentiality (MVC): High (H)
- Modified Vulnerable System Impact Integrity (MVI): High (H)
- Modified Vulnerable System Impact Availability (MVA): High (H)
- Modified Subsequent System Impact Confidentiality (MSC): Negligible (N)
- Modified Subsequent System Impact Integrity (MSI): Negligible (N)
- Modified Subsequent System Impact Availability (MSA): Negligible (N)
- Safety (S): Not Defined (X)
- Automatable (AU): Not Defined (X)
- Recovery (R): Not Defined (X)
- Value Density (V): Not Defined (X)
- Vulnerability Response Effort (RE): Not Defined (X)
- Provider Urgency (U): Not Defined (X)
- Confidentiality Req. (CR): Not Defined (X)
- Integrity Req. (IR): Not Defined (X)
- Availability Req. (AR): Not Defined (X)
- Exploit Maturity (E): Not Defined (X)
by @LeSuisse Activity log
- Created suggestion
- @LeSuisse accepted
- @LeSuisse published on GitHub
MONAI before 1.6.0 Remote Code Execution via algo_from_pickle
MONAI versions before 1.6.0 contain a remote code execution vulnerability in the algo_from_pickle() function due to unsafe pickle.loads() deserialization in monai/auto3dseg/utils.py. Attackers can craft malicious pickle files that execute arbitrary system commands when deserialized by the vulnerable function.
References
-
GitHub Security Advisory (GHSA-qxq5-qhx6-94qw) vendor-advisory
-
VulnCheck Advisory: MONAI before 1.6.0 Remote Code Execution via algo_from_pickle third-party-advisory
Affected products
- <1.6.0
- ==1.6.0
Matching in nixpkgs
pkgs.python313Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
pkgs.python314Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
pkgs.pkgsRocm.python3Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
Package maintainers
-
@bcdarwin Ben Darwin <bcdarwin@gmail.com>
8.6 HIGH
- CVSS version (CVSS): 4.0
- Attack Vector (AV): Local (L)
- Attack Complexity (AC): Low (L)
- Attack Requirement (AT): None (N)
- Privileges Required (PR): None (N)
- User Interaction (UI): None (N)
- Vulnerable System Impact Confidentiality (VC): High (H)
- Vulnerable System Impact Integrity (VI): High (H)
- Vulnerable System Impact Availability (VA): High (H)
- Subsequent System Impact Confidentiality (SC): None (N)
- Subsequent System Impact Integrity (SI): None (N)
- Subsequent System Impact Availability (SA): None (N)
- Modified Attack Vector (MAV): Local (L)
- Modified Attack Complexity (MAC): Low (L)
- Modified Attack Requirement (MAT): None (N)
- Modified Privileges Required (MPR): None (N)
- Modified User Interaction (MUI): None (N)
- Modified Vulnerable System Impact Confidentiality (MVC): High (H)
- Modified Vulnerable System Impact Integrity (MVI): High (H)
- Modified Vulnerable System Impact Availability (MVA): High (H)
- Modified Subsequent System Impact Confidentiality (MSC): Negligible (N)
- Modified Subsequent System Impact Integrity (MSI): Negligible (N)
- Modified Subsequent System Impact Availability (MSA): Negligible (N)
- Safety (S): Not Defined (X)
- Automatable (AU): Not Defined (X)
- Recovery (R): Not Defined (X)
- Value Density (V): Not Defined (X)
- Vulnerability Response Effort (RE): Not Defined (X)
- Provider Urgency (U): Not Defined (X)
- Confidentiality Req. (CR): Not Defined (X)
- Integrity Req. (IR): Not Defined (X)
- Availability Req. (AR): Not Defined (X)
- Exploit Maturity (E): Not Defined (X)
by @LeSuisse Activity log
- Created suggestion
- @LeSuisse accepted
- @LeSuisse published on GitHub
MONAI before 1.6.0 OS Command Injection via dataset_name_or_id
MONAI before 1.6.0 is vulnerable to OS command injection in the nnUNetV2Runner component (monai.apps.nnunet.nnunetv2_runner). User-controlled values taken from the YAML configuration file (notably dataset_name_or_id) and from CLI/kwargs arguments are concatenated into a command string without quoting or validation and then passed to subprocess with shell=True, so shell metacharacters (e.g., ';' on Linux, '&' on Windows) are interpreted. If a victim loads and processes a crafted configuration file — for example by instantiating nnUNetV2Runner with the malicious YAML and invoking a training/validation job such as train_single_model() — arbitrary commands are executed with the privileges of the user running the job.
References
-
GitHub Security Advisory (GHSA-rghg-q7wp-9767) vendor-advisory
-
VulnCheck Advisory: MONAI before 1.6.0 OS Command Injection via dataset_name_or_id third-party-advisory
Affected products
- ==1.6.0
- <1.6.0
Matching in nixpkgs
pkgs.python313Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
pkgs.python314Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
pkgs.pkgsRocm.python3Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
Package maintainers
-
@bcdarwin Ben Darwin <bcdarwin@gmail.com>
8.5 HIGH
- CVSS version (CVSS): 4.0
- Attack Vector (AV): Local (L)
- Attack Complexity (AC): Low (L)
- Attack Requirement (AT): None (N)
- Privileges Required (PR): None (N)
- User Interaction (UI): Passive (P)
- Vulnerable System Impact Confidentiality (VC): High (H)
- Vulnerable System Impact Integrity (VI): High (H)
- Vulnerable System Impact Availability (VA): High (H)
- Subsequent System Impact Confidentiality (SC): None (N)
- Subsequent System Impact Integrity (SI): None (N)
- Subsequent System Impact Availability (SA): None (N)
- Modified Attack Vector (MAV): Local (L)
- Modified Attack Complexity (MAC): Low (L)
- Modified Attack Requirement (MAT): None (N)
- Modified Privileges Required (MPR): None (N)
- Modified User Interaction (MUI): Passive (P)
- Modified Vulnerable System Impact Confidentiality (MVC): High (H)
- Modified Vulnerable System Impact Integrity (MVI): High (H)
- Modified Vulnerable System Impact Availability (MVA): High (H)
- Modified Subsequent System Impact Confidentiality (MSC): Negligible (N)
- Modified Subsequent System Impact Integrity (MSI): Negligible (N)
- Modified Subsequent System Impact Availability (MSA): Negligible (N)
- Safety (S): Not Defined (X)
- Automatable (AU): Not Defined (X)
- Recovery (R): Not Defined (X)
- Value Density (V): Not Defined (X)
- Vulnerability Response Effort (RE): Not Defined (X)
- Provider Urgency (U): Not Defined (X)
- Confidentiality Req. (CR): Not Defined (X)
- Integrity Req. (IR): Not Defined (X)
- Availability Req. (AR): Not Defined (X)
- Exploit Maturity (E): Not Defined (X)
by @LeSuisse Activity log
- Created suggestion
- @LeSuisse accepted
- @LeSuisse published on GitHub
MONAI before 1.6.0 Remote Code Execution via NumpyReader
MONAI before 1.6.0 contains an unsafe deserialization vulnerability in the NumpyReader class that unconditionally uses numpy.load with allow_pickle=True when loading .npy and .npz files. Attackers can craft malicious .npy files with pickle payloads that execute arbitrary code when loaded through MONAI's standard data pipeline.
References
-
GitHub Security Advisory (GHSA-wg9g-w2j2-8pgr) vendor-advisory
-
VulnCheck Advisory: MONAI before 1.6.0 Remote Code Execution via NumpyReader third-party-advisory
Affected products
- <1.6.0
- ==1.6.0
Matching in nixpkgs
pkgs.python313Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
pkgs.python314Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
pkgs.pkgsRocm.python3Packages.monai
Pytorch framework (based on Ignite) for deep learning in medical imaging
Package maintainers
-
@bcdarwin Ben Darwin <bcdarwin@gmail.com>