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MONAI EUVDEUVD-2026-112388

| CVE-2026-118683 HIGH
Code Injection (CWE-94)
2026-10-28 disclosure@vulncheck.com GHSA-475h-4rfw-9899
8.6
CVSS 4.0 · Vendor: vulncheck
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Publish malicious bundle to model zoo/HF/NGC
  2. Delivery
    Victim loads bundle via monai.bundle.load/run
  3. Exploit
    ConfigWorkflow.__init__ passes logging.conf to fileConfig
  4. Execution
    eval() of handler class= field
  5. Impact
    Arbitrary code execution as victim user

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A05:2025 - Injection
ASVS 5.0
V1 - Encoding and Sanitization Code injection prevention, sandbox exec
MITRE ATT&CK

2 techniques across 2 tactics: Execution, Initial Access

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Exploitation Conditions

Exploitation requires the victim to obtain and load an attacker-controlled MONAI bundle through any entrypoint that constructs a workflow - monai.bundle.load() (the 'load weights' verb), monai.bundle.run(), run_workflow(), create_workflow(), or direct ConfigWorkflow(config_file=...) - and the bundle must contain a configs/logging.conf file. No special non-default configuration is needed: ConfigWorkflow.__init__ defaults logging_file to the bundle's own configs/logging.conf and calls logging.config.fileConfig() on it with no trust check, so the default behavior is vulnerable whenever that file is present; the payload fires at object construction, before initialize() or run(), and independently of any MONAI $-expression in the main config. The principal limiting factor is user interaction (CVSS UI:A): the attacker cannot reach a target that never loads their bundle, making this a supply-chain/social-engineering vector rather than direct remote exploitation; no authentication is required of the victim (PR:N) and code runs with the loading user's privileges. Setting logging_file=False disables the fileConfig call per the code.

Impact

This is a genuine high-priority issue rather than a CVSS-inflated one, though its real-world reach is gated by user interaction. The supplied CVSS 4.0 vector (AV:N/AC:L/AT:N/PR:N/UI:A, VC:H/VI:H/VA:H, score 8.6) describes a network-reachable, low-complexity, unauthenticated flaw with full confidentiality/integrity/availability impact, but the UI:A (Active user interaction) metric is decisive: exploitation requires a victim to deliberately load or run an attacker-published MONAI bundle, so this is a supply-chain social-engineering vector, not a wormable remote service bug. Exploit maturity is strong: the GHSA advisory ships a complete working PoC and reports a successful dynamic reproduction (ATTACKER_CODE_RAN: True, live id -u/whoami output), so publicly available exploit code exists and is confirmed functional. The vuln.today decision label is this_week. Two signals are absent: no EPSS score is provided in the input, and there is no CISA KEV listing, so there is no evidence of active in-the-wild exploitation despite the confirmed PoC - the current risk is 'proven-exploitable, not yet observed exploited.' The signals are internally consistent (network/low-complexity delivery, user-interaction gate, confirmed code execution); the main caveat is that 'network' reach depends entirely on the victim fetching an untrusted bundle.

Exploit ScenarioDerived

An attacker publishes a MONAI bundle (e.g. 'coolseg') to a widely-trusted channel such as the model zoo, Hugging Face, or NGC, with a benign-looking train.json and weights but a malicious configs/logging.conf. A researcher or inference service calls monai.bundle.load()/run() on it; during ConfigWorkflow.__init__ the logging.conf is passed to logging.config.fileConfig(), which eval()s the handler class= field and runs the attacker's Python as the victim user. A confirmed working PoC and successful dynamic reproduction are documented in the GHSA advisory.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
Patch available

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AnalysisAIv1 – Oct 28, 2026

Remote code execution in MONAI's bundle workflow loader (versions 1.2.0 through 1.6.0, fixed in 1.6.1) lets an attacker who publishes a malicious model bundle run arbitrary Python in the context of anyone who loads or runs it. Because ConfigWorkflow.__init__ defaults to the bundle's own configs/logging.conf and hands it to logging.config.fileConfig() without any trust check, a crafted INI handler section executes at object construction - before initialize() or run() - and independently of any MONAI $-expression in the main config, so a bundle with a completely benign train.json still triggers it. Exploitation is gated by user interaction: the victim must fetch and load the attacker's bundle via monai.bundle.load(), monai.bundle.run(), run_workflow(), create_workflow(), or a direct ConfigWorkflow(config_file=...), making this primarily a supply-chain/social-engineering vector against researchers and inference services rather than direct remote exploitation; a public proof-of-concept with dynamic reproduction is published in the upstream advisory, and the issue is prioritized for remediation this week.

RemediationAI

Vendor-released patch: MONAI 1.6.1. Upgrade the monai package to 1.6.1 or later (pip install --upgrade 'monai>=1.6.1') per advisory https://github.com/Project-MONAI/MONAI/security/advisories/GHSA-wvpx-5qmp-46g3. Where upgrading cannot be done immediately, pass logging_file=False to the workflow so ConfigWorkflow skips the logging.config.fileConfig() call entirely - the trade-off is that the bundle's own logging configuration is then not applied and logging falls back to defaults, and any code paths that construct workflows implicitly through monai.bundle.load()/run() would need to be wrapped or patched to supply that argument. Additionally, treat bundles as code: inspect or strip configs/logging.conf from any third-party bundle before loading it, and restrict loading to bundles from trusted, verified publishers (model zoo, NGC, vetted Hugging Face repos) until the upgrade is applied; the upstream advisory notes that remediating the fileConfig call is the complete fix, so review-before-load should be considered an interim control rather than a substitute for patching.

Affected VersionsENISA

MONAI 1.2.0 <1.6.1

Affected Packages

euvd monai fix: 1.6.1

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 1 package in euvd

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

MONAI bundles package a model's weights alongside JSON configuration that MONAI's workflow machinery interprets, and they are distributed widely through the MONAI model zoo, Hugging Face, and NGC. The root cause is CWE-94 (improper control of generation of code): monai/bundle/workflows.py sets logging_file to <bundle>/configs/logging.conf by default and, when that path exists, calls logging.config.fileConfig() with no validation. Python's fileConfig resolves the class= and args= fields of each INI handler section using eval(), so an attacker-supplied handler class expression such as a tuple-indexing trick around __import__('os').system(...) is executed as Python the moment the workflow object is constructed. This is a distinct primitive from the separately reported MONAI $-expression / _target_ RCE: the payload lives in a plain INI file rather than the MONAI $-DSL, so it evades the review heuristic of checking bundles for $ expressions, and it fires at construction time rather than at initialize(). The affected distribution is the PyPI package pypi/monai, vulnerable from 1.2.0 up to (but not including) 1.6.1. The assessed vector is CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H - no victim authentication is needed and code runs with the loading user's privileges, but the attacker cannot reach a target that never loads their bundle.

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Severity by source

Vendor (vulncheck) PRIMARY 8.6 HIGH
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
vuln.today AI 8.8 HIGH

Bundle is fetched over the network (AV:N) with no auth (PR:N) and reliable triggering (AC:L), but a victim must load the untrusted bundle (UI:R); full code execution yields C:H/I:H/A:H.

3.1 AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
4.0 AV:N/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Primary rating from Vendor (vulncheck).

CVSS VectorVendor: vulncheck

Attack Vector
Network
Attack Complexity
Low
Attack Requirements
None
Privileges Required
None
User Interaction
Active
Vulnerable system: Confidentiality
High
Vulnerable system: Integrity
High
Vulnerable system: Availability
High
Subsequent system: Confidentiality
None
Subsequent system: Integrity
None
Subsequent system: Availability
None

Lifecycle Timeline

2
Patch available
Oct 28, 2026 - 20:04 EUVD
CVE Published
Oct 28, 2026 - 19:17 cve.org
HIGH 8.6

DescriptionCVE.org

MONAI from 1.2.0 before 1.6.1 contains a remote code execution vulnerability in ConfigWorkflow.__init__, which passes a bundle's configs/logging.conf to logging.config.fileConfig that eval()s handler class and args fields. Attackers can publish a malicious bundle whose logging.conf runs arbitrary Python at workflow construction when a victim loads or runs the bundle.

Technical POC Analysis

Tracking note: the supplied upstream advisory (GHSA-wvpx-5qmp-46g3) scores this 8.8 and says "No known CVE" at publication; the CVE ID and 8.6 score come from the tracking entry.

Full technical POC analysis - root cause, step-by-step exploitation and patch breakdown - is part of the Pro and Business plans.

Executive ViewAI

Why It Matters

A high-severity vulnerability in MONAI's bundle workflow loader allows arbitrary code execution when users load malicious model bundles, primarily affecting AI researchers and inference services that use third-party bundles. Leadership should care because this supply-chain risk can compromise intellectual property and research integrity, requiring immediate user awareness and changes to bundle verification processes.

Business Impact: High
Compensating Controls

No vendor-released patch identified at time of analysis. Interim mitigations include: only load bundles from trusted sources; review bundle configuration files (especially logging.conf) before loading; run bundles in isolated environments (e.g., containers, sandboxes) with minimal privileges; monitor for suspicious Python process activity during bundle loading.

Threat Intelligence Overview

Prevalence
Medium
popular medical-imaging AI framework, niche to healthcare/ML research
Social Media
1
Sightings
Data Quality
3 sources
Confidence: Low

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity HIGH
CISA KEV No
Public POC No
EPSS N/A
Patch available Yes
Active threat No
Deciding rule
HIGH severity (CVSS 7.0-8.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision· derived
Scheduled
Exploitation· derived
None
System Exposure· derived
Open
Automatable· derived
No
Human Impact
High

Priority Score

43
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +43
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Code Injection CRITICAL

Code injection occurs when an application accepts user input and passes it directly into a language interpreter or evaluator without proper sanitization.

Remote Code Execution represents the critical moment when an attacker successfully runs arbitrary code on a target system without physical access.

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