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Hugging Face Diffusers CVE-2025-14922

HIGH
Deserialization of Untrusted Data (CWE-502)
2025-12-23 zdi-disclosures@trendmicro.com
7.8
CVSS 3.0 · Vendor: trendmicro
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Attack ChainAIDerived

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

  1. Access
    Craft malicious CogView4 checkpoint
  2. Delivery
    Deliver via model hub or malicious page
  3. Exploit
    Victim loads checkpoint in Diffusers
  4. Execution
    Unsafe deserialization triggers in parser
  5. Impact
    Execute arbitrary code as current user

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A08:2025 - Software or Data Integrity Failures
ASVS 5.0
V1 - Encoding and Sanitization Safe deserialization, type allowlists
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 load an attacker-supplied, untrusted model checkpoint through Hugging Face Diffusers' CogView4 checkpoint-parsing path, or to visit a malicious page / open a malicious file that triggers that loading (UI:R confirmed in the CVSS vector). The specific prerequisite is the processing of a crafted serialized checkpoint whose contents are not properly validated before deserialization (CWE-502). Limiting factors: the attack vector is LOCAL (AV:L), not a remotely reachable service, and mandatory user interaction is required - there is no unattended, network-triggered path. Risk is therefore concentrated in workflows that routinely ingest third-party or community-sourced model weights; environments that load only trusted, self-produced checkpoints face substantially lower practical exposure.

Impact

Signals are consistent and point to a genuine but condition-gated code-execution risk rather than an internet-wormable emergency. The CVSS 3.0 vector (AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H, 7.8 High) is the key discriminator: despite being described as 'Remote Code Execution,' the attack vector is LOCAL and requires user interaction (UI:R) - the victim must load a malicious checkpoint file or visit a malicious page, so this is not a direct remote-service exploit. EPSS is low at 0.37% (29th percentile), consistent with no active exploitation. The vulnerability is NOT on CISA KEV, and no public POC is referenced - only the ZDI advisory (ZDI-25-1142), so exploitation status is 'no public exploit identified at time of analysis.' The 'RCE' label plus AV:L is a common point of confusion: the title implies remote reach but the CVSS correctly models this as a local/file-borne deserialization bug (CWE-502), which in practice means social-engineering a user into loading an untrusted model checkpoint. Net: a real priority for anyone who loads third-party model weights, but not a drop-everything network-exposure crisis - the low EPSS and absence of KEV/POC support treating it as patch-in-normal-cycle unless you handle untrusted checkpoints routinely.

Exploit ScenarioDerived

An attacker publishes a malicious CogView4 checkpoint to a model-sharing hub or sends it directly, and a user loads it with Hugging Face Diffusers; the crafted serialized data triggers unsafe deserialization during checkpoint parsing and executes attacker-controlled code in the context of the user's process. No public POC is referenced, and because the CVSS vector is AV:L with UI:R, exploitation depends on convincing the victim to open the malicious file rather than on any network-facing service.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
Patch available 0.4% exploitation probability (EPSS)

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AnalysisAIv1 – Oct 19, 2026

Deserialization of untrusted data in Hugging Face Diffusers' CogView4 checkpoint-parsing path lets an attacker who supplies a crafted serialized checkpoint execute arbitrary code in the context of the current process when the victim loads it. The attack is local in vector (AV:L) and requires user interaction (UI:R) - the target must open a malicious file or visit a malicious page that triggers the CogView4 loader - so exposure is concentrated in workflows that routinely ingest third-party or community-sourced model weights rather than in internet-facing services. Severity is rated 7.8 (CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) with an EPSS of only 0.37% (29th percentile); no public exploit code or confirmed active exploitation (CISA KEV) was identified at time of analysis, and reported signals are consistent with a genuine but condition-gated code-execution risk rather than an internet-wormable emergency.

RemediationAI

No vendor-released patch was identified in the available data at time of analysis; track the Zero Day Initiative advisory (https://www.zerodayinitiative.com/advisories/ZDI-25-1142/) and the Diffusers project for the fixed release before loading untrusted CogView4 checkpoints. Until then, the highest-value control is to eliminate pickle deserialization from the load path: convert and load weights exclusively in safetensors format, and where torch.load cannot be avoided use weights_only=True and avoid unpickling .bin/.pt checkpoint artifacts from community repositories. Additional compensating controls, with trade-offs: pin model repository revisions and verify file hashes against known-good values (blocks tampered uploads but requires manual tracking and breaks unpinned experimental workflows); run checkpoint loading inside a disposable container or sandbox with no network egress and no cloud credentials mounted (contains blast radius if a payload executes, at the cost of extra infrastructure and slower iteration); and restrict CogView4 loading to self-produced or internally vetted weights (strongest reduction of exposure but rules out pulling community models directly). Because the vector is local with mandatory user interaction, user awareness around opening unsolicited model files or checkpoint-bearing pages is a meaningful secondary mitigation.

Affected ProductsAI

The affected component is the CogView4 checkpoint-parsing path in Hugging Face Diffusers, reachable whenever an affected installation loads a CogView4 model checkpoint from an untrusted source. The provided intelligence does not include CPE strings or an explicit version range, so the exact affected releases are not confirmed from available data and should be validated against the vendor advisory at https://www.zerodayinitiative.com/advisories/ZDI-25-1142/ (ZDI-25-1142, credited to zdi-disclosures@trendmicro.com). Practically, any Diffusers deployment whose CogView4 pipeline can be pointed at third-party or community-uploaded weights should be treated as in scope until the vendor publishes a fixed version.

Affects your stack

Find out whether the stacks and servers you run are on an affected version.

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

Technical ContextAI · show details

The root cause is CWE-502 (Deserialization of Untrusted Data): Hugging Face Diffusers' CogView4 pipeline parses model checkpoints without adequately validating user-supplied content before deserialization, so a crafted checkpoint can carry a serialized payload that executes when reconstructed in memory. In the Diffusers/torch ecosystem this typically means pickle-based checkpoint artifacts (e.g., .bin/.pt files) rather than the safer safetensors format, which is why the flaw is classified as a checkpoint-parsing issue tied to the CogView4 loading path rather than to model inference itself. Because the payload runs in the context of the current process, the attacker inherits the privileges, credentials, and network reach of whatever job loads the weights. The CVSS vector confirms a local attack vector with no privileges required but mandatory user interaction, making this a supply-chain-style risk driven by the trust placed in downloaded model repositories, not a remotely reachable service flaw. No CPE data was supplied in the intelligence feed, so the precise affected version range is not pinned down in the available data; the authoritative vendor coordination reference is ZDI-25-1142.

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Severity by source

Vendor (trendmicro) PRIMARY 7.8 HIGH
AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
vuln.today AI 7.8 HIGH

File-borne deserialization needing the victim to load a malicious checkpoint, so AV:L and UI:R; no prior privileges (PR:N) and code execution yields full C/I/A impact.

3.1 AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
4.0 AV:L/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Red Hat 7.8 MEDIUM

Primary rating from Vendor (trendmicro).

CVSS VectorVendor: trendmicro

Attack Vector
Local
Attack Complexity
Low
Privileges Required
None
User Interaction
Required
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

DescriptionCVE.org

Hugging Face Diffusers CogView4 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Diffusers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file.

The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-27424.

Executive ViewAI

Why It Matters

Hugging Face Diffusers' CogView4 checkpoint-loading path can execute attacker-supplied code when a user loads a crafted model checkpoint, putting AI/ML teams and MLOps pipelines that ingest third-party or community model weights at risk. Although exploitation is local and requires user interaction-and no public exploit or active exploitation has been identified-the vulnerability can compromise developer workstations and model supply-chain integrity, so leadership should prioritize governance of model provenance and isolation of checkpoint-loading workflows.

Business Impact: Medium
Compensating Controls

No vendor-released patch identified at time of analysis. Interim mitigations: load only trusted, internally vetted CogView4 checkpoints; prefer safetensors or other non-executable formats over pickle-based serialization where supported; scan and quarantine third-party checkpoints; run model loading in isolated containers/VMs with least privilege and no sensitive network or filesystem access; disable or restrict automatic checkpoint downloads from community hubs; and verify cryptographic hashes and provenance for all model artifacts.

Threat Intelligence Overview

EPSS Score
0.4%
Top 71% exploitable
Prevalence
Medium
widely-used ML library but checkpoint-loading flaw is AI/ML-specific, not everywhere
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 0.4%
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
Defer
Exploitation· derived
None
System Exposure· derived
Small
Automatable· derived
No
Human Impact
Medium

Priority Score

39
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +39
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Insecure deserialization occurs when an application converts serialized data (a stream of bytes representing an object's state) back into a living object without proper validation.

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

Vendor StatusVendor

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