Evidence used
- No CISA KEV confirmation is currently recorded.
- Exploitation requires an existing local or physical foothold with privileges.
- EPSS is 0.10% for the current model date.
BlackTreeCVE IntelligenceFull vulnerability report · 2026
CVE-2026-80047High confidence
Hugging Face Transformers library writes remote code to disk prior to consent check
Hugging Face · Transformers
7.8HighCVSS 3.1
Patch only the product branches with a verified fix
Fix availability varies by productHigh technical severity; prioritise exposed affected systems while verifying vendor guidance. Verified remediation exists for at least one product or source, but 75 structured product or package states remain unresolved. Apply remediation only to the exact product branch confirmed by its source.
R
Open reassessment dashboard →Operational reassessment
Published severity in operational context
Published severityHigh→Operational priority:Medium, lowered one band.downgradedsince 3 Sep 2026
Compensating controls
- Apply the linked authoritative mitigation while planning the permanent fix.
- Restrict local access and enforce least privilege on affected hosts.
- Monitor vendor guidance and exploitation sources for a material change.
Verification
- Confirm that the asset runs Hugging Face Transformers and falls inside the recorded affected range.
- Recheck the vendor advisory before scheduling a change because no verified fixed version is currently retained.
- Validate exposure, authentication requirements and compensating controls in the actual environment.
- Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
Remediation availability differs by product scope
Verified remediation exists for at least one product or source, but 75 structured product or package states remain unresolved. Apply remediation only to the exact product branch confirmed by its source.
- Red Hat Product Security: 75 affected or under-investigation product states without a fixed product state in the same current advisory
Open-source package ranges3 source-attributed ranges
These OSV and GitHub advisory ranges apply only to the named package and ecosystem. A listed fixed version is not a universal product patch or proof that an update is installed.
| Ecosystem and package | Affected range | First fixed version | Evidence |
|---|---|---|---|
| PyPItransformers | ECOSYSTEM: introduced 4.49.0; last affected 5.8.1 | Not stated | OSV record ↗aggregator derived · 1 Oct 2026 |
| PyPItransformers | ECOSYSTEM: introduced 4.49.0; last affected 5.8.1 | Not stated | OSV record ↗source linked ecosystem record · 1 Oct 2026 |
| piptransformers | >= 4.49.0, <= 5.8.1 | Not stated | GitHub advisory ↗github reviewed aggregator · 30 Sep 2026 |
Direct vendor intelligence
1 currentAuthoritative vendor CSAF and VEX advisories
Structured product status and remediation from the issuing vendor. Product-state explanations are always visible; large lists can be searched or downloaded.
CVE-2026-80047 · CSAF 2.0 · revision 3 · finalRed Hat Product Securitytransformers: Hugging Face Transformers: Unauthorized file write via GenerativePreTrainedModel.load_custom_generate()
The vendor explicitly identifies these products as affected by this CVE.
- lightspeed-core/lightspeed-stack-rhel9 as a component of Lightspeed Core
- lightspeed-core/rag-tool-cpu-rhel9 as a component of Lightspeed Core
- lightspeed-core/rag-tool-cuda-12.9-rhel9 as a component of Lightspeed Core
- openshift-lightspeed-tech-preview/lightspeed-rag-tool-rhel9 as a component of OpenShift Lightspeed
- openshift-lightspeed/lightspeed-ocp-rag-rhel9 as a component of OpenShift Lightspeed
- openshift-lightspeed/lightspeed-service-api-rhel9 as a component of OpenShift Lightspeed
- rhaii/model-opt-cuda-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server
- Summary
- A flaw was found in the Hugging Face Transformers library. A remote attacker could exploit this vulnerability by enticing a user to load a model using the GenerativePreTrainedModel.load_custom_generate() function. This function writes remote Python files to the local disk without requiring explicit user consent, bypassing intended security checks. This can result in unauthorized files persisting on the system and potentially lead to the execution of previously cached malicious code during subsequent trusted model loads.
- Remediation
- Until a fix is available, users should only load models from trusted repositories. Avoid using the GenerativePreTrainedModel.load_custom_generate() function with models from untrusted or unknown sources. Organizations should implement controls to restrict which model repositories their AI/ML workloads can access.
High technical severity; prioritise exposed affected systems while verifying vendor guidance. Verified remediation exists for at least one product or source, but 75 structured product or package states remain unresolved. Apply remediation only to the exact product branch confirmed by its source.
Fix availability varies by product01
What, why and how
A vulnerability in Hugging Face Transformers (versions 4.57.0 to 5.16.1) allows remote Python files to be written to local disk without user consent when using GenerativePreTrainedModel.load_custom_generate(). The function fetches and caches a remote module file before performing the required trust_remote_code consent check, inverting the security model enforced by other code-loading paths (such as AutoConfig, AutoModel, and AutoTokenizer). As a result, attacker‑controlled Python code from custom_generate/generate.py is copied into the user’s ~/.cache/huggingface/modules directory even if the user declines the trust prompt. Although execution is correctly gated, the file write is not reversible and can persist across sessions. This can lead to persistent, unauthorized files on disk and stale cache collisions where cached attacker code may later be executed during trusted model loads. The issue stems from an unconditional file write in dynamic_module_utils.py prior to any trust verification.
What
A vulnerability in Hugging Face Transformers (versions 4.57.0 to 5.16.1) allows remote Python files to be written to local disk without user consent when using GenerativePreTrainedModel.load_custom_generate(). The function fetches and caches a remote module file before performing the required trust_remote_code consent check, inverting the security model enforced by other code-loading paths (such as AutoConfig, AutoModel, and AutoTokenizer). As a result, attacker‑controlled Python code from custom_generate/generate.py is copied into the user’s ~/.cache/huggingface/modules directory even if the user declines the trust prompt. Although execution is correctly gated, the file write is not reversible and can persist across sessions. This can lead to persistent, unauthorized files on disk and stale cache collisions where cached attacker code may later be executed during trusted model loads. The issue stems from an unconditional file write in dynamic_module_utils.py prior to any trust verification.
Why
The product attempts to drop privileges but does not check or incorrectly checks to see if the drop succeeded.
How
An attacker operating through local access may attempt exploitation with low privileges. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
What
A vulnerability in Hugging Face Transformers (versions 4.57.0 to 5.16.1) allows remote Python files to be written to local disk without user consent when using GenerativePreTrainedModel.load_custom_generate(). The function fetches and caches a remote module file before performing the required trust_remote_code consent check, inverting the security model enforced by other code-loading paths (such as AutoConfig, AutoModel, and AutoTokenizer). As a result, attacker‑controlled Python code from custom_generate/generate.py is copied into the user’s ~/.cache/huggingface/modules directory even if the user declines the trust prompt. Although execution is correctly gated, the file write is not reversible and can persist across sessions. This can lead to persistent, unauthorized files on disk and stale cache collisions where cached attacker code may later be executed during trusted model loads. The issue stems from an unconditional file write in dynamic_module_utils.py prior to any trust verification.
Why
The product attempts to drop privileges but does not check or incorrectly checks to see if the drop succeeded.
How
An attacker operating through local access may attempt exploitation with low privileges. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
02
Exploit reality and attack path
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
Observed exploitation No confirmed evidence
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
Public PoC / exploit material None recorded
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.
- Likely attack path
- local access → Improper Check for Dropped Privileges → cause the confidentiality, integrity or availability impact described by the vendor
- Attack surface
- Local
- Privileges required
- Low: a basic authenticated account is required
- User interaction
- None
- Attack complexity
- Low: no specialised conditions are recorded
- Security boundary
- Unchanged: impact remains within the vulnerable component's security authority
- Weakness
?
CWE means Common Weakness Enumeration: a standard category for the underlying weakness. - CWE-273 ↗
CWE-273: Improper Check for Dropped Privileges. The product attempts to drop privileges but does not check or incorrectly checks to see if the drop succeeded.
- CVSS vector
?
CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity. CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HCommon Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
AVLocalAttack vector: The attacker needs local access to the vulnerable system.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRLowPrivileges required: The attacker needs basic user-level privileges.UINoneUser interaction: No action by another user is required.SUnchangedScope: The security impact remains within the vulnerable component's authority.CHighConfidentiality impact: A successful attack can cause a major loss.IHighIntegrity impact: A successful attack can cause a major loss.AHighAvailability impact: A successful attack can cause a major loss.- Post-exploitation / living off the land
- Stolen credentials, tokens and legitimate administration functions may provide continued access without deploying a large custom toolset.
03
Patch and workaround
Operational remediation based on structured source evidence.
- Status
?
Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change. - Fix availability varies by product
- Affected
- lightspeed-core/lightspeed-stack-rhel9 as a component of Lightspeed Core
- lightspeed-core/rag-tool-cpu-rhel9 as a component of Lightspeed Core
- lightspeed-core/rag-tool-cuda-12.9-rhel9 as a component of Lightspeed Core
- openshift-lightspeed-tech-preview/lightspeed-rag-tool-rhel9 as a component of OpenShift Lightspeed
- openshift-lightspeed/lightspeed-ocp-rag-rhel9 as a component of OpenShift Lightspeed
- openshift-lightspeed/lightspeed-service-api-rhel9 as a component of OpenShift Lightspeed
- rhaii/model-opt-cuda-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server
- rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/model-opt-cuda-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
- ansible-automation-platform-25/lightspeed-chatbot-rhel8 as a component of Red Hat Ansible Automation Platform 2
- ansible-automation-platform-26/lightspeed-chatbot-rhel9 as a component of Red Hat Ansible Automation Platform 2
- ansible-automation-platform-27/lightspeed-chatbot-rhel9 as a component of Red Hat Ansible Automation Platform 2
- rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
- rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
- rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
- rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
- rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
- rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
- and 45 more
- Action
- Use the product-specific evidence above. Patch only products with a verified fixed release, and keep every affected or under-investigation state without a matching fix in the remediation queue.
- Workaround
- Until a fix is available, users should only load models from trusted repositories. Avoid using the GenerativePreTrainedModel.load_custom_generate() function with models from untrusted or unknown sources. Organizations should implement controls to restrict which model repositories their AI/ML workloads can access.
04
Evidence and provenance
Published 1 Sept 2026 · Last source change 23 Sept 2026, 17:24 UTC · CWE-273 · Improper Check for Dropped Privileges
CVE recordCVE.org · 5.2
CVSS sourceCISA ADP
EPSS source FIRST · tracked since 2026-09-02
?
The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.European sourceENISA EUVD · EUVD-2026-69144
Product sourceVendor CSAF · Red Hat Product Security
Remediation sourceVendor CSAF · Red Hat Product Security
CWE sourceCNA
NVD statusNVD awaiting enrichment
Core structured fields are present and their contributing authorities are shown above.
Material change intelligence
View recent updates ↗What changed after publication
- Affected versionsThe structured affected or fixed version information changed.CNA ↗
- Before
- Transformers: 4.49.0 ≤ 5.8.1 · Fixed: No fixed version is explicitly recorded in the structured CVE data.
- After
- Transformers: 4.57.0 ≤ 5.16.1 · Fixed: No fixed version is explicitly recorded in the structured CVE data.
- Affected versionsThe structured affected or fixed version information changed.CNA ↗
- Before
- 4.49.0 ≤ 5.8.1 · Fixed: No fixed version is explicitly recorded in the structured CVE data.
- After
- Transformers: 4.49.0 ≤ 5.8.1 · Fixed: No fixed version is explicitly recorded in the structured CVE data.
- SeveritySeverity changed from Unknown to High.CISA ADP ↗
- Before
- Unknown
- After
- High
- CVSS scoreCVSS score changed from not recorded to 7.8 (CVSS 3.1 · CISA ADP · CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).CISA ADP ↗
- Before
- not recorded
- After
- 7.8 (CVSS 3.1 · CISA ADP · CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H)
- ENISA EUVD mappingEUVD-2026-69144 was added to the official ENISA EUVD mapping for this CVE.ENISA EUVD ↗
- Before
- not recorded
- After
- {"euvdId":"EUVD-2026-69144"}
- Catalogue recordCVE added to the BlackTree catalogue.CNA ↗
Technical terms and abbreviations used in this report
- CVE
- Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
- CVSS
- Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
- EPSS
- Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
- CWE
- Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
- CNA
- CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
- CISA ADP
- Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
- NVD
- National Vulnerability Database: NIST's enrichment service for CVE records.
- CERT / CSIRT
- A computer security incident response team that publishes warnings or coordinates incident response.
- PoC
- Proof of concept: public material that demonstrates or helps reproduce exploitation.
- CSAF
- Common Security Advisory Framework: a machine-readable format for security advisories.
- LoTL
- Living off the land: abuse of legitimate tools or system functions during an attack.