CVE-2024-12720
Huggingface Transformers ≤ 4.48.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-12720 is a high-severity Inefficient Regular Expression Complexity (CWE-1333) vulnerability in Huggingface Transformers. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique OS Credential Dumping (T1003); ranked in the top 46% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-12720 is a Regular Expression Denial of Service (ReDoS) vulnerability in the Hugging Face transformers library, specifically within the tokenization_nougat_fast.py file's post_process_single() function. The issue arises from a regular expression that exhibits exponential time complexity due to excessive backtracking when processing specially crafted input, resulting in high CPU usage and potential application downtime. This affects version v4.46.3, which was the latest at the time of disclosure, and is rated with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H), mapped to CWE-1333.
The vulnerability can be exploited by any remote attacker with network access, requiring low attack complexity, no privileges, and no user interaction. By supplying malicious input to the vulnerable function, an attacker triggers the ReDoS condition, causing severe resource exhaustion and denial of service that disrupts application availability.
Mitigation is available via a patch in the transformers repository at commit deac971c469bcbb182c2e52da0b82fb3bf54cccf. Security practitioners should update to a version incorporating this fix. The issue was disclosed through Huntr, with bounty details at https://huntr.com/bounties/4bed1214-7835-4252-a853-22bbad891f98.
This vulnerability is particularly relevant to AI/ML workflows, as the transformers library is a core component for natural language processing models, underscoring the need to validate inputs in ML tokenization pipelines.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7016
Vulnerability Data
A Regular Expression Denial of Service (ReDoS) vulnerability was identified in the huggingface/transformers library, specifically in the file tokenization_nougat_fast.py. The vulnerability occurs in the post_process_single() function, where a regular expression processes specially crafted input. The issue stems from the regex…
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exhibiting exponential time complexity under certain conditions, leading to excessive backtracking. This can result in significantly high CPU usage and potential application downtime, effectively creating a Denial of Service (DoS) scenario. The affected version is v4.46.3 (latest).
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: huggingface, transformers
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover inefficient regex patterns via performance or static analysis.
Development standards and tools can require safe regex construction and forbid known exponential patterns.
Denial-of-service protections limit resource exhaustion caused by expensive regex evaluation.
Input validation can constrain data that would otherwise trigger worst-case regex complexity.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.