CVE-2025-3777: Improper Input Validation

Hugging Face Transformers versions up to 4.49.0 are affected by an improper input validation vulnerability in the imageutils.py file. The vulnerability arises from insecure URL validation using the startswith method, which can be bypassed through URL username injection. This allows attackers to...

CVE-2025-3264: Inefficient Regular Expression Complexity

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically in the getimports function within dynamicmoduleutils.py. This vulnerability affects versions 4.49.0 and is fixed in version 4.51.0. The issue arises from a regular...

CVE-2025-3263: Inefficient Regular Expression Complexity

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically in the getconfigurationfile function within the transformers.configurationutils module. The affected version is 4.49.0, and the issue is resolved in version 4.51.0. The...

CVE-2025-3262: Inefficient Regular Expression Complexity

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the huggingface/transformers repository, specifically in version 4.49.0. The vulnerability is due to inefficient regular expression complexity in the SETTINGRE variable within the transformers/commands/chat.py file. The...

Hugging Face Transformers security vulnerabilities

Hugging Face Transformers is Hugging Face's open source advanced natural language processing for Jax, PyTorch and TensorFlow. A security vulnerability exists in Hugging Face Transformers version 4.49.0, which stems from insufficient regular expression complexity in the SETTINGRE variable in...

Hugging Face Transformers security vulnerabilities

Hugging Face Transformers is Hugging Face's open source advanced natural language processing for Jax, PyTorch and TensorFlow. A security vulnerability exists in Hugging Face Transformers version 4.49.0, which stems from insufficient regular expression complexity in the getconfigurationfile functi...

Hugging Face Transformers input validation vulnerability

Hugging Face Transformers is Hugging Face's open source advanced natural language processing for Jax, PyTorch and TensorFlow. An input validation error vulnerability exists in Hugging Face Transformers version 4.49.0 and earlier, which stems from insufficient URL validation in imageutils.py and...

Hugging Face Transformers security vulnerabilities

Hugging Face Transformers is Hugging Face's open source advanced natural language processing for Jax, PyTorch and TensorFlow. A security vulnerability exists in Hugging Face Transformers version 4.49.0, which stems from insufficient regular expression complexity in the getimports function in...

Dependacy chain attack through hijacking broken github repository at https://github.com/huggingface/transformers/blob/main/src/\ntransformers/models/fuyu/\nconvert_fuyu_model_weights_to_hf.py

Description Type: Dependency Chain Attack through hijacking broken github repository Risk: High Allows arbitrary code execution in model conversion workflows Affected Asset: https://github.com/adept-ai-labs/adept-inference Broken URL in Hugging Face Transformers Root Cause The Hugging Face...

mantik (>=0.2.0, <=0.4.2), mlflow-hf-transformers (0.4.0) +2 more potentially affected by CVE-2025-52967 via mlflow (2.2.2)

mlflow PYPI version 2.2.2 is affected by a known vulnerability. The following packages have a transitive dependency on mlflow and may be impacted: - mantik =0.2.0, =0.4.2 - mlflow-hf-transformers 0.4.0 - pyrovelocity 0.1.2 - tradex 0.0.3 Source cves: CVE-2025-52967 Source advisory: OSV:PYSEC-2025...

Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs

Adversarial examples have attracted significant attention over the years, yet understanding their frequency-based characteristics remains insufficient. In this paper, we investigate the intriguing properties of adversarial examples in the frequency domain for the image classification task, with t...

Detecting Hard-Coded Credentials in Software Repositories Via LLMs

Software developers frequently hard-code credentials such as passwords, generic secrets, private keys, and generic tokens in software repositories, even though it is strictly advised against due to the severe threat to the security of the software. These credentials create attack surfaces...

Busting the Paper Ballot: Voting Meets Adversarial Machine Learning

We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is deciding whether a mark does or does not appear on a bubble associated to an alternative in a contest on the ballot. Barrett...

PT-2025-39174

Name of the Vulnerable Software and Affected Versions huggingface/transformers versions prior to 4.53.0 Description The software is susceptible to a Regular Expression Denial of Service ReDoS within the AdamWeightDecay optimizer. The issue stems from the do use weight decay method, which handles...

Regular expression Denial of Service - ReDoS

Description A regular expression denial of service ReDoS vulnerability has been identified in the Hugging Face Transformers library's MarianTokenizer. The vulnerability exists in the removelanguagecode method of the MarianTokenizer class, which processes text to remove language codes. The method...

TRIDENT -- a Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks Using Transformers, Adversarial Learning, and Differential Privacy

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntact...

Regular expression Denial of Service - ReDoS

Description A regular expression denial of service ReDoS vulnerability has been identified in the Hugging Face Transformers library's CLVP number normalizer. The vulnerability exists in the normalizenumbers method of the EnglishNormalizer class, which converts numeric strings to their English wor...

PT-2025-37422

Name of the Vulnerable Software and Affected Versions Hugging Face Transformers versions up to 4.52.4 Description A Regular Expression Denial of Service ReDoS vulnerability exists in the normalize numbers method of the EnglishNormalizer class. This issue arises from the method's handling of numer...

Attacking Attention of Foundation Models Disrupts Downstream Tasks

Foundation models represent the most prominent and recent paradigm shift in artificial intelligence. Foundation models are large models, trained on broad data that deliver high accuracy in many downstream tasks, often without fine-tuning. For this reason, models such as CLIP , DINO or Vision...

JavelinGuard: Low-Cost Transformer Architectures for LLM Security

We present JavelinGuard, a suite of low-cost, high-performance model architectures designed for detecting malicious intent in Large Language Model LLM interactions, optimized specifically for production deployment. Recent advances in transformer architectures, including compact BERTDevlin et al...