Hugging Face Transformers vulnerable to Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer

The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service ReDoS in the AdamWeightDecay optimizer. The vulnerability arises from the douseweightdecay method, which processes user-controlled regular expressions in the includeinweightdecay...

CVE-2025-6921

The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service ReDoS in the AdamWeightDecay optimizer. The vulnerability arises from the douseweightdecay method, which processes user-controlled regular expressions in the includeinweightdecay...

CVE-2025-6921 Regular Expression Denial of Service (ReDoS) in huggingface/transformers

The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service ReDoS in the AdamWeightDecay optimizer. The vulnerability arises from the douseweightdecay method, which processes user-controlled regular expressions in the includeinweightdecay...

CVE-2025-6921

CVE-2025-6921 affects the huggingface/transformers library prior to 4.53.0, causing a Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer via unsafe handling in _do_use_weight_decay of include_in_weight_decay/exclude_from_weight_decay. IBM Maximo Application Suite Monito...

CVE-2025-6921 Regular Expression Denial of Service (ReDoS) in huggingface/transformers

The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service ReDoS in the AdamWeightDecay optimizer. The vulnerability arises from the douseweightdecay method, which processes user-controlled regular expressions in the includeinweightdecay...

transformers 资源管理错误漏洞

transformers is a Hugging Face open source application for machine learning. A resource management error vulnerability exists in transformers versions prior to 4.53.0, which stems from a flaw in the douseweightdecay method's handling of user-controlled regular expressions, which could lead to a...

Defending against Stegomalware in Deep Neural Networks with Permutation Symmetry

Deep neural networks are being utilized in a growing number of applications, both in production systems and for personal use. Network checkpoints are as a consequence often shared and distributed on various platforms to ease the development process. This work considers the threat of neural networ...

Regular Expression Denial Of Service (ReDoS)

Hugging Face Transformers is vulnerable to Regular Expression Denial of Service ReDoS. The vulnerability is due to the use of a regex pattern /^/^// in the converttfweightnametoptweightname function, which allows attackers to craft malicious input strings causing catastrophic backtracking and...

Weight loss scams, or why ‘Jodie Foster’ wants me to lose weight

It seems like it's hard to move on social media without some kind of mention of weight-loss injections these days. And, sure, these drugs can have a positive affect for many people, but not all these cases of weight loss are real, nor are the people promoting them who they say they are. Weight-lo...

Regular Expression Denial of Service (ReDoS)

Overview transformers is a State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow Affected versions of this package are vulnerable to Regular Expression Denial of Service ReDoS via the converttfweightnametoptweightname function. An attacker can cause excessive CPU consumption and disru...

Hugging Face Transformers Regular Expression Denial of Service (ReDoS) vulnerability

A Regular Expression Denial of Service ReDoS vulnerability exists in the Hugging Face Transformers library, specifically in the converttfweightnametoptweightname function. This function, responsible for converting TensorFlow weight names to PyTorch format, uses a regex pattern /^/^// that can be...

CVE-2025-5197

The CVE-2025-5197 ReDoS vulnerability affects Hugging Face Transformers in the convert_tf_weight_name_to_pt_weight_name() function, where the regex /[^/]___([^/] )/ can cause excessive CPU usage via catastrophic backtracking. Affected versions: up to 4.51.3, with a fix in 4.53.0. Practical impact...

PT-2025-32158

Name of the Vulnerable Software and Affected Versions Hugging Face Transformers versions up to 4.51.3 Description A Regular Expression Denial of Service ReDoS vulnerability exists in the convert tf weight name to pt weight name function of the Hugging Face Transformers library. This function, whi...

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage

Model compression is crucial for minimizing memory storage and accelerating inference in deep learning DL models, including recent foundation models like large language models LLMs. Users can access different compressed model versions according to their resources and budget. However, while existi...

Multi-Trigger Poisoning Amplifies Backdoor Vulnerabilities in LLMs

Recent studies have shown that Large Language Models LLMs are vulnerable to data poisoning attacks, where malicious training examples embed hidden behaviours triggered by specific input patterns. However, most existing works assume a phrase and focus on the attack's effectiveness, offering limite...

Generalized and Unified Equivalences between Hardness and Pseudoentropy

Pseudoentropy characterizations provide a quantitatively precise demonstration of the close relationship between computational hardness and computational randomness. We prove a unified pseudoentropy characterization that generalizes and strengthens previous results for both uniform and non-unifor...

Cyclic Equalizability of Words and Its Application to Card-Based Cryptography

Card-based cryptography is a research area to implement cryptographic procedures using a deck of physical cards. In recent years, it has been found to be related to finite group theory and algebraic combinatorics, and is becoming more and more closely connected to the field of mathematics. In thi...

Towards Provable (In)Secure Model Weight Release Schemes

Recent secure weight release schemes claim to enable open-source model distribution while protecting model ownership and preventing misuse. However, these approaches lack rigorous security foundations and provide only informal security guarantees. Inspired by established works in cryptography, we...

New Characterization of Full Weight Spectrum One-Orbit Cyclic Subspace Codes

In this paper, we determine the weight distributions of a family of FWS codes and exhibit some equivalence classes of FWS codes under certain conditions. Furthermore, we provide a complete classification for $r$-FWS codes...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...