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...

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...

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...

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...

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...

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...

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...

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...

Security Bulletin: IBM Watson Speech Services Cartridge v4.8.8 is vulnerable to Remote Code Execution in Transformers [CVE-2024-11392, CVE-2024-11393, CVE-2024-11394]

Summary IBM Watson Speech Services Cartridge v4.8.8 is vulnerable to Remote Code Execution in Transformers, due to a lack of proper validation of user-supplied dataCVE-2024-11392, CVE-2024-11393, CVE-2024-11394. This vulnerabilitiy has been addressed. Please read the details for remediation below...

Security Bulletin: IBM Maximo Application Suite - Monitor Component is vulnerable to transformers-4.46.3-py3-none-any.whl CVE-2025-1194

Summary IBM Maximo Application Suite - Monitor Component is vulnerable to transformers-4.46.3-py3-none-any.whl CVE-2025-1194. This bulletin identifies the steps to take to address the vulnerabilities. Vulnerability Details CVEID:CVE-2025-1194 DESCRIPTION: A Regular Expression Denial of Service...

Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook

The rise of hardware-level security threats, such as side-channel attacks, hardware Trojans, and firmware vulnerabilities, demands advanced detection mechanisms that are more intelligent and adaptive. Traditional methods often fall short in addressing the complexity and evasiveness of modern...

Deserialization Of Untrusted Data

transformers is vulnerable to Deserialization Of Untrusted Data. The vulnerability is due to improper validation of user-supplied data during the parsing of model files, which allows deserialization of untrusted data...

CVE-2023-2800

Insecure Temporary File in GitHub repository huggingface/transformers prior to 4.30.0...

CVE-2023-6730

Deserialization of Untrusted Data in GitHub repository huggingface/transformers prior to 4.36...

Regular Expression Denial Of Service (ReDoS)

Transformers is vulnerable to Regular Expression Denial of Service ReDoS. The vulnerability is due to inefficient regular expression processing due to nested quantifiers in the preprocessstring function of transformers.testingutils, which can cause exponential backtracking and high CPU usage when...

Hugging Face Transformers Regular Expression Denial of Service

A Regular Expression Denial of Service ReDoS exists in the preprocessstring function of the transformers.testingutils module. In versions before 4.50.0, the regex used to process code blocks in docstrings contains nested quantifiers that can trigger catastrophic backtracking when given inputs wit...

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 preprocessstring function in the transformers.testingutils module. An attacker can cause high CPU usa...