CVE-2025-3262: Huggingface Transformers DoS Vulnerability

CVE-2025-3262 Overview

A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers repository, specifically affecting version 4.49.0. The vulnerability exists due to inefficient regular expression complexity in the SETTING_RE variable within the transformers/commands/chat.py file. The regex contains repetition groups and non-optimized quantifiers, leading to exponential backtracking when processing 'almost matching' payloads. This can degrade application performance and potentially result in a denial-of-service (DoS) when handling specially crafted input strings.

Critical Impact

Attackers can exploit this ReDoS vulnerability to cause severe application performance degradation or complete denial of service by sending specially crafted input strings that trigger exponential regex backtracking, affecting AI/ML pipelines that depend on the Transformers library.

Affected Products

• Hugging Face Transformers versions prior to 4.51.0
• Hugging Face Transformers version 4.49.0 (confirmed vulnerable)
• Applications using the transformers/commands/chat.py module

Discovery Timeline

• 2025-07-07 - CVE-2025-3262 published to NVD
• 2025-08-02 - Last updated in NVD database

Technical Details for CVE-2025-3262

Vulnerability Analysis

This vulnerability falls under CWE-1333 (Inefficient Regular Expression Complexity), a category of algorithmic complexity attacks. The flaw resides in the SETTING_RE regular expression pattern within the chat command module of the Transformers library.

Regular expression engines using backtracking algorithms are susceptible to ReDoS when patterns contain nested quantifiers or overlapping alternations. When an attacker provides a carefully constructed input string that "almost matches" the pattern, the regex engine enters a state of exponential backtracking, attempting numerous permutations before ultimately failing to match. This behavior can consume significant CPU resources, effectively halting the application.

The vulnerability is exploitable over the network without requiring authentication or user interaction. While the attack does not compromise data confidentiality or integrity, it can render affected systems unresponsive, disrupting AI/ML workflows and dependent services.

Root Cause

The root cause is an inefficiently designed regular expression pattern in the SETTING_RE variable located in transformers/commands/chat.py. The pattern contains repetition groups and non-optimized quantifiers that create multiple paths for the regex engine to explore. When processing input strings designed to exploit this weakness, the engine experiences catastrophic backtracking, where the time complexity grows exponentially with input length.

Attack Vector

The attack can be executed remotely by providing specially crafted input strings to any application component that processes user input through the vulnerable regex pattern. Attack scenarios include:

1. Direct API calls - Sending malicious payloads to endpoints that utilize the chat command functionality
2. Crafted model inputs - Embedding ReDoS payloads in text that gets processed by the chat module
3. Automated attacks - Scripted requests with progressively longer malicious strings to maximize CPU exhaustion

The following code shows the security patch released in version 4.51.0:

 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.51.0.dev0")
+check_min_version("4.51.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

Source: GitHub Commit

Detection Methods for CVE-2025-3262

Indicators of Compromise

• Abnormally high CPU utilization on servers running Transformers-based applications
• Unusual request patterns with long or repetitive string inputs to chat-related endpoints
• Application timeouts or unresponsiveness during text processing operations
• Log entries showing extended processing times for regex operations in chat.py

Detection Strategies

• Monitor CPU usage patterns for sustained spikes correlating with incoming requests
• Implement request logging to identify payloads with repetitive character sequences typical of ReDoS attacks
• Deploy application performance monitoring (APM) to detect regex processing anomalies
• Audit application dependencies to identify vulnerable Transformers versions using software composition analysis (SCA) tools

Monitoring Recommendations

• Set up alerts for CPU exhaustion events on systems running Transformers library components
• Implement request rate limiting and payload size restrictions on exposed endpoints
• Configure timeout thresholds for regex operations to prevent indefinite processing
• Enable detailed logging for the transformers/commands/chat.py module to capture suspicious input patterns

How to Mitigate CVE-2025-3262

Immediate Actions Required

• Upgrade Hugging Face Transformers to version 4.51.0 or later immediately
• Audit all applications and ML pipelines using the Transformers library to identify vulnerable deployments
• Implement input validation to limit the length and complexity of strings processed by the chat module
• Deploy web application firewalls (WAF) with ReDoS protection rules where applicable

Patch Information

The vulnerability has been fixed in Hugging Face Transformers version 4.51.0. The patch addresses the inefficient regular expression pattern in transformers/commands/chat.py by optimizing the SETTING_RE variable to prevent catastrophic backtracking. Users should upgrade immediately using pip:

pip install --upgrade transformers>=4.51.0

The fix commit is available at the Hugging Face Transformers GitHub repository. Additional details about the vulnerability discovery can be found in the Huntr bounty listing.

Workarounds

• Implement input length limits on all user-controlled strings before they reach the vulnerable regex pattern
• Add request timeout mechanisms to terminate long-running regex operations
• Deploy rate limiting to reduce the impact of automated ReDoS attacks
• Consider running Transformers workloads in isolated containers with resource limits to contain potential DoS impact

# Upgrade Transformers to patched version
pip install transformers>=4.51.0

# Verify installed version
python -c "import transformers; print(transformers.__version__)"

# For production environments, pin to specific patched version
pip install transformers==4.51.0