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CVE Vulnerability Database
Vulnerability Database/CVE-2026-4500

CVE-2026-4500: bagofwords RCE Vulnerability

CVE-2026-4500 is a remote code execution flaw in bagofwords library affecting versions up to 0.0.297. The vulnerability allows remote attackers to execute arbitrary code. This article covers technical details, impact, and mitigation.

Published: March 27, 2026

CVE-2026-4500 Overview

A code injection vulnerability has been identified in bagofwords1 bagofwords versions up to 0.0.297. This vulnerability impacts the generate_df function located in the file backend/app/ai/code_execution/code_execution.py. An attacker can exploit this flaw through specially crafted input that leads to injection attacks. The vulnerability can be exploited remotely by authenticated users, potentially allowing arbitrary code execution on the target system.

Critical Impact

Remote attackers with low privileges can exploit the injection vulnerability in the code execution module to manipulate application behavior, potentially leading to unauthorized data access and system compromise.

Affected Products

  • bagofwords1 bagofwords versions up to 0.0.297
  • Systems utilizing the code_execution.py module
  • Applications integrating the bagofwords AI backend

Discovery Timeline

  • March 20, 2026 - CVE-2026-4500 published to NVD
  • March 23, 2026 - Last updated in NVD database

Technical Details for CVE-2026-4500

Vulnerability Analysis

This vulnerability is classified as CWE-74 (Improper Neutralization of Special Elements in Output Used by a Downstream Component - Injection). The flaw exists within the generate_df function in the code execution module of the bagofwords application. The function fails to properly sanitize or validate user-supplied input before processing, allowing malicious payloads to be injected and executed.

The vulnerability affects the AI code execution backend, which is a particularly sensitive component as it handles dynamic code processing. Exploitation requires network access and low-level privileges, making it accessible to authenticated users who may have limited permissions within the application.

Root Cause

The root cause of this vulnerability lies in insufficient input validation within the generate_df function. The function processes external input without adequate sanitization, allowing injection payloads to be interpreted as executable code or commands. This represents a fundamental failure in the application's input handling mechanisms, where untrusted data flows directly into sensitive code execution pathways without proper neutralization of special characters or command sequences.

Attack Vector

The attack can be launched remotely over the network. An authenticated attacker with low privileges can craft malicious input targeting the generate_df function in backend/app/ai/code_execution/code_execution.py. The exploitation requires no user interaction beyond the attacker submitting the malicious payload. Upon successful exploitation, the attacker may achieve limited impact on confidentiality, integrity, and availability of the affected system.

The vulnerability mechanism involves passing specially crafted data to the code execution module. Technical details and proof-of-concept information can be found in the GitHub RCE Proof of Concept and related GitHub Issue Discussion.

Detection Methods for CVE-2026-4500

Indicators of Compromise

  • Unusual or malformed requests targeting the /ai/code_execution/ endpoint or related API paths
  • Unexpected process spawning or command execution originating from the bagofwords application
  • Log entries showing injection-style patterns in parameters processed by generate_df
  • Anomalous data access patterns or unauthorized file operations following code execution requests

Detection Strategies

  • Monitor application logs for requests containing injection payloads targeting the code execution module
  • Implement Web Application Firewall (WAF) rules to detect common injection patterns in API requests
  • Deploy runtime application self-protection (RASP) to detect and block injection attempts in real-time
  • Configure intrusion detection systems to alert on suspicious activity patterns associated with code injection

Monitoring Recommendations

  • Enable verbose logging for the backend/app/ai/code_execution/ module to capture detailed request data
  • Set up alerts for any execution anomalies or unexpected behavior from the bagofwords application process
  • Monitor system calls and process creation events from the application server
  • Review access logs regularly for patterns indicating reconnaissance or exploitation attempts

How to Mitigate CVE-2026-4500

Immediate Actions Required

  • Upgrade bagofwords to version 0.0.298 or later immediately
  • Review application logs for any signs of exploitation prior to patching
  • Implement network segmentation to limit exposure of affected systems
  • Apply input validation controls at the perimeter while planning the upgrade

Patch Information

The vulnerability has been addressed in bagofwords version 0.0.298. The fix is available in commit 47b20bcda31264635faff7f6b1c8095abe1861c6. Organizations should upgrade to this version or later to remediate the vulnerability.

For detailed patch information, refer to:

  • GitHub Commit with Fix
  • GitHub Release Version 0.0.298
  • GitHub Pull Request Review

Workarounds

  • Restrict network access to the code execution module to trusted internal networks only
  • Implement strict input validation at the application gateway or reverse proxy level
  • Disable or limit access to the affected generate_df functionality until patching is complete
  • Deploy additional authentication controls to limit which users can access the code execution features
bash
# Example: Restrict access to the code execution endpoint using nginx
location /ai/code_execution/ {
    # Allow only trusted internal networks
    allow 10.0.0.0/8;
    allow 192.168.0.0/16;
    deny all;
    
    # Additional rate limiting
    limit_req zone=code_exec burst=5 nodelay;
}

Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

  • Vulnerability Details
  • TypeRCE

  • Vendor/TechBagofwords

  • SeverityMEDIUM

  • CVSS Score5.3

  • EPSS Probability0.06%

  • Known ExploitedNo
  • CVSS Vector
  • CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
  • Impact Assessment
  • ConfidentialityLow
  • IntegrityNone
  • AvailabilityLow
  • CWE References
  • CWE-74
  • Technical References
  • GitHub RCE Proof of Concept

  • GitHub Bag of Words Repository

  • GitHub Commit History

  • GitHub Issue Discussion

  • GitHub Pull Request Review

  • GitHub Release Version 0.0.298

  • VulDB CTI ID #352065

  • VulDB Vulnerability ID #352065

  • VulDB Submission ID #773890
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