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TensorAbuse

Detect TensorFlow API abuse in machine learning models.

Updated Feb 11, 2026
Search Tags:#tensorflow#vulnerability detection#ai security#supply chain#ml security#model security
One-click install
npx skills add https://github.com/zzw4257/security-skills --skill tensorabuse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: TensorAbuse
Source: https://github.com/zzw4257/security-skills/tree/main/skills/tensor-abuse
Command: npx skills add https://github.com/zzw4257/security-skills --skill tensorabuse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps identify and mitigate risks associated with supply chain attacks targeting machine learning models, specifically those leveraging TensorFlow API abuse.

Core Features & Use Cases

  • Model Security Analysis: Analyzes ML models for vulnerabilities related to supply chain compromises.
  • TensorFlow API Abuse Detection: Identifies malicious patterns in model development or deployment that exploit TensorFlow functionalities.
  • Use Case: A security engineer can use this Skill to scan a newly integrated TensorFlow model to ensure it hasn't been tampered with or doesn't contain hidden malicious logic before deploying it into production.

Quick Start

Run the TensorAbuse skill to scan the current directory for potential supply chain vulnerabilities.

Frequently Asked Questions about TensorAbuse

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect TensorFlow supply chain attacks in my ML models?▼

Detect TensorFlow supply chain attacks by scanning your models for malicious patterns and API abuse. This Skill analyzes machine learning model security to identify hidden vulnerabilities or tampered logic before production deployment.

What is TensorFlow API abuse in machine learning security?▼

TensorFlow API abuse is a supply chain attack vector where malicious logic exploits TensorFlow functionalities within an ML model. It compromises model integrity by injecting hidden vulnerabilities during development or deployment phases.

Do I need Python 3.10 to scan for ML model vulnerabilities?▼

Yes, you need Python 3.10 or higher to run in-depth TensorFlow API abuse analysis. Specific TensorFlow libraries are also required to perform machine learning model security assessments and pre-deployment vulnerability checks.

How do I run a pre-deployment vulnerability check for TensorFlow models?▼

Run a pre-deployment vulnerability check by executing the Skill to scan your current directory. It analyzes the machine learning model for supply chain compromises to ensure it has not been tampered with before production integration.

What is the best way to secure machine learning models against supply chain risks?▼

Securing machine learning models against supply chain risks involves analyzing TensorFlow API abuse to detect malicious patterns. Running a pre-deployment vulnerability check ensures newly integrated models are free from hidden malicious logic.

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