BAIT

Name: BAIT
Availability: InStock
Author: zzw4257

Detects hidden backdoors in LLMs using inverted attack target analysis.

Updated Feb 11, 2026

Search Tags:#ai security #prompt injection #vulnerability research #llm security #backdoor detection

Ready to run

npx skills add https://github.com/zzw4257/security-skills --skill bait

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: BAIT
Source: https://github.com/zzw4257/security-skills/tree/main/skills/bait
Command: npx skills add https://github.com/zzw4257/security-skills --skill bait

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the emerging threat of backdoors embedded within Large Language Models (LLMs), enabling proactive detection and mitigation.

Core Features & Use Cases

LLM Backdoor Detection: Identifies hidden malicious functionalities within LLMs.
Inverted Attack Target: Utilizes a novel approach to uncover vulnerabilities by inverting the attack vector.
Use Case: Security researchers and AI developers can use this Skill to audit LLMs before deployment, ensuring they are free from adversarial manipulations.

Quick Start

Use the BAIT skill to scan the attached LLM model for potential backdoors.

Frequently Asked Questions about BAIT

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

FAQPage Schema

How do I detect backdoors in Large Language Models before deployment?▼

To detect backdoors in Large Language Models, you can use an inverted attack target methodology to scan for hidden malicious functionalities. This approach uncovers embedded vulnerabilities by reversing the attack vector, ensuring models are free from adversarial manipulations prior to release.

What is an inverted attack target methodology for LLM vulnerability research?▼

An inverted attack target methodology for LLM vulnerability research uncovers hidden vulnerabilities by reversing the attack vector. Instead of searching for triggers, it inverts the attack target to proactively identify embedded malicious functionalities within the model.

Can I use this approach to audit LLMs for prompt injection vulnerabilities?▼

While primarily designed for LLM backdoor detection, this approach applies to broader AI security auditing and LLM vulnerability research. It identifies hidden malicious functionalities, which complements overall security assessments for adversarial manipulations like prompt injection.

Do I need specific Python libraries to scan an LLM for backdoors?▼

Yes, you need specific Python libraries and a target LLM model for analysis to scan for backdoors. The Skill requires these dependencies to execute the inverted attack target methodology and successfully identify hidden malicious functionalities.

What is the best way for AI developers to ensure models are free from adversarial manipulations?▼

The best way to ensure models are free from adversarial manipulations is to perform LLM backdoor detection before deployment. By utilizing an inverted attack target methodology, AI developers can proactively uncover and mitigate hidden malicious functionalities.