Unlocking Pandora's Box: The Dark Side of AI Jail Breaks
Published: 2023-06-30 02:12:24

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title: Unlocking Pandora's Box: The Dark Side of AI Jail Breaks
published:  true
description: A short overview of the current models shortcomings with back-doors and why it reveals a deeper problem in AI. 
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# published_at: 2023-06-29 20:03 +0000
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> One, of the many Arms Races, has begun. I would like to think of it as a speed run towards unlocking Pandora's Box. 

As the field of AI advances so does the field of Infosec. The growing concerns surrounding the potential presence of backdoors in LLMs have raised significant questions regarding their integrity, security, and ethical implications. The Infosec industry will need to adapt to overcome some of these concerns. In this blog post, we delve into the problem of having a backdoor inside an LLM, exploring the potential risks, impact, and mitigation strategies.

## What are Back doors and Jail Breaks?
A backdoor in the context of LLMs refers to the intentional or unintentional insertion of malicious content or biases within the model's underlying parameters, leading to potentially harmful outputs. These backdoors can manifest in various forms, such as hidden triggers or explicit rules that manipulate the LLM's behavior and generate biased or malicious outputs. The presence of a backdoor undermines the trustworthiness and reliability of LLMs, as it can compromise the quality, fairness, and privacy of their generated content.

A "jailbreak" refers to the unauthorized modification or exploitation of the model's limitations or restrictions. It involves bypassing or overcoming the predefined constraints imposed on the LLM's behavior, access, or capabilities, which are typically put in place to ensure responsible and safe usage of the model. Jailbreaking an LLM can allow users to access or utilize features that are typically restricted or to make the model perform tasks beyond its intended scope.

## What's the difference? 
In short a backdoor focuses on inserting malicious content or biases into the model's parameters, while a jailbreak involves circumventing the security mechanisms of a system to gain unauthorized access and control over its functionalities. In combination these two problems can create unintended side effects such as Privacy Breaches, Harmful Information(Biowewapons etc), and could even side chain attack by the injection of malicious code into downstream applications that rely on the LLM. To put it bluntly it's not something to scoff at. It's by no means an understood area so it's basically the wild west as of late. 

## Why it matters.
We are heading towards an AI-Driven world and securing the platforms will be a necessity for global and national security. 

### Privacy Concerns 
Backdoors can compromise user privacy by allowing unauthorized access to sensitive or personal information. If an adversary gains control of an LLM's backdoor, it could lead to unintended data leaks or violations of privacy regulations.

### Adversarial Attacks
LLMs are vulnerable to adversarial attacks, where input text is strategically crafted to trigger the backdoor and manipulate the model's behavior. This poses a significant threat in security-sensitive applications such as malware detection or content filtering.

### Manipulation of Information 
A malicious actor who gains control of an LLM's backdoor could exploit it to manipulate information, disseminating false narratives, fake news, or even generating misleading financial, legal, or medical advice.

### Bias Amplification
Backdoors can exacerbate pre-existing biases present in training data, leading to discriminatory outputs. For instance, a biased backdoor might cause an LLM to generate sexist or racist content, reinforcing harmful stereotypes.