The Hidden Dangers of Connecting Large Language Models to the world

As artificial intelligence continues to evolve, we are witnessing the rise of large language models (LLMs) like ChatGPT, which have become increasingly sophisticated and useful. But did you know that connecting these powerful LLMs to other applications can pose potential security risks? Let’s dive into the hidden dangers of connecting LLMs to other applications and uncover some of the latest attack methods hackers are using, providing specific examples of such threats and explaining the underlying concepts clearly.

Attack Vectors and Techniques

As an LLM user, you might be surprised to learn that there are various ways hackers can exploit these systems, putting you and your data at risk. Some of these attack vectors and techniques include:

A. Remote control of LLMs

Imagine a hacker creating a webpage with a sneaky payload. Once an LLM processes it, the hacker gains control over the LLM’s responses. This method allows the attacker to remotely control the LLM without the user’s knowledge.

This could include anything that scans the internet or uses website data for its work. For example bing bot or bard.

B. Leaking/exfiltrating user data

Using carefully crafted email messages, attackers can trick LLMs into spreading harmful injections to other LLMs, ultimately exfiltrating or changing user data without their knowledge. For instance, an attacker might send an email with a hidden payload that exploits a vulnerability in the LLM’s text processing, causing the LLM to inadvertently leak sensitive information.

This one will probably be popular against email response systems. Like for cold outreach, personal calendar systems and support systems.

C. Persistent compromise across sessions

Ever heard of a digital cockroach? That’s what we call a malicious payload that burrows into an LLM’s internal data structures. It’s so sneaky that it stays in control even after the LLM is rebooted or purged. If you want to know more about these I recommend you read the paper directly as its a complex one.

D. Spread injections to other LLMs

Attackers can create small, hidden injections that can spread to other LLMs, effectively creating a network of compromised LLMs. For example, an attacker might embed a malicious payload in a popular online forum, which would then be processed by various LLMs as they analyze the content of the forum, spreading the infection.

E. Compromising LLMs with tiny multi-stage payloads

LLMs can be compromised with small payloads that trigger the LLM to fetch a larger, more harmful payload, all without the user’s knowledge. An example of this is an attacker hiding a small payload in a seemingly innocuous text that, when processed by the LLM, causes it to download a more extensive, more dangerous payload from an external source.


Researchers have discovered that prompt injections (malicious code inserted into LLM responses) can be as powerful as arbitrary code execution, allowing attackers to take control of an LLM. Indirect prompt injections, a new and more powerful method of delivering injections, pose a significant threat to LLM security. In an indirect prompt injection, the attacker hides malicious code within a seemingly innocuous prompt, which is then processed by the LLM, executing the hidden payload without the user’s knowledge. This technique can be used to compromise LLMs and cause them to execute arbitrary code, making them

a valuable tool for attackers seeking to gain unauthorized access to computer systems or exfiltrate sensitive information.


Connecting LLMs to other applications can have critical security implications. The research presented here demonstrates a variety of new attack vectors and methods that significantly raise the stakes of deploying these models. To keep yourself safe while using LLMs, it’s essential to be aware of the potential threats and understand how to protect against them. Companies and organizations should implement robust security protocols and continuously monitor their LLMs for signs of compromise, including unexpected behaviors, unusual network activity, and unexplained resource utilization.

Researchers and developers need to keep pushing the boundaries, exploring cutting-edge techniques like AI-driven threat detection and secure-by-design system architectures to improve LLM security and fend off attacks. Ultimately, the safe and responsible deployment of LLMs requires a collaborative effort between industry, academia, and government stakeholders to ensure that these powerful tools are used for the benefit of society while minimizing the risks associated with their use. By working together, we can harness the full potential of LLMs and create a safer digital world for everyone.







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