Why Prompt Injection Is a Risk to Large Language Models (LLMs)
Why Attack Large Language Models (LLMs)?
While attacking artificial intelligence (AI) systems like Large Language Models (LLMs) might seem unusual, there are several key reasons why malicious actors might target them. These attacks could be intellectually challenging, but more importantly, they present serious risks to business, security, and privacy. Here are the main reasons behind these attacks:
- Gaining Access to Sensitive Business Data
- Exploiting Personal Information for Advantage
- Manipulating AI Tools for Malicious Purposes
We’ll dive into these reasons in more detail later. First, let’s explore how a prompt works and why it’s vulnerable to exploitation.
What is a Prompt and How Does It Work in LLMs?
At its core, a prompt is a large block of text fed into a language model. It’s structured in a way to guide the model in generating responses. Understanding how a prompt works is crucial in defending against prompt injection attacks, which is the main focus of this article. 
The prompt typically has three main layers:
- System Prompt: This layer defines the model’s task and sets instructions on how it should behave, including rules about tone, politeness, and output format. For example, you might instruct the model to answer questions in a professional tone or provide structured responses.
- Context Layer: This section is used to supply additional data to the model that helps it produce more accurate and up-to-date answers. For example, businesses can include recent internal documents, or user-specific data to personalize responses.
- User Prompt: This is the most vulnerable part of the prompt. It’s the point where users interact directly with the model, and where prompt injection attacks can take place.
How Prompt Injection Works and Its Risks
Prompt injection is akin to attacks like SQL injection, where malicious input from users can compromise the system. Here’s an example of how LLM vulnerabilities can be exploited:
A Chevrolet dealership created a chatbot powered by ChatGPT for its website. However, users quickly found ways to exploit the system:
- They asked it to solve complex math equations.
- They made it agree that Tesla was superior to Chevrolet.
- One user tricked the bot into entering into a $1 car sale contract.
This happened because the chatbot simply forwarded user inputs directly to the LLM, without any safeguards. By setting strict rules within the system prompt (e.g., “only answer questions about Chevrolet”), the bot could have been protected against such attacks.
Can You Steal the System Prompt?
A major risk associated with LLMs is that attackers can often access or infer the system prompt. This opens the door for further exploitation, as the system’s behavior and logic can be manipulated by simply asking the model to reveal or summarize its own instructions. This is why securing the system prompt is essential to prevent attacks like prompt stealing.
Real-World Exploitation: The Slack Case
Prompt injection attacks are not just theoretical. In fact, real-world examples like the PromptArmor discovery in Slack show how dangerous these attacks can be. The attack involved malicious instructions being injected into public Slack channels, allowing attackers to exfiltrate sensitive data like API keys from private channels.
This real case highlights how even big companies are vulnerable to AI-driven security flaws. It’s often impossible to know when data has been stolen, as AI systems do not leave logs that would signal an attack.
How to Protect Against Prompt Injection Attacks
So, how can you protect your business from prompt injection and related vulnerabilities? Here are some strategies to mitigate risks:
- Tighten the System Prompt
The first defense against prompt injection is to clearly define and limit the instructions within the system prompt. However, this isn’t foolproof, and attackers may still find ways around it. - Use Adversarial Prompt Detectors
Adversarial prompt detectors are AI-based tools that scan and analyze user input to detect malicious prompts before they reach the LLM. Tools like Microsoft’s Prompt Shields or NVIDIA’s NeMo Guardrails can help spot and block such attacks. - Fine-Tune Your Model
By fine-tuning your LLM with custom, domain-specific data, you can make it less dependent on generic instructions, making it harder for attackers to manipulate the model through prompts. For example, a Chevrolet chatbot could be trained using specific business data, reducing reliance on generic responses.
Conclusion: Safeguarding LLMs from Prompt Injection
Prompt injection and prompt stealing pose significant risks to the security of any AI-driven system, particularly LLMs. Attackers can gain access to sensitive data, manipulate AI tools, and exploit vulnerabilities to their advantage.
To defend against these threats, companies should:
- Strengthen their system prompts.
- Implement adversarial prompt detectors.
- Fine-tune models to better handle specific use cases.
Despite these protections, there’s no completely foolproof solution to prevent prompt-based attacks. Businesses must remain vigilant and actively monitor and update their AI systems to stay ahead of new threats.
