OWASP LLM Top 10: SteveWilson PromptInjection

## Prompt Injection 

**Author(s):**

Steve Wilson

**Description:**

Prompt Injection is a vulnerability where an attacker influences the input to a Large Language Model (LLM) to guide its output in a malicious manner. This can lead to the generation of inappropriate content, disclosure of sensitive information, or unwanted system behavior.

**Labels/Tags:**

- Label: "Input Manipulation"
- Label: "Prompt Injection"

**Common Examples of Vulnerability:**

1. Example 1: An attacker provides an input that tricks the LLM into generating harmful or inappropriate content.
2. Example 2: The LLM is coaxed into generating sensitive information, such as internal details of its training data.
3. Example 3: The LLM is manipulated to output instructions that lead to unwanted system behavior, such as running malicious scripts.

**How to Prevent:**

1. Prevention Step 1: Implement robust input validation and sanitization methods to identify and filter out potential malicious inputs.
2. Prevention Step 2: Limit the exposure of sensitive information during the LLM training process.
3. Prevention Step 3: Regularly update and fine-tune the LLM to improve its ability to handle potentially harmful inputs.

**Example Attack Scenarios:**

Scenario #1: An attacker manipulates the LLM to generate inappropriate content, which is then published on a public platform, leading to reputational damage.

Scenario #2: The attacker tricks the LLM into disclosing sensitive information about its training data, which the attacker then uses for further exploits.

**Reference Links:**

1. [Prompt Injection Attacks in Chatbots](https://arxiv.org/abs/2102.07527): This paper discusses the potential security risks associated with chatbots, including prompt injection attacks.
2. [Reducing The Impact of Prompt Injection Attacks Through Design](https://research.kudelskisecurity.com/2023/05/25/reducing-the-impact-of-prompt-injection-attacks-through-design/): This blog post describes how design choices can help mitigate the impact of prompt injection attacks.

**Author Commentary (Optional):**

Prompt Injection, much like SQL Injection or Cross-Site Scripting (XSS), is a vulnerability that arises when an attacker can manipulate input to an AI model to produce harmful outputs. It's a unique challenge in the world of AI and machine learning, specifically for large language models (LLMs) that generate text based on user input.

Unlike traditional injection attacks where an attacker targets a software's code, in prompt injection, the target is an AI's learning model. The attacker aims to manipulate the model's output to generate harmful or misleading content. This could include generating illegal content, revealing sensitive information, or promoting harmful behavior.

The severity of prompt injection attacks can range from low to high, depending on the application of the AI. For a benign AI application, the risk might be limited to generating inappropriate or off-topic responses. However, for an AI used in critical applications or dealing with sensitive data, the risks could be far more severe, such as data leakage, misinformation, or harmful actions.

Addressing prompt injection attacks involves both technical and policy approaches. On the technical side, rate limiting requests to the AI can help prevent mass exploitation. Alert lists can be used to detect and block unsafe content. More secure model designs can also help. On the policy side, AI governance, including robust testing, auditing, and monitoring, is vital. User awareness and education about potential AI manipulation should also be emphasized.

As AI and machine learning become more pervasive, the threat of prompt injection will likely increase. Security professionals will need to be proactive in understanding the unique vulnerabilities that AI introduces and developing strategies to mitigate them. Future iterations of the OWASP Top 10 will likely need to consider AI-specific threats like prompt injection more prominently.

The cybersecurity community has recognized the threat of prompt injection, and researchers are actively working on mitigation strategies. These efforts will be crucial in shaping the future of AI security.

The aim of including Prompt Injection in the OWASP Top 10 is to raise awareness of this emerging threat in the cybersecurity community. As with any security vulnerability, understanding the threat is the first step in mitigating it. With the rising prevalence of AI and machine learning technologies, it's more important than ever to be vigilant about new types of attacks and vulnerabilities.