Blog Article

LLM Threat Vector: Prompt Injection

Arnav Bathla

8 min read

In the rapidly evolving landscape of artificial intelligence (AI), Large Language Models (LLMs) like OpenAI's GPT series have become central to a myriad of applications, ranging from content generation to customer support and beyond. However, as these models are increasingly integrated into enterprise applications, a unique and sophisticated security challenge emerges: prompt injection attacks. This blog post delves into what prompt injection attacks are, their implications for Application Security (AppSec), and strategies to mitigate these risks effectively.

What are Prompt Injection Attacks?

Prompt injection attacks occur when an attacker manipulates the input to an LLM in a way that causes the model to generate outputs that serve the attacker's objectives. This can include generating misleading information, exposing sensitive data, or executing commands that should be restricted. These attacks exploit the flexible and generative nature of LLMs, turning their strength into a potential vulnerability.

The Mechanics of a Prompt Injection Attack

There are 3 types of Prompt Injection attacks:

  1. Direct Prompt Injection: In the context of Direct Prompt Injections or "jailbreaking," an attacker directly changes or exposes the system's foundational command prompt. This manipulation can grant the attacker the ability to misuse backend systems by leveraging unprotected functionalities and accessing data repositories through the LLM.

  2. Indirect Prompt Injection: Indirect Prompt Injections involve the LLM's processing of tampered inputs from external origins, such as manipulated web content or document files, which are under the control of an attacker. By embedding malicious commands within these inputs, the attacker can steer the LLM's behavior. This method turns the LLM into a "confused deputy," misused to mislead users or to meddle with other systems that the LLM interacts with. It's noteworthy that for indirect prompt injections, the malicious inputs do not require to be directly perceivable or understandable by humans, as long as they are decipherable by the LLM.

  3. Trojan Prompts: Embedding hidden triggers or commands within a normal-looking text that activate specific model behaviors or responses when processed.

Video Example of Direct and Indirect Prompt Injection

Below is an educational video for cybersecurity professionals, developers, and companies building LLM apps using a retrieval system that exposes their company documents.

Example of Direct Prompt Injection:

Here's an example of indirect prompt injection:

Implications for Application Security

The implications of prompt injection attacks for AppSec are profound. As applications increasingly rely on LLMs for processing natural language input, the attack surface broadens, encompassing not just traditional web application vulnerabilities but also the novel vectors introduced by LLM integration. These can include:

  • Data Leakage: Exposing sensitive information through manipulated model outputs.

  • Unauthorized Actions: Causing the LLM to perform or suggest actions that compromise security.

  • System Compromise: In scenarios where LLM outputs can influence system behavior, prompt injections could lead to more severe security breaches.

Mitigating Prompt Injection Attacks

Mitigating prompt injection attacks requires a multifaceted approach, focusing on both preventive measures and detection strategies. Here are key strategies to consider:

Input Validation and Sanitization

This is where you can use providers like Layerup. You can do:

  • Structural Validation: Ensuring that inputs conform to expected patterns and rejecting anomalous inputs.

  • Content-Based Filtering: Applying filters to remove or neutralize potentially malicious content within inputs, including known patterns that could trigger unintended LLM responses.

  • Block API calls if necessary: If there's a suspicion of prompt injection, the API call is stopped and you're alerted.

Secure Model Training Practices

The way an LLM is trained can influence its susceptibility to prompt injections. Practices such as:

  • Adversarial Training: Including examples of prompt injection attacks in the training data can help the model learn to recognize and resist such manipulations.

  • Data Privacy Measures: Ensuring that the model's training data does not contain sensitive information can reduce the risk of data leakage.

  • Avoid using general tools such as web search: Tools is a powerful way to give LLMs a superpower to perform tasks. But, using search APIs like Exa can lead you to a website that might have an embedded code or prompt that can be injected.

Monitoring and Anomaly Detection

Implementing monitoring solutions that can detect unusual patterns in LLM inputs or outputs can help identify potential prompt injection attacks in real-time. This involves:

  • Behavioral Analytics: Analyzing the patterns of inputs and outputs over time to identify deviations that could indicate an attack.

  • Anomaly Detection Systems: Employing machine learning or rule-based systems to flag activities that fall outside of normal operational parameters.

User Education and Awareness

Educating users about the potential risks associated with interacting with LLM-powered applications and encouraging safe practices can play a significant role in mitigating risks.


As LLMs continue to be adopted across various sectors, understanding and addressing the unique security challenges they introduce, such as prompt injection attacks, is crucial. By implementing comprehensive security measures, including rigorous input validation, secure model training, and active monitoring, organizations can safeguard their applications against these sophisticated attacks. Staying informed and proactive is key to securing the future of AI-driven applications. Subscribe to our newsletter today to keep yourself up-to-date with the evolving space of security x LLMs. Contact us if you're looking to set up an LLM security solution at your company.

Application Security for Generative AI


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