The OWASP Top 10 for Large Language Models Explained for CISOs: Part 1

Introduction

The OWASP Top 10 for Large Language Model (LLM) Applications is a crucial resource that outlines the most significant security vulnerabilities associated with the use of LLMs in various applications. As organizations increasingly adopt these advanced AI technologies, understanding and addressing these vulnerabilities becomes essential for Chief Information Security Officers (CISOs) and their teams.

Overview of the OWASP Top 10 for LLM Applications

The OWASP Top 10 for LLM Applications highlights ten critical security risks that developers and security professionals must consider when integrating LLMs into their systems. This list serves as a guide to help organizations identify potential weaknesses in their applications and implement effective security measures. The vulnerabilities range from issues like prompt injection, where attackers manipulate inputs to exploit the model, to insecure output handling that can lead to unintended data exposure.

Importance for CISOs

For CISOs, the OWASP Top 10 is not just a checklist; it represents a framework for understanding the unique challenges posed by LLMs. By familiarizing themselves with the OWASP Top 10, CISOs can better prepare their organizations to mitigate risks associated with LLMs, ensuring that their deployment is both safe and effective.

Also Read: The Essential Guide to AI Red Teaming in 2024

Understanding the OWASP Top 10 for LLM Applications

1. Prompt Injection

Definition and Impact of Prompt Injection Attacks on LLMs

Prompt injection is a type of attack where an individual manipulates the input given to a large language model (LLM) to produce unintended or harmful outputs. This can occur when the attacker crafts a prompt that tricks the model into executing unauthorized actions or revealing sensitive information. The impact of such attacks can be severe, leading to data breaches, compromised decision-making, and a loss of trust in automated systems.

Prompt Injection is commonly referred to as AI jailbreaking. Read more about the techniques and safeguards against prompt injections here and join these AI jailbreaking communities to stay updated on the latest trends.

Examples of How Attackers Can Manipulate LLMs Through Crafted Inputs

There are two main types of prompt injection: direct and indirect.

1. Direct Prompt Injection: In this scenario, an attacker directly interacts with the model by submitting a crafted prompt. For example, an attacker might input, "Summarize this document and send it to my email." If the LLM processes both instructions without proper checks, it could inadvertently send sensitive information to unauthorized recipients.

2. Indirect Prompt Injection: This method is subtler and involves manipulating external sources that influence the LLM's behavior. For instance, if an attacker embeds a malicious prompt within a web page, when a user interacts with that page and requests a summary from the LLM, it could trigger unauthorized actions based on the hidden prompt. This could lead to situations where the model generates harmful content or executes commands that compromise security. Read how RAG poisoning made Llama 3 racist.

2. Insecure Output Handling

Importance of Validating, Sanitizing, and Handling LLM Outputs Before Passing Them Downstream

Insecure output handling refers to the failure to properly validate and sanitize outputs generated by LLMs before they are used in other systems or presented to users. This step is crucial because LLM outputs can contain harmful content or executable code that can lead to security vulnerabilities. By ensuring that outputs are thoroughly checked and cleaned, organizations can prevent potential exploits that could arise from blindly trusting the model's responses.

Potential Consequences of Insecure Output Handling

When LLM outputs are not adequately handled, several security risks emerge:

• Cross-Site Scripting (XSS): If an LLM generates JavaScript code without proper sanitization and this code is executed in a user's browser, it can lead to XSS attacks. This could allow attackers to steal sensitive information or hijack user sessions.

• Remote Code Execution: Unsanitized output might be passed directly into system functions that execute commands. For example, if an LLM generates a command that deletes files or alters system settings without validation, it can lead to significant operational disruptions and data loss.

• Data Exposure: Insecure handling of outputs may also result in sensitive information being inadvertently exposed to unauthorized users. If an LLM generates output containing confidential data and this output is not properly filtered before being shared, it could lead to severe privacy breaches.

By recognizing these risks and implementing robust validation and sanitization processes for LLM outputs, organizations can significantly enhance their security posture and protect against potential attacks stemming from both prompt injection and insecure output handling.

3. Training Data Poisoning

Risks Associated with Manipulated Pre-Training Data or Fine-Tuning Data

Training data poisoning occurs when an attacker deliberately introduces harmful or misleading data into the dataset used to train a large language model (LLM). This can happen during the initial pre-training phase or later during fine-tuning, where the model is adjusted based on specific datasets for particular tasks. The risks associated with this type of attack are significant. If the training data is compromised, the LLM may learn incorrect patterns, biases, or even malicious instructions. This can lead to the model generating harmful outputs, making incorrect decisions, or failing to perform its intended tasks effectively.

For example, if an attacker inserts false information into a dataset that is used to train an LLM for medical advice, the model might provide dangerous health recommendations. Similarly, if biased data is included in the training set, it could lead to outputs that reinforce stereotypes or discrimination.

How Poisoned Training Data Can Introduce Vulnerabilities into LLM Models

When training data is poisoned, it can create vulnerabilities that are difficult to detect. The LLM might not show immediate signs of being compromised; instead, it may only produce harmful outputs under specific conditions or when prompted in a certain way. This stealthy nature makes it challenging for developers and security teams to identify and rectify issues before they lead to real-world consequences.

For instance, a model trained on biased data might only reveal its problematic behavior when asked about certain topics. If users rely on such a model for critical tasks—like legal advice or customer service—the results could be damaging. Furthermore, poisoned data can also be used to create backdoors in the model, allowing attackers to manipulate its behavior at will.

4. Model Denial of Service

Attackers Overloading LLMs with Resource-Heavy Operations

Model Denial of Service (DoS) attacks target the operational capacity of large language models by overwhelming them with excessive requests or complex inputs. These attacks exploit the resource-intensive nature of LLMs, which require significant computational power and memory to function effectively. When an attacker floods the model with too many requests or particularly demanding queries, it can slow down processing times or even cause the system to crash entirely.

For example, an attacker might send a series of complex prompts designed to exhaust the model's resources. As the model struggles to handle this influx of requests, legitimate users may experience delays or complete unavailability of the service. This not only disrupts normal operations but can also lead to increased costs for organizations relying on cloud-based services that charge based on resource usage.

Mitigating Denial of Service Attacks on LLMs

To protect against Model DoS attacks, organizations can implement several strategies:

1. Input Validation and Sanitization: Ensuring that all incoming requests meet specific criteria can help filter out malicious inputs before they reach the LLM. This includes checking for size limits and content appropriateness.

2. Rate Limiting: By controlling how many requests a single user or IP address can make within a certain timeframe, organizations can prevent any one source from overwhelming the system.

3. Resource Management: Setting caps on how much computational power each request can use helps prevent any single operation from draining resources excessively.

4. Monitoring Traffic Patterns: Continuously observing incoming requests allows teams to identify unusual spikes in activity that may indicate an ongoing attack.

5. Implementing Fallback Mechanisms: Having backup systems in place ensures that even if one part of the service becomes unavailable due to an attack, other functionalities can continue operating smoothly.

By proactively addressing these vulnerabilities and implementing robust security measures, organizations can better protect their LLMs from both training data poisoning and denial of service attacks, ensuring reliable and safe operation in their applications.

5. Supply Chain Vulnerabilities

Risks in the LLM Supply Chain

The supply chain for large language models (LLMs) encompasses all the components and processes involved in developing, deploying, and maintaining these AI systems. This includes the collection of training data, the algorithms used to build the models, the pre-trained models themselves, and the platforms where they are deployed. Each of these elements can introduce vulnerabilities that may be exploited by attackers.

For instance, if a malicious actor gains access to the training data, they could manipulate it to introduce harmful biases or inaccuracies. Similarly, if pre-trained models are sourced from unreliable providers, they may contain hidden flaws or backdoors that can be exploited later. Deployment platforms can also have weaknesses that attackers might exploit to gain unauthorized access or disrupt services.

Potential Consequences of Supply Chain Vulnerabilities

The consequences of these vulnerabilities can be severe. A compromised supply chain can lead to biased outcomes in LLM outputs, which can affect decision-making processes across various applications—from healthcare to finance. For example, if an LLM trained on biased data is used to make hiring decisions, it could unfairly disadvantage certain candidates based on race or gender.

Moreover, vulnerabilities in the supply chain can result in security breaches that expose sensitive information. If an attacker successfully exploits a weakness in a model or its training data, they might gain access to confidential user data or proprietary algorithms. This not only compromises individual privacy but can also lead to significant financial losses and damage to an organization's reputation.

In extreme cases, supply chain vulnerabilities can cause complete system failures. If a critical component is compromised or fails to function as expected due to underlying vulnerabilities, it could disrupt services entirely, leading to operational downtime and loss of customer trust.

Read the second part of our OWASP Top 10 for LLMs and CISO security checklist here.