Why Pentesting Large Language Models is Crucial in 2024

Generative AI technologies such as large language models (LLMs) are rapidly emerging as some of the most significant technological advancements in recent times. According to Gartner, it is expected that over 80% of enterprises will have implemented GenAI APIs and models or have GenAI-powered applications in production by 2026.¹. In 2023, less than 5% of companies had already implemented such technology. ² Bloomberg Intelligence also found that Generative AI will reach a $1.3 trillion market by 2032. ³

While these technologies offer numerous benefits regarding scalability, efficiency, and speed for individuals and businesses, they also present risks if malicious entities compromise them, making AI security a necessity. Penetration testing, or pen testing, is a proven testing method that earned its place in cybersecurity as a practice. Conducting penetration testing on AI and LLMs can help uncover and mitigate vulnerabilities, thereby reducing cybersecurity risks associated with AI and limiting potential misuse and. In this article, we’ll look at the risks associated with LLMs and address how penetration testing can mitigate these.

What are common LLM applications that can be pentested?

Customer Service Bots

These AI-driven customer service chatbots handle customer queries and support. Pentesting can help uncover vulnerabilities like prompt injection or data leakage, ensuring these systems do not expose sensitive customer information.

Content Generation

LLMs are used for generating written content, from articles to marketing materials. Testing these systems can ensure they do not inadvertently produce or disseminate harmful or misleading information due to manipulation or adversarial attacks.

Sentiment Analysis Tools

Used widely in social media monitoring and market analysis, sentiment analysis tools analyze text to determine sentiment. Pentesting can check for biases or vulnerabilities that could skew analysis results, leading to incorrect business insights.

Translation Services

AI-driven translation services are common LLM applications. These systems can be tested for their susceptibility to injection attacks that might result in incorrect or offensive translations.

Personalized Recommendation Systems

Whether for shopping, entertainment, or content curation, these systems can be pentested to prevent data poisoning attacks that might skew the recommendations.

Educational and Tutoring Applications

AI applications that provide learning assistance or tutoring can be tested for vulnerabilities that could be exploited to provide incorrect information or compromise student data.

Compliance and Legal Aid Tools

These legal aid tools assist organizations in maintaining compliance with regulations or providing legal advice. Testing is crucial to ensure they are not only accurate but also secure against attacks that could lead to legal repercussions.

What are cybersecurity risks associated with LLMs, and how can pentesting detect/mitigate them?

1. Data Privacy and Leakage

LLMs require access to vast amounts of data, often including sensitive or proprietary information. There is a risk that the model could inadvertently expose this data in its responses or that attackers could extract such information through sophisticated querying techniques, known as data extraction attacks.

• Simulating sophisticated querying: This tests the model’s response to various inputs, checking for inadvertent disclosure of sensitive information.
• Analyzing data handling and output: Ensuring that data processed by LLMs does not expose sensitive information in any form and implementing data anonymization where necessary.

2. Adversarial Attacks

LLMs can be vulnerable to adversarial attacks, where attackers input deliberately crafted data to trick the model into making errors or revealing sensitive information. These can be particularly concerning if the model is used in security-sensitive environments.

• Simulating attacks: Pentesting tools create inputs that attempt to trick the model into making errors or revealing sensitive data, assessing the model’s resilience.
• Implementing robust defenses: Based on pentest results, models can be fine-tuned to detect and reject malicious inputs using techniques like input validation and adversarial training.

3. API Security

If the model is accessed via APIs, there are risks associated with API security, such as unauthorized access, denial of service attacks, or exploiting vulnerabilities in the API design and implementation to gain access to underlying systems. API testing practices generally mitigate these risks.

• Testing API endpoints: Pentests can identify vulnerabilities in how APIs handle data, authenticate users, and manage access.
• Securing endpoints: Implementing stronger authentication, rate limiting, and encryption to protect APIs from unauthorized access and attacks.

4. Scalability and Abuse

The scalability of LLMs can be a double-edged sword. While it allows organizations to handle large volumes of queries efficiently, it also means that any security vulnerability can be exploited at scale, potentially leading to widespread disruption or data breaches.

• Stress testing: By simulating high loads, pentesting can determine how the system behaves under stress, identifying potential points of failure.
• Resource management: Enhancing monitoring and response strategies to manage load effectively and prevent service disruptions.

5. Supply Chain Attacks

LLMs are often developed using third-party libraries or hosted on cloud platforms. Compromise in any part of the supply chain can lead to potential vulnerabilities in the model, such as embedding malicious code that could be activated post-deployment.

• Evaluating third-party components: Testing can help identify vulnerabilities introduced through third-party libraries or services.
• Securing the supply chain: Implementing stricter security measures for third-party integrations and continuously monitoring for vulnerabilities.

OWASP Top 10 Vulnerabilities Associated With LLMs

The OWASP Top 10 for Large Language Model Applications is specifically tailored to address security vulnerabilities in systems using large language models.⁴. Our “Top LLM Security Risks” article covers these in detail; here, we go over how pentesting can mitigate these vulnerabilities.

1. Prompt Injection (LLM01): This involves attackers manipulating LLMs through crafted inputs, causing the model to perform unintended actions
   • Pentesting tools can simulate both direct and indirect prompt injection attacks to test how well the system identifies and neutralizes harmful inputs. This helps strengthen input validation processes and implement robust sanitation measures.
2. Insecure Output Handling (LLM02): This risk occurs when LLM outputs are not properly sanitized, leading to potential downstream security exploits like code execution or data exposure.
   •  By attempting to exploit outputs in various ways, pentesting can identify vulnerabilities where outputs are not properly sanitized. This can lead to improvements in output handling procedures to prevent downstream security exploits.
3. Training Data Poisoning (LLM03): Introducing vulnerabilities or biases into the model during training can compromise its security, effectiveness, or ethical behavior.
   • Pentesting tools can test the resilience of a model against malicious data inputs intended to alter its behavior. This helps in enhancing the security measures around data handling and model training environments.
4. Model Denial of Service (LLM04): Overloading LLMs with resource-intensive operations can lead to service disruptions and increased operational costs.
   • By stressing the model with resource-intensive queries, pentesting can evaluate the system’s robustness against denial-of-service attacks. This can guide the implementation of rate limiting, caching, and other protective measures.
5. Supply Chain Vulnerabilities (LLM05): LLMs relying on compromised components, services, or datasets can suffer from data breaches and system failures.
   •  Pentesting can include testing third-party components and services for vulnerabilities that could affect the large language model. This helps ensure that all parts of the supply chain are secure and trustworthy.
6. Sensitive Information Disclosure (LLM06): There’s a risk of LLMs disclosing sensitive information within their outputs, leading to privacy violations and potential security breaches.
   • Through targeted probing and testing outputs, pentesting can uncover scenarios where the model might leak sensitive information, leading to enhancements in data privacy and output filtering mechanisms.
7. Insecure Plugin Design (LLM07): Plugins for LLMs that process untrusted inputs or lack sufficient access control can be exploited, potentially leading to severe security breaches.
   • By attacking plugins directly, testers can identify weak points in their design and implementation, encouraging better security practices in plugin development and integration.
8. Excessive Agency (LLM08): Allowing LLMs too much autonomy or functionality can lead to unintended actions that may compromise system security.
   • Tests can be conducted to assess how the model behaves when given high levels of autonomy, ensuring that safety checks are in place to prevent unintended actions.
9. Overreliance (LLM09): Overly depending on LLM outputs without critical assessment can lead to misinformed decisions and expose security vulnerabilities.
   • Pentesting can help demonstrate the potential pitfalls of over-relying on model outputs without verification, promoting the importance of manual oversight and validation in decision-making processes.
10. Model Theft (LLM10): Unauthorized access, copying, or exfiltration of proprietary LLMs can lead to significant losses, including the leakage of sensitive information.
    • Security tests can simulate attacks aiming to access or copy the model, helping to fortify defenses against unauthorized access and data exfiltration.

Further Reading

• LLM Security Tools
• LLMops Tools
• Top Application Security Tools
• Application Security Metrics: Why do they matter

If you have further questions, reach us:

External Links

• 1. Gartner. “Gartner Says More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026”
• 2. Forbes. “Beyond The GenAI Hype: Three Considerations For Practical Adoption”
• 3. Bloomberg Intelligence.“Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds”
• 4. OWASP “The OWASP TOP 10 for Large Language Model Applications”