LLMs (large language models), such as GPT-4 and BERT, are renowned for text generation, data analysis, and customer interaction. They have become essential in employees' daily professional lives. Yet, as we adopt these advanced AI tools in our routine tasks, securing sensitive data in LLM prompts becomes a concern.

Using LLMs in business processes increases the responsibility to protect sensitive information. This blog post explores the security threats, strategies, and best practices for protecting sensitive data in LLM prompts. Let’s begin.

A Closer look at LLM Security Risks & Threats

While LLMs are extremely useful, they are also vulnerable to cyber threats compromising the integrity and confidentiality of the data they process. Here are the key security vulnerabilities associated with them.

• Prompt injection attacks: These attacks occur when malicious actors manipulate the input prompts given to an LLM, leading to unauthorized actions or data exposure. These attacks can significantly undermine the model's reliability, leading to incorrect or harmful outputs.
• Prompt leaking: This is a subtle yet potentially damaging vulnerability where the LLM inadvertently reveals its own prompts or internal processing logic. This differs from prompt injection attacks in that the primary goal is not to alter the model's behavior but to extract information about the model itself.
• Data leakage: It refers to the unintentional exposure of sensitive information through the outputs of an LLM due to flaws in the model's design or training data.
• Personal information in LLMs: Incorporating personal information into LLM prompts poses significant privacy risks. If not managed properly, this practice can lead to the exposure of personal identifiers, violating user privacy and trust.
• Compliance issues with data protection laws: The use of LLMs to process personal data presents compliance challenges with various data protection laws, such as GDPR and CCPA.‍
• Insecure output handling: LLMs can generate unsafe or harmful content due to insecure output handling from inadequate filtering or processing of outputs.‍
• Model denial-of-service (DoS): A Model Denial-of-Service attack involves overwhelming an LLM with a flood of requests or inputs, rendering it unable to function properly.‍
• Insecure plugin design: If not designed with robust security measures, plugins or extensions added to LLMs can introduce security vulnerabilities.‍
• Excessive agency and overreliance: Excessive agency gives LLMs more autonomy or functions than necessary, leading to unintended consequences. Overreliance on LLMs without proper oversight can also pose security risks due to blind trust in outputs and neglect of anomaly detection.‍
• Model theft: It refers to the unauthorized access, copying, or use of proprietary LLM models.

How to Mitigate LLM Security Risks?

Prompt injection vulnerabilities pose a significant threat, as malicious inputs can manipulate the model's output. Here are the strategies and industry best practices for securing LLMs against prompt injection attacks.

1. Input Validation and Sanitization

• Strict Validation: Implement strict validation rules for inputs based on expected types, formats, and lengths. This can prevent malicious inputs from being processed.
• Sanitization: Automatically sanitize inputs to remove or encode potentially harmful characters or patterns. This step is crucial for preventing the injection of malicious commands.

2. Use of Allowlists

• Allowlists Over Blocklists: Employ allowlists (lists of permitted inputs) rather than blocklists (lists of forbidden inputs), as the former are generally more secure. Allowlisting ensures that only pre-approved inputs are processed.

3. Role-based Access Control

• Limit Permissions: Use role-based access control (RBAC) to limit the actions that can be performed based on the user's role. This minimizes the potential damage of a successful injection attack.

4. Secure Prompt Design

• Controlled Prompt Construction: Design prompts in a way that limits the user's ability to influence the execution path. This may involve using structured data for inputs rather than free text where possible.
• Template Usage: Use templates for generating prompts, where variables can be safely inserted without giving the user direct control over the prompt structure.

5. Regular Expression Checks

• Pattern Recognition: Use regular expressions to identify and block potentially malicious patterns in inputs. This requires ongoing updates as new attack vectors are identified.

6. Monitoring and Logging

• Anomaly Detection: Implement monitoring and logging to detect unusual patterns of use that might indicate an attempted or successful injection attack. This helps in quick identification and response.
• Audit Trails: Keep detailed logs of user actions and system responses to aid in investigating incidents after they occur.

7. User Education

• Awareness Training: Educate users about the risks of prompt injection and encourage the reporting of suspicious activities. Informed users can act as an additional layer of defense.

What are the technical tips to address Prompt Leaking Concerns (LLM Security Risks)?

The below strategies address prompt leaking concerns and maintain the integrity and confidentiality of LLM operations.

1. Use Data Anonymization, Data Redaction and Pseudonymization

• Anonymize Data: Before sending prompts containing potentially sensitive data to an LLM, anonymize this information. Replace names, addresses, and other personally identifiable information (PII) with generic placeholders.‍
• Automated Redaction: Implement automated tools to identify and redact sensitive information from inputs before processing.

• Pseudonymization: If you need to keep references consistent within a dataset or a series of interactions, use pseudonymization to replace sensitive data with non-identifiable placeholders that maintain reference integrity.

2. Implement Input Validation and Sanitization

• Sanitize Inputs: Remove or encode potentially dangerous characters and patterns in input data that might be misinterpreted by the LLM or could lead to unintended behavior.
• Validate Inputs: Ensure that the data sent to the LLM adheres to expected formats and ranges, minimizing the risk of injecting sensitive data unintentionally.

3. Encryption of Data in Transit and At Rest

• Encrypt Data in Transit: Use strong encryption protocols like TLS (Transport Layer Security) for all data transmitted to and from LLMs to prevent eavesdropping and man-in-the-middle attacks.
• Encrypt Data at Rest: Ensure that any sensitive data stored, either for processing or as part of the model's training data, is encrypted using strong encryption standards.

4. Role-based Access Control (RBAC)

• Limit Access: Use RBAC to control who has access to sensitive data and the ability to interact with the LLM. Ensure that only authorized personnel can input sensitive data or retrieve the output from such interactions.

5. Secure Data Handling Practices

• Data Encryption: Ensure that data is encrypted both data at rest and data in transit to protect against unauthorized access.
• Access Controls: Apply strict access controls to limit who can view and interact with data, based on the principle of least privilege.

6. Data Redaction Tools

• Automate Redaction: Use automated tools to identify and redact sensitive information from the outputs provided by LLMs, especially when these outputs are shared or stored.

7. Secure Authentication Mechanisms

• Strong Authentication: Implement strong authentication mechanisms, such as multi-factor authentication (MFA), to protect accounts that have access to LLMs, preventing unauthorized access.

8. Usage of API Gateways

• API Gateways: Use API gateways with rate limiting and monitoring capabilities to control and monitor access to private LLMs, preventing abuse and detecting potential data leakage scenarios.

9. User Education and Awareness

• Inform Users: Clearly inform users about the types of data that the system can process and any associated risks. Provide guidelines on what information should not be submitted to the system.
• Training: Train staff and users on data privacy best practices and the importance of safeguarding sensitive information.

10. Regular Audits and Assessments

• Security Audits: Conduct regular security audits and privacy impact assessments to identify vulnerabilities and assess the effectiveness of privacy protection measures.

11. Privacy-by-Design

• Incorporate Privacy: Adopt a privacy-by-design approach in all processes involving LLMs, ensuring that data protection measures are integrated into the development and deployment phases.

How does Strac Address LLM Security Concerns?

Data Loss Prevention (DLP) tools like Strac play a crucial role in addressing security concerns related to the use of Large Language Models (LLMs) by providing a layer of protection that prevents the accidental or unauthorized transmission of sensitive information. With the increasing use of AI platforms like ChatGPT, Claude, Google Gemini, Jesper, and others for various tasks, including sensitive data processing, the risk of data leaks or breaches has become a significant concern.

Strac's approach to mitigating these risks includes several remediation actions that can be leveraged both through a Chrome extension for end-users and via an API for backend server integration. Here is what our customers say,

Here's how these features address LLM security concerns:

1. Alerting

• Proactive User Engagement: By alerting users in real-time when they are about to submit sensitive data to AI websites, Strac helps in creating awareness and encourages caution. This immediate feedback loop can significantly reduce accidental data leaks by fostering a security-conscious environment.
• Behavioral Change: Continuous alerts can influence user behavior over time, promoting a culture of data protection and security mindfulness among individuals and within organizations.

2. Redaction

• Automated Protection: Redaction automatically removes sensitive information from data before it is sent to LLMs. This ensures that even if data is inadvertently shared, the risk of exposure is minimized.
• Seamless Integration: Implementing redaction at both the client and server levels allows organizations to maintain data privacy without disrupting the user experience or the functionality of the AI services being used.

3. Pseudonymization

• Data Utility Preservation: Pseudonymization replaces sensitive information with non-identifiable placeholders, allowing data to be processed by LLMs without exposing real identities or sensitive details. This technique maintains the utility of data for analysis or processing while safeguarding privacy.

• Compliance Support: This approach also aids in compliance with data protection regulations like GDPR, which encourage pseudonymization as a method to reduce the risk of harm to data subjects.

4. Blocking

• Direct Prevention: By blocking the transmission of sensitive data to AI platforms, Strac provides a direct control mechanism to prevent potential data breaches. This is particularly useful for highly sensitive or regulated information that should not be processed by external entities under any circumstances.
• Customizable Policies: Organizations can define what constitutes sensitive data based on their specific regulatory requirements and risk assessments, allowing for tailored blocking rules that align with their security policies.

5. Extension and API Integration

• Wide Application: The availability of Strac as a Chrome extension makes it accessible to a broad user base, offering immediate protection without the need for complex configurations. Similarly, the API allows for integration into backend systems, extending protection to data before it leaves the organization's controlled environment.
• Flexibility and Control: With the API, organizations can programmatically define, update, and enforce data protection policies across all their data processing activities involving LLMs, ensuring consistency and compliance.

By incorporating DLP solutions like Strac, organizations can significantly enhance the security posture of their interactions with LLM platforms. This multifaceted approach addresses not only the immediate risk of data exposure but also promotes a broader culture of security and privacy that is essential in today’s data-driven world.

Read More on need for DLP in AI environments:

• Strac Data Loss Prevention for Claude
• Strac Data Loss Prevention for Jasper
• Strac DLP for Copy.ai