LLM Security: Prompt Injection and Leakage
Large Language Models (LLMs) and their capabilities have been in the limelight in recent times. Specifically, Text-to-SQL applications leveraging LLMs are transforming the way users interact with databases by allowing natural language queries to retrieve or manipulate data. These applications bridge the gap between complex SQL query syntax and the intuitive nature of human language, making information retrieval from databases more accessible to non-technical users.
However, with great power comes great responsibility. In this post we look at the how these advancements also necessitate robust security measures to prevent prompt injection and leakage, ensuring the safe and effective use of text-to-SQL technologies in diverse industries
Understanding Prompt Injection and Prompt Leaking
Prompt injection occurs when an attacker manipulates the prompt of a large language model in a way that alters the intended function of the SQL query. This can be done by injecting malicious code or commands into the user input, which the LLM then translates into an SQL query that executes the attacker’s commands on the database. This vulnerability exploits the model’s ability to generate code from natural language input, turning it into a vector for SQL injection attacks.
Example:
Consider a user input that’s intended to exploit the system:
“Show me the sales figures for last quarter; DROP TABLE Customers; –“
If the application directly translates this input into an SQL query without proper validation, the malicious SQL command (DROP TABLE Customers;) embedded within the user’s input could be executed. This would lead to the deletion of the “Customers” table, causing significant data loss and potentially disrupting business operations.
Prompt leaking, on the other hand, involves the inadvertent exposure of sensitive information through the prompts used in text-to-SQL applications. Since LLMs learn from vast amounts of data, including the prompts and queries they process, there is a risk that sensitive information contained in these inputs could be leaked through the model’s responses. This could happen if the model is prompted to generate SQL queries based on sensitive information, and the generated queries or their metadata expose that information either directly or indirectly.
Example:
Suppose a large language model is used in a healthcare application to generate SQL queries from natural language inputs. A doctor might input a request like:
“Show me the medical records for John Doe with the diagnosis from last month.”
If the model’s training data included real patient information or if it generates queries that embed specific patient details directly into the SQL command, there’s a risk of exposing sensitive information.
Consider the query –
SELECT * FROM Patient_Records WHERE name = ‘John Doe’ AND diagnosisDate BETWEEN ‘2024-01-01’ AND ‘2024-01-31’;
In this case, the patient’s name and diagnosis period are embedded directly into the query. If these queries are logged or if the model’s outputs are not adequately secured, unauthorized individuals could potentially access this information, leading to a breach of confidentiality.
Implications
As you can see the implications of prompt injection and prompt leaking are significant, particularly in environments where sensitive data is accessed and manipulated using text-to-SQL interfaces. These vulnerabilities can lead to data breaches, unauthorized data manipulation, and exposure of confidential information. In sectors such as finance, healthcare, and government, where data security is paramount, the risks associated with prompt injection and leaking are especially concerning.
Risk Mitigation
To mitigate the risks of prompt injection and prompt leaking, several strategies can be employed:
- Input Validation and Sanitization: Implement rigorous input validation and sanitization measures to ensure that inputs do not contain malicious SQL code or sensitive information that could be leaked. This includes checking for and removing SQL keywords, special characters, and patterns that could be used in SQL injection attacks.
- AI based Cross Validation : This allows another LLM without context about the user queries to validate the nature of the queries and provide a safety score. This technique provides an intelligent layer beyond rules or syntactical based input / output validation.
- Use of Parameterized Queries: Instead of directly translating natural language inputs into SQL queries, use parameterized queries where the structure of the SQL query is predefined, and user inputs are treated as parameters. This significantly reduces the risk of SQL injection. This strategy however limits the potential of interactive querying.
- Access Controls and Authentication: Implement strict access controls and authentication mechanisms to limit who can generate and execute SQL queries through the text-to-SQL interface. This helps prevent unauthorized access and reduces the risk of prompt injection attacks.
- Logging and Monitoring: Log and monitor the activity on text-to-SQL interfaces to detect and respond to suspicious behavior that could indicate prompt injection or leaking. Regularly review logs for signs of unauthorized access or data leakage.
- Education and Awareness: Educate users and developers about the risks of prompt injection and leaking in text-to-SQL applications. Awareness is key to preventing accidental data exposure through careless prompt formulation.
In conclusion, while text-to-SQL applications powered by LLMs offer significant benefits for querying databases using natural language, they also introduce new security challenges. By understanding and mitigating the risks of prompt injection and prompt leaking, organizations can safeguard their data while leveraging the advantages of these innovative technologies.
