Securing LLMs
Injection vulnerabilities are a critical security concern for any application that processes untrusted data, as these vulnerabilities can affect multiple components across an application. Even if data appears safe for one part of an application, it can trigger an attack elsewhere. For instance, data may look harmless to an interface that checks for cross-site scripting (XSS) but could still initiate a dangerous SQL injection. This is why secure input handling is essential for LLMs and other applications that use sensitive data.
The first line of defense against injection vulnerabilities is input validation. This involves carefully evaluating incoming data, either by blocking dangerous characters (a blocklist) or allowing only known safe characters (an allowlist). For example, if an application expects alphanumeric usernames, blocking any characters outside that set can prevent SQL injection risks. However, blocklists require careful configuration, as overlooking even one risky character can allow a security breach. On the other hand, allowlists can sometimes be too restrictive; for instance, a name field may need to accept special characters like an apostrophe, as in the name "O'Brien."
Another effective protection is parameterization, which ensures that program logic is defined separately from user data. By pre-compiling code and inserting user data as a final step, parameterization prevents the user input from being treated as executable code. This method effectively separates command logic from data, limiting the risk of injection attacks in contexts where it can be implemented.
Escaping is another technique, where special characters in user input are encoded to prevent them from being executed. While useful, escaping requires precise syntax for each downstream parser to avoid misinterpretation. Each application component must be set up to recognize the same encoding standards, as a missed escape sequence could enable an attack.
By employing these strategies—input validation, parameterization, and escaping—developers can better protect applications from injection vulnerabilities. Sazakan helps test these defenses by simulating various types of prompt injections, ensuring that models are thoroughly safeguarded. Developing a solid foundation in these techniques is essential for creating secure AI applications that resist injection attacks.
