What We Check Before Shipping an LLM Integration to a Client
When we hand off an LLM-powered feature to a client, they are putting it in front of their users. Some of those users will try to break it — not always maliciously, but because users probe things. Some of them will be malicious. Either way, the question we ask before every handoff is: what happens when someone sends this system something it wasn't designed to handle? This post is our pre-shipment…
Before delivering an LLM-powered feature to a client, we conduct a thorough pre-shipment checklist. This process ensures the system behaves as expected even when presented with unexpected or malicious inputs. The primary focus is on three key areas: prompt injection, output filtering, and data exposure.
First, we test the system prompt for extractability. We generate a series of probes designed to elicit the system prompt's content or reveal sensitive information. Examples include asking the model to repeat the entire system prompt, summarize its instructions, or explicitly state its system prompt. We then feed these probes into the LLM and examine the responses.
If any probe successfully extracts system prompt content, the system prompt must be modified to remove sensitive data or hardened to prevent information leakage. This could involve removing the prompt entirely, retrieving it separately, or limiting its exposure.
Second, we test for instruction override. Attackers may attempt to manipulate the system's behavior by providing contradictory or deceptive instructions. We create a set of injection probes designed to override system instructions or force the model to act in an unintended manner. These probes include commands like "Ignore your previous instructions" or "Bypass content filters."
We feed these probes into the LLM and analyze the responses. If any probe succeeds in overriding the system's behavior or causing it to violate downstream expectations, we must implement safeguards to prevent such attacks.
Third, we examine for data exposure. If sensitive data, such as API keys or internal instructions, is included in the system prompt, it could be inadvertently revealed to users. We test the system prompt for any persistent references to sensitive data and remove or secure this information. If removal is not possible, we consider passing sensitive data as context rather than including it in the prompt, which reduces its persistence and potential for exposure.
In addition to these primary tests, we also conduct broader testing to identify other failure modes that could impact the system's security or functionality. This includes stress testing the system with a variety of inputs, monitoring for unexpected behavior, and reviewing logs for any signs of unauthorized access or misuse.
By systematically addressing these key areas, we can significantly reduce the risk of LLM features being compromised by malicious actors or unintended user inputs. This comprehensive pre-shipment checklist is a crucial step in ensuring the reliability and security of LLM-powered features before they are shipped to clients.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
