**Red-Teaming LLMs with Garak: A Step-by-Step Guide to Evaluating Model Safety**
Hey everyone, welcome back to my blog!
In this tutorial, we’re going to dive into how to build a multi-turn crescendo-style red-teaming pipeline using Garak to test large language model (LLM) safety. We’ll explore how to simulate lifelike conversational escalation patterns and assess whether the model maintains its security boundaries throughout the interaction. Instead of focusing on single-prompt failures, we’ll take a closer look at multi-turn robustness and discuss practical, reproducible ways to analyze it.
To get started, let’s set up the execution environment and load the required dependencies. We’ll import the necessary Python modules for file handling, subprocess management, and time management. We’ll also load data evaluation and plotting libraries so we can later examine and visualize Garak’s scan results.
Next, we’ll securely load the OpenAI API key and inject it into the runtime environment. We’ll make sure the secret is never hardcoded and is provided either through Colab secrets or a hidden prompt. We’ll validate that the key exists before continuing, so the scan can run without authentication failures.
We’ll also extend Garak by adding a custom detector that flags potential system leakage or hidden directions. We’ll outline simple but efficient heuristics that identify unsafe disclosures in model outputs. We’ll register this detector directly inside Garak’s plugin system so it can be invoked during scans.
To create a more realistic simulation, we’ll implement a multi-turn iterative probe that simulates a crescendo-style conversational escalation. We’ll start from benign prompts and progressively steer the dialog towards sensitive extraction attempts across multiple turns. We’ll construct and manage dialog history carefully so the probe realistically displays how gradual stress unfolds in real interactions.
After setting up the probe and detector, we’ll configure and execute the Garak scan using the custom probe and detector against a specific OpenAI-compatible model. We’ll control concurrency and generation parameters to ensure steady execution in a Colab setting. We’ll capture the raw output and logs so we can later analyze the model’s behavior under multi-turn stress.
Once the scan is complete, we’ll find the generated Garak report and parse the JSONL results into a structured dataframe. We’ll extract key fields like probe ID, detector result, and model output for inspection. We’ll then visualize the detection scores to quickly assess whether any multi-turn escalation attempts trigger potential security violations.
In conclusion, we’ve demonstrated how to systematically evaluate a model’s resilience against multi-turn conversational drift using a structured, extensible Garak workflow. We’ve shown that combining iterative probes with custom detectors provides clearer visibility into where security policies hold firm and where they might start to weaken over time. This method allows us to move beyond ad-hoc prompt testing towards repeatable, defensible red-teaming practices that can be tailored, expanded, and integrated into real-world LLM analysis and monitoring pipelines.
Try the full code here and explore the nuances of red-teaming LLMs with Garak!
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most recent endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily comprehensible by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity amongst audiences.
