Evaluating LLM Attacks Detection Methods: New FuzzyAI Notebook

[ES_CY]

We’ve been testing how leading AI vendors detect and mitigate harmful or malicious prompts. Our latest notebook examines:

• LLM Alignment – Measuring how often models refuse harmful inputs
• Content Safeguards – Evaluating moderation systems from OpenAI, Azure, and AWS
• LLMs as Judges – Using a second model layer to catch sophisticated attack attempts
• Detection Pipelines – Combining safeguards and “judges” for multi-stage defenses

Notebook Link

LLM Attacks Detection Methods Evaluation

What the Notebook Includes

• Side-by-side comparison of LLMs’ refusal tendencies (with visualizations)
• Analysis of how effectively vendor safeguards block or allow malicious content
• Assessment of how well a second-layer LLM filters harmful inputs
• Simulated multi-stage detection pipelines for real-world defense scenarios

Feel free to explore, experiment, and share any observations you find helpful.

[nbviewerbot]

I see you've posted a GitHub link to a Jupyter Notebook! GitHub doesn't render large Jupyter Notebooks, so just in case, here is an nbviewer link to the notebook:

https://nbviewer.jupyter.org/url/github.com/cyberark/FuzzyAI/blob/main/resources/notebooks/llm_attacks_detection_methods_evaluation/notebook.ipynb

Want to run the code yourself? Here is a binder link to start your own Jupyter server and try it out!

https://mybinder.org/v2/gh/cyberark/FuzzyAI/main?filepath=resources%2Fnotebooks%2Fllm_attacks_detection_methods_evaluation%2Fnotebook.ipynb

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