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Ban Topics Scanner

This scanner is designed to detect outputs that touch upon topics that are considered sensitive using Zero-Shot classifier.

Attack scenario

Even with controlled prompts, LLMs might produce outputs touching upon themes or subjects that are considered sensitive, controversial, or outside the scope of intended interactions. Without preventive measures, this can lead to outputs that are misaligned with the platform's guidelines or values.

How it works

It relies on the capabilities of the following models to perform zero-shot classification:

Model Description
MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 It was trained on a mixture of 33 datasets and 389 classes reformatted in the universal NLI format. The model is English only. You can also use it for multilingual zeroshot classification by first machine translating texts to English.
MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33 This is essentially the same as its larger sister only that it's smaller. Use it if you need more speed. The model is English-only.
MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33 Same as above, just smaller/faster.
MoritzLaurer/xtremedistil-l6-h256-zeroshot-v1.1-all-33 Same as above, just even faster. The model only has 22 million backbone parameters. The model is 25 MB small (or 13 MB with ONNX quantization).


from llm_guard.output_scanners import BanTopics

scanner = BanTopics(topics=["violence"], threshold=0.5)
sanitized_output, is_valid, risk_score = scanner.scan(prompt, model_output)

Optimization Strategies

Read more



  • Platform: Amazon Linux 2
  • Python Version: 3.11.6

Run the following script:

python benchmarks/ output BanTopics


Instance Test Times Latency Variance Latency 90 Percentile Latency 95 Percentile Latency 99 Percentile Average Latency (ms) QPS
AWS m5.xlarge 5 2.39 485.00 509.32 528.78 435.82 204.21
AWS m5.xlarge with ONNX 5 0.09 165.61 170.05 173.60 155.90 570.87
AWS g5.xlarge GPU 5 35.44 331.25 425.26 500.46 142.77 623.37
AWS g5.xlarge GPU with ONNX 5 0.13 33.26 38.89 43.40 21.76 4090.94
Azure Standard_D4as_v4 5 3.91 547.06 577.87 602.53 483.73 183.99
Azure Standard_D4as_v4 with ONNX 5 0.06 176.34 179.65 182.30 168.16 529.25