> For the complete documentation index, see [llms.txt](https://docs.mozilla.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mozilla.ai/any-guardrail/api-reference/index/prompt-injection/prompt-guard.md).

# Prompt Guard 2

Encoder classifier for prompt-injection and jailbreak detection.

Binary encoder classifier (mDeBERTa / DeBERTa) that labels a single prompt string `benign` (index 0) or `malicious` (index 1, i.e. a prompt-injection or jailbreak attempt). Meta's v2 collapsed Prompt Guard 1's separate "injection" class and focuses on explicit jailbreak / injection attacks. It screens prompt text only — it is not designed to judge model responses.

Verdict mapping onto `GuardrailOutput`:

* `valid` is `True` when the predicted class is `benign` (argmax index != 1).
* `score` (canonical risk: higher = riskier) is the `malicious` probability.
* `categories` carries one `CategoryResult` per class, each with its probability and a `triggered` flag on the predicted class. The gated repos publish only the generic `LABEL_0` / `LABEL_1` names, so categories fall back to those when the provider surfaces no label list.

Expected inputs: a single prompt string, or a `list[str]` for batched classification (the inherited `validate` dispatches list input to `_validate_batch`). The 86M default is multilingual; the 22M variant is English-only.

The repos are gated under the Llama 4 Community License — accept the terms on the model page and authenticate with `hf auth login` before first use.

For more information, please see the model cards:

* [Llama-Prompt-Guard-2-86M](https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M) (default) — mDeBERTa-base, multilingual.
* [Llama-Prompt-Guard-2-22M](https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-22M) — DeBERTa-xsmall, English, \~75% lower latency.

## Supported Models

* `meta-llama/Llama-Prompt-Guard-2-86M`
* `meta-llama/Llama-Prompt-Guard-2-22M`

## Constructor

| Parameter  | Type                                                 | Required | Default | Description                                                                                                                                                             |
| ---------- | ---------------------------------------------------- | -------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model_id` | \`str                                                | None\`   | No      | `None`                                                                                                                                                                  |
| `provider` | `Optional[Provider[dict[str, Any], dict[str, Any]]]` | No       | `None`  | Optional pre-configured provider. If `None`, a default `HuggingFaceProvider` (targeting `AutoModelForSequenceClassification`) is built and the model is loaded eagerly. |

Initialize the Prompt Guard 2 guardrail.

## validate

Default validation pipeline: preprocess -> inference -> postprocess.

**Parameters**

| Parameter    | Type  | Required     | Default | Description |
| ------------ | ----- | ------------ | ------- | ----------- |
| `input_text` | \`str | list\[str]\` | Yes     | —           |

**Returns:** `GuardrailOutput | list[GuardrailOutput]`

## Benchmarks

### Prompt Injection

| Dataset (rev)             | Metric | Threshold    | Value     | Harness                 | Source                           | Contam. |
| ------------------------- | ------ | ------------ | --------- | ----------------------- | -------------------------------- | ------- |
| deepset\_pi (unspecified) | f1     | native-valid | 0.235294  | guardrail-bench+ag0.7.4 | measured:guardrail-bench+ag0.7.4 |         |
| notinject (unspecified)   | fpr    | native-valid | 0.0315789 | guardrail-bench+ag0.7.4 | measured:guardrail-bench+ag0.7.4 |         |
| gandalf (unspecified)     | recall | native-valid | 0.973214  | guardrail-bench+ag0.7.4 | measured:guardrail-bench+ag0.7.4 |         |

## License

* **Vendor:** Meta
* **Default license:** `llama-4` (of the default model/service)


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