> 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/injec-guard.md).

# PIGuard

Binary prompt-injection classifier trained to mitigate over-defense.

Runs PIGuard's DeBERTa-v3 encoder classifier over a single user prompt and reports whether the text is a prompt-injection attempt. The model is a two-class sequence classifier whose unsafe class is labeled `"injection"`; the guardrail treats that class as the risky one. PIGuard adds the "Mitigating Over-defense for Free" (MOF) training strategy (ACL 2025), which reduces the trigger-word bias that makes prompt-injection guards falsely flag benign inputs.

Expected input: prompt-only text. `validate(input_text)` accepts a single string, or a `list[str]` to classify a batch in one call; there is no prompt+response or chat-message mode.

Verdict mapping onto `GuardrailOutput`:

* `valid` is `True` when the predicted class is not `"injection"` (i.e. the text looks safe).
* `score` is the model's probability of the `"injection"` class (canonical risk direction: higher = riskier).
* `categories` carries one `CategoryResult` per class label, each with its softmax `score` and a `triggered` flag marking the argmax class.
* No `spans` or `modified_text` are produced.

`leolee99/PIGuard` is the renamed, maintained successor to InjecGuard (the rename was for licensing reasons); `leolee99/InjecGuard` is the original repository, kept for backward compatibility. Both share the same DeBERTa-v3 architecture and `"injection"` label and ship custom model code, so the default provider loads them with `trust_remote_code=True`.

For more information, see:

* [PIGuard model card](https://huggingface.co/leolee99/PIGuard) (default)
* [InjecGuard model card](https://huggingface.co/leolee99/InjecGuard)
* [InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models (arXiv:2410.22770)](https://arxiv.org/abs/2410.22770)

## Supported Models

* `leolee99/PIGuard`
* `leolee99/InjecGuard`

## Constructor

| Parameter  | Type                                                 | Required | Default | Description                                                                                                                                                                                                                                                                                                                     |
| ---------- | ---------------------------------------------------- | -------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model_id` | \`str                                                | None\`   | No      | `None`                                                                                                                                                                                                                                                                                                                          |
| `provider` | `Optional[Provider[dict[str, Any], dict[str, Any]]]` | No       | `None`  | Execution backend that loads the model and runs inference. Defaults to a `HuggingFaceProvider` constructed with `trust_remote_code=True` (PIGuard ships a custom model class), targeting `AutoModelForSequenceClassification`. Supply your own to control device, dtype, or `cache_dir`, or to run against a different backend. |

Initialize the PIGuard 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 | 0.8       | guardrail-bench+ag0.7.4 | measured:guardrail-bench+ag0.7.4 |         |
| notinject (unspecified)    | fpr    | native-valid | 0.0842105 | guardrail-bench+ag0.7.4 | measured:guardrail-bench+ag0.7.4 | ⚠️      |
| gandalf (unspecified)      | recall | native-valid | 0.955357  | guardrail-bench+ag0.7.4 | measured:guardrail-bench+ag0.7.4 |         |
| bipia\_email (unspecified) | f1     | native-valid | 0.863755  | bir\@fd86c16            | measured:bir\@fd86c16            |         |
| bipia\_table (unspecified) | f1     | native-valid | 0.908286  | bir\@fd86c16            | measured:bir\@fd86c16            |         |

## License

* **Vendor:** leolee99
* **Default license:** `mit` (of the default model/service)


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