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mdeberta-v3-base-prompt-injection-onnx
pistra-dev/mdeberta-v3-base-prompt-injection-onnx
mdeberta-v3-base-prompt-injection-onnx is a text classification model from pistra-dev. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
An ONNX export of proventra/mdeberta-v3-base-prompt-injection produced by the injection-mdeberta recipe of pistra-models, measured through pistra guardrails eval — the gateway's own inspection path (tokenizer binding,…
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.onnx1.5 GB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026
- SHA
- aedb96cb
Try a prompt
Base models
Model card
From the Hugging Face model README
mdeberta-v3-base-prompt-injection-onnx
An ONNX export of proventra/mdeberta-v3-base-prompt-injection produced by the injection-mdeberta recipe of pistra-models, measured through pistra guardrails eval — the gateway's own inspection path (tokenizer binding, windows, fold, decoder, threshold) at byte offsets into the text as sent — and published because that measurement said so.
Files: config.json, tokenizer.json, onnx/model.onnx (fp32), onnx/model_quantized.onnx (dynamic int8). The measured graph is onnx/model.onnx, sha256 41296300ce584147c2faa6e3af73096b1e6900d104234a2ca0a8045d5cf8747c.
Before anything was measured from it, the fp32 graph was run against the checkpoint on the same text at 3 shapes (1×13, 1×202, 5×202) and agreed to within 1.2e-05 on logits, against a tolerance of 0.001. The int8 graph sits 0.96 from the fp32 one, agreeing with it on 100.0% of argmax positions — recorded, not judged: what quantization cost is the measurement below, not this.
Use from pistra
models:
models:
- name: mdeberta-v3-base-prompt-injection-onnx
hf:
repo: pistra-dev/mdeberta-v3-base-prompt-injection-onnx
revision: <the commit of this card>
model: onnx/model.onnx
Measurement
pistra devel 82ccc3176ba3 2026-09-01T15:04:18Z go1.27.0 darwin/arm64 on 2026-09-01T19:46:12+00:00, guardrails eval -config eval-model.yaml -json -parallel 6 -errors 20 -kind user -sweep -types INJECTION -detector candidate safeguard-test.jsonl deepset-test.jsonl jailbreak-test.jsonl.
| set | cases | AP | P | R | F1 | F1~ | ms/case |
|---|---|---|---|---|---|---|---|
| safeguard-test | 2060 | 0.677 | 0.929 | 0.726 | 0.815 | 0.815 | 53.3 |
| deepset-test | 116 | 0.833 | 1.000 | 0.850 | 0.919 | 0.919 | 16.4 |
| jailbreak-test | 262 | 0.993 | 0.993 | 0.993 | 0.993 | 0.993 | 217.0 |
safeguard-test by type
| type | P | R | F1 | F1~ |
|---|---|---|---|---|
| INJECTION | 0.929 | 0.726 | 0.815 | 0.815 |
deepset-test by type
| type | P | R | F1 | F1~ |
|---|---|---|---|---|
| INJECTION | 1.000 | 0.850 | 0.919 | 0.919 |
jailbreak-test by type
| type | P | R | F1 | F1~ |
|---|---|---|---|---|
| INJECTION | 0.993 | 0.993 | 0.993 | 0.993 |
Against the recipe's baseline
| set | metric | baseline | measured | delta | status |
|---|---|---|---|---|---|
| safeguard-test | ap | 0.677 | 0.677 | -0.000 | ok |
| safeguard-test | f1 | 0.814 | 0.815 | +0.001 | ok |
| safeguard-test | type:INJECTION:f1 | 0.814 | 0.815 | +0.001 | ok |
Language
What the verdict depends on besides the text. Every case in these sets is a negative, so every fire is a false positive, and none of this is a score: a detector that fires on nothing is perfectly invariant and blocks no customer. Read it with the recall above in the other hand.
multijail: 315 prompts × 10 languages
The same meanings in every language, so a verdict that changes with the language is a defect no ground truth is needed to see. pistra devel 82ccc3176ba3 2026-09-01T15:04:18Z go1.27.0 darwin/arm64 on 2026-09-01T19:35:25+00:00, 12.8 ms a case; read against en.
| lang | fired | rate | keeps en | lost | gained | agrees with en |
|---|---|---|---|---|---|---|
| en | 7 | 0.022 | 1.000 | 0 | 0 | 1.000 |
| zh | 9 | 0.029 | 0.571 | 3 | 5 | 0.975 |
| it | 9 | 0.029 | 0.714 | 2 | 4 | 0.981 |
| vi | 14 | 0.044 | 0.714 | 2 | 9 | 0.965 |
| ar | 7 | 0.022 | 0.571 | 3 | 3 | 0.981 |
| ko | 7 | 0.022 | 0.286 | 5 | 5 | 0.968 |
| th | 9 | 0.029 | 0.571 | 3 | 5 | 0.975 |
| bn | 27 | 0.086 | 0.714 | 2 | 22 | 0.924 |
| sw | 10 | 0.032 | 0.429 | 4 | 7 | 0.965 |
| jv | 36 | 0.114 | 0.571 | 3 | 32 | 0.889 |
1 row flagged in every language, 263 in none; 51 rows (16.2%) got different verdicts in different languages.
topics: fires on text that attacks nothing
Ordinary traffic with the jailbreak turns tagged; the other rows are the customers a deny rule would block. pistra devel 82ccc3176ba3 2026-09-01T15:04:18Z go1.27.0 darwin/arm64 on 2026-09-01T19:35:28+00:00, 8.5 ms a case.
| language, kind | fired | of | rate |
|---|---|---|---|
| en, benign | 1 | 81 | 0.012 |
| en, jailbreak | 7 | 8 | 0.875 |
| ar, benign | 1 | 81 | 0.012 |
| ar, jailbreak | 6 | 8 | 0.750 |
Data
the product's evaluation sets at 925b2b7abe97 — fetched from a checkout at 7cb21b107f31871027ce70c56f73401cd9b27c50, which is not that commit.
| set | cases | sha256 |
|---|---|---|
| safeguard-test | 2060 | 87352eb07c7c3d97cf394de62416a0def1f596f14e42ca3eee01c01217d2455b |
| deepset-test | 116 | 133d59690b33c4276d557c4f9e8e4b4f738d1dfe9a914b52c1c0e930511fc9e6 |
| jailbreak-test | 262 | b22cc550b36f991748e3479a4c2e07b73060d556ef4e3214283039d4ac92d7b9 |
| multijail | 3150 | 352e29aac777ffd1ade5ef9b274a21ebdc624251339a5eb16226d86b96b10e89 |
| topics | 178 | 1c9d0192dd4068c70e08feb391c5ed180d4f30c0ca0e02ca6cc8968863c0ab20 |
multijail is not the product's: it is DAMO-NLP-SG/MultiJail at 93d65c778925 (MIT), MultiJail.csv at sha256 953132d40b8bf562f20bd9db0a9edf6a3564dc7362bd15a8bb649c8afd128ef4 before conversion.
Provenance
{
"recipe": "injection-mdeberta",
"resolved": "2026-08-31T19:32:23+00:00",
"checkpoint": {
"repo": "proventra/mdeberta-v3-base-prompt-injection",
"revision": "b8a89d3096cf11a71d57b283c854f5ae2ed3df83",
"license": "mit",
"local": "recipes/injection-mdeberta/checkpoints/base"
},
"trained_on_our_eval_sets": {
"deepset-test": "named by this checkpoint's card",
"jailbreak-test": "named by this checkpoint's card and by ProtectAI's"
},
"data": {
"pin": {
"pin": "925b2b7abe9791679587c54c061d0785626f74df",
"direct": {
"multijail": {
"out": "multijail.jsonl",
"repo": "DAMO-NLP-SG/MultiJail",
"revision": "93d65c778925d55973b9ff30c7bccc8545494eb6",
"path": "MultiJail.csv",
"sha256": "953132d40b8bf562f20bd9db0a9edf6a3564dc7362bd15a8bb649c8afd128ef4",
"license": "MIT"
}
},
"source": "checkout",
"checkout": "7cb21b107f31871027ce70c56f73401cd9b27c50",
"drift": true,
"fetched": "2026-08-31T18:35:26+00:00",
"written": [
"multijail.jsonl"
],
"files": {
"anercorp-test.conll": "9dcc3cf9e9db5a1fe2f4147e0504f57cb144a978006eb64f6f210534cac2ad65",
"aqmar.conll": "5ca489909be68cc79947433a816315c21e4c3c3931d5f0e70393642c6a8fef76",
"deepset-test.jsonl": "133d59690b33c4276d557c4f9e8e4b4f738d1dfe9a914b52c1c0e930511fc9e6",
"gulf-names.jsonl": "4cd99f730ff8056f0147826a723cb96a4965f53bdbd7e69b16a39611d2cb69d3",
"jailbreak-test.jsonl": "b22cc550b36f991748e3479a4c2e07b73060d556ef4e3214283039d4ac92d7b9",
"multijail.jsonl": "352e29aac777ffd1ade5ef9b274a21ebdc624251339a5eb16226d86b96b10e89",
"nemotron-pii-test.jsonl": "ccb748fcee6fbf6705ed0b44db00ff6258081458f179341afbe9a98d1a1423ad",
"safeguard-test.jsonl": "87352eb07c7c3d97cf394de62416a0def1f596f14e42ca3eee01c01217d2455b",
"topics.jsonl": "1c9d0192dd4068c70e08feb391c5ed180d4f30c0ca0e02ca6cc8968863c0ab20",
"wikiann-ar-test.conll": "70f08a00d4c901b8e7ce6174218e5335dcaac0682c92969f705d7b28578fdfc4",
"wojood-sample-test.jsonl": "c7949973e48fa40740b95f2b45a7f046d5359acf7074a443d9e703ef8cad19b7"
}
},
"sets": [
"data/eval/safeguard-test.jsonl",
"data/eval/deepset-test.jsonl",
"data/eval/jailbreak-test.jsonl",
"data/eval/multijail.jsonl",
"data/eval/topics.jsonl"
]
}
}
Notes
Evaluated as a candidate to replace the pinned injection classifier for multilingual traffic. Its card names jackhhao/jailbreak-classification and deepset/prompt-injections as training data, so the numbers on those two sets are memorisation and are not a comparison with any other model; safe-guard, topics and multijail are not in its training data and are. Published as the pin for Arabic-serving traffic: on the product's topics.jsonl it flags 1 of 81 Arabic customer turns against protectai's 65, and catches 6 of the 8 Arabic jailbreak turns against Prompt Guard 2's 1; it costs safe-guard F1 0.914 to 0.815 against protectai, which stays the better pin for English-only traffic. The int8 graph in this layout was measured (safe-guard F1 0.621, 2 of 8 Arabic attacks) and is not the one to pin.
Report
The report measure.py wrote — the model card is generated from it and nothing above is hand-edited. Two things are removed rather than published: the absolute paths of the machine it ran on, and the per-set list of mismatched cases, which quotes the evaluation corpora verbatim and is theirs to distribute, not this card's. Both are in the report.json beside the recipe.
{
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"graph": "onnx/model.onnx",
"onnx_sha256": "41296300ce584147c2faa6e3af73096b1e6900d104234a2ca0a8045d5cf8747c",
"export": {
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"task": "text-classification",
"fp32": "onnx/model.onnx",
"int8": "onnx/model_quantized.onnx",
"outputs": [
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],
"sha256": {
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"axes": {
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"attention_mask": [
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"int8_drift": {
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"stripped_padding": {
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"direction": "Right",
"pad_to_multiple_of": null,
"pad_id": 0,
"pad_type_id": 0,
"pad_token": "[PAD]"
}
},
"kind": "classify",
"segment_kind": "user",
"config": {
"guardrails": {
"detectors": [
{
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"name": "candidate",
"classify": {
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"tokenizer_library": "libpitok.dylib",
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}
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"rules": [
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"command": [
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"6",
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"data_pin": {
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],
"mismatches": 214,
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"per_case_ms": 53.32
},
{
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"tags": {},
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"sequence_length"
]
}
},
"int8_drift": {
"graph": "onnx/model_quantized.onnx",
"against": "onnx/model.onnx",
"output": "logits",
"max_abs_diff": 0.9604771137237549,
"argmax_agreement": 1.0
},
"stripped_padding": {
"strategy": {
"Fixed": 512
},
"direction": "Right",
"pad_to_multiple_of": null,
"pad_id": 0,
"pad_type_id": 0,
"pad_token": "[PAD]"
}
},
"data": {
"file": "topics.jsonl",
"sha256": "1c9d0192dd4068c70e08feb391c5ed180d4f30c0ca0e02ca6cc8968863c0ab20",
"pin": {
"pin": "925b2b7abe9791679587c54c061d0785626f74df",
"direct": {
"multijail": {
"out": "multijail.jsonl",
"repo": "DAMO-NLP-SG/MultiJail",
"revision": "93d65c778925d55973b9ff30c7bccc8545494eb6",
"path": "MultiJail.csv",
"sha256": "953132d40b8bf562f20bd9db0a9edf6a3564dc7362bd15a8bb649c8afd128ef4",
"license": "MIT"
}
},
"source": "checkout",
"checkout": "7cb21b107f31871027ce70c56f73401cd9b27c50",
"drift": true,
"fetched": "2026-08-31T18:35:26+00:00",
"written": [
"multijail.jsonl"
],
"files": {
"anercorp-test.conll": "9dcc3cf9e9db5a1fe2f4147e0504f57cb144a978006eb64f6f210534cac2ad65",
"aqmar.conll": "5ca489909be68cc79947433a816315c21e4c3c3931d5f0e70393642c6a8fef76",
"deepset-test.jsonl": "133d59690b33c4276d557c4f9e8e4b4f738d1dfe9a914b52c1c0e930511fc9e6",
"gulf-names.jsonl": "4cd99f730ff8056f0147826a723cb96a4965f53bdbd7e69b16a39611d2cb69d3",
"jailbreak-test.jsonl": "b22cc550b36f991748e3479a4c2e07b73060d556ef4e3214283039d4ac92d7b9",
"multijail.jsonl": "352e29aac777ffd1ade5ef9b274a21ebdc624251339a5eb16226d86b96b10e89",
"nemotron-pii-test.jsonl": "ccb748fcee6fbf6705ed0b44db00ff6258081458f179341afbe9a98d1a1423ad",
"safeguard-test.jsonl": "87352eb07c7c3d97cf394de62416a0def1f596f14e42ca3eee01c01217d2455b",
"topics.jsonl": "1c9d0192dd4068c70e08feb391c5ed180d4f30c0ca0e02ca6cc8968863c0ab20",
"wikiann-ar-test.conll": "70f08a00d4c901b8e7ce6174218e5335dcaac0682c92969f705d7b28578fdfc4",
"wojood-sample-test.jsonl": "c7949973e48fa40740b95f2b45a7f046d5359acf7074a443d9e703ef8cad19b7"
}
}
},
"cases": 178,
"fired_total": 15,
"per_case_ms": 8.502,
"crosstab": {
"file": "topics.jsonl",
"split": "jailbreak",
"untagged": 0,
"counts": {
"en": {
"benign": {
"fired": 1,
"total": 81,
"rate": 0.0123
},
"jailbreak": {
"fired": 7,
"total": 8,
"rate": 0.875
}
},
"ar": {
"benign": {
"fired": 1,
"total": 81,
"rate": 0.0123
},
"jailbreak": {
"fired": 6,
"total": 8,
"rate": 0.75
}
}
}
},
"config": {
"guardrails": {
"detectors": [
{
"type": "classify",
"name": "candidate",
"classify": {
"model": "onnx/model.onnx",
"tokenizer": "tokenizer.json",
"labels_path": "config.json",
"tokenizer_library": "libpitok.dylib",
"library": "libonnxruntime.dylib"
}
}
],
"rules": [
{
"name": "note",
"when": "true",
"action": "annotate"
}
]
}
},
"command": [
"pistra",
"guardrails",
"eval",
"-config",
"crosstab-model.yaml",
"-json",
"-parallel",
"6",
"-errors",
"501",
"-kind",
"user",
"-types",
"INJECTION",
"topics.jsonl"
]
}
]
}
Licence
The weights derive from proventra/mdeberta-v3-base-prompt-injection and carry its licence (mit). The recipe that produced this export is under the Business Source License 1.1 of pistra-models.
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026