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library_name: transformers
license: cc-by-4.0
base_model: roberta-base
pipeline_tag: text-classification
language:
- en
datasets:
- CIRCL/vulnerability-attack-techniques
- CIRCL/vulnerability-attack-techniques-llm-scaling
tags:
- security
- vulnerability
- cve
- mitre-attack
- cti
- multi-label-classification
- negative-result
- generated_from_trainer
model-index:
- name: vulnerability-attack-technique-classification-roberta-base-llm-expanded
results:
- task:
type: text-classification
name: Multi-label MITRE ATT&CK technique classification
dataset:
name: CIRCL/vulnerability-attack-techniques
type: CIRCL/vulnerability-attack-techniques
split: test
metrics:
- type: recall
name: Recall@5
value: 0.6156
- type: recall
name: Recall@3
value: 0.5337
- type: f1
name: F1 micro
value: 0.3790
- type: f1
name: F1 macro
value: 0.1480
---
# vulnerability-attack-technique-classification-roberta-base-llm-expanded
**This is a negative-result comparison checkpoint, published for
reproducibility. For applications, use
[CIRCL/vulnerability-attack-technique-classification-roberta-base](https://huggingface.co/CIRCL/vulnerability-attack-technique-classification-roberta-base).**
A multi-label classifier that suggests [MITRE ATT&CK](https://attack.mitre.org/)
(Enterprise) techniques from a free-text vulnerability description. It is
identical to the released gold-only model — same base model
([roberta-base](https://huggingface.co/roberta-base)), same 53-technique
label vocabulary, same seed, same evaluation protocol — except for one
thing: its training set folds 984 additional LLM-labeled CVEs
([CIRCL/vulnerability-attack-techniques-llm-scaling](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques-llm-scaling),
labeled by qwen3.5:122b at ≈0.39 agreement with the expert gold labels)
into the 972 expert-labeled training rows.
The paper
[*Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and
the Limits of LLM-Assisted Label Expansion*](https://arxiv.org/abs/2607.25572)
(arXiv:2607.25572) uses this pair of checkpoints to answer the question "can LLM-assisted
labeling extend a small expert gold set?" — and the answer is **no, not at
this agreement level**: no reliable ranking improvement at any expansion
size from 100 to 984 CVEs, and measurable degradation of rare-technique
coverage at scale.
DOI: [10.57967/hf/9624](https://doi.org/10.57967/hf/9624)
## What this checkpoint shows
Five seeds, corrected protocol (checkpoint selection on the validation
split), identical test split — gold-only vs. this configuration
(gold + 984 LLM rows):
| Metric | Gold-only | Gold + 984 LLM |
|--------|-----------|----------------|
| Recall@5 | **0.673 ± 0.019** | 0.651 ± 0.022 |
| Recall@3 | 0.536 ± 0.032 | 0.534 ± 0.012 |
| F1 micro | 0.410 ± 0.006 | 0.427 ± 0.028 |
| F1 macro | **0.177 ± 0.014** | 0.151 ± 0.014 |
The pattern: the noisy labels concentrate mass on frequent, "obvious"
techniques (micro-F1 up a little) while deflating exactly the
rare-technique coverage the expert labels paid for (macro-F1 down ≈3 SEM,
no recall@5 gain). On CVE-2021-44077, for example, this checkpoint is more
confident than the gold model about T1190 (*Exploit Public-Facing
Application*) but drops the analyst-credited T1505 (*Server Software
Component*) below the prediction threshold and pushes tail techniques such
as T1136 (*Create Account*) from rank 18 to 32. Section 6 of the paper
gives the full account, including why an earlier apparent gain turned out
to be evaluation noise.
## How to use
Same interface as the gold-only model:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_id = "CIRCL/vulnerability-attack-technique-classification-roberta-base-llm-expanded"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()
description = "..." # free-text vulnerability description
inputs = tokenizer(description, truncation=True, max_length=512, return_tensors="pt")
with torch.no_grad():
probs = torch.sigmoid(model(**inputs).logits)[0]
for i in probs.argsort(descending=True)[:5]:
print(f"{model.config.id2label[int(i)]} {probs[i]:.4f}")
```
Or side by side with the released model on a live CVE:
```bash
vulntrain-infer-attack-classification --cve CVE-2021-44077 \
--model CIRCL/vulnerability-attack-technique-classification-roberta-base-llm-expanded
```
## Intended uses & limitations
**Intended**: reproducing and extending the paper's expansion experiments —
e.g. contrasting its per-technique behaviour with the gold-only checkpoint,
or as a baseline for better silver-labeling strategies (higher-agreement
labelers, agreement-weighted losses, human-in-the-loop curation).
**Not intended**: production use. It is strictly dominated by the gold-only
model on ranking and rare-technique metrics, which is why Vulnerability-Lookup
deploys the gold-only checkpoint. All limitations of the gold-only model
(53-technique vocabulary, KEV-skewed data, English only, 512-token
truncation, uncalibrated scores, unverified suggestions) apply here too.
## Training and evaluation data
- **Expert rows**: the 972-row train split of
[CIRCL/vulnerability-attack-techniques](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques)
(MITRE CTID gold mappings).
- **LLM rows (train only)**: 984 CVEs from
[CIRCL/vulnerability-attack-techniques-llm-scaling](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques-llm-scaling),
labeled by qwen3.5:122b (Ollama, assertive single-call prompt following
the CTID methodology) — the best configuration of the paper's labeler
benchmark, at ≈0.39 F1 agreement with held-out expert labels.
- The label vocabulary stays frozen to the gold train split, and the
validation (106) and test (118) splits contain **only** expert-labeled
rows; checkpoint selection uses the validation split.
## Training procedure
Binary cross-entropy over 53 sigmoid outputs with balanced per-label
`pos_weight`, trained with `vulntrain-train-attack-classification`
(VulnTrain), like the gold-only model — only the training set differs
(1,956 rows instead of 972).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 40
- max_length: 512
- loss: BCEWithLogitsLoss, balanced pos_weight
- checkpoint selection: best macro-F1 on the validation split
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Recall Micro | Recall At 3 | Recall At 5 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-----------:|:-----------:|
| 0.7195 | 1.0 | 62 | 0.7652 | 0.2547 | 0.0278 | 0.1821 | 0.4232 | 0.2862 | 0.3870 |
| 0.6487 | 2.0 | 124 | 0.7303 | 0.2628 | 0.0431 | 0.1779 | 0.5021 | 0.2717 | 0.3564 |
| 0.6442 | 3.0 | 186 | 0.7133 | 0.2931 | 0.0528 | 0.2042 | 0.5187 | 0.3741 | 0.4821 |
| 0.6053 | 4.0 | 248 | 0.6903 | 0.3353 | 0.0752 | 0.2597 | 0.4730 | 0.3730 | 0.5167 |
| 0.6037 | 5.0 | 310 | 0.6715 | 0.3392 | 0.0886 | 0.2619 | 0.4813 | 0.4193 | 0.5561 |
| 0.5560 | 6.0 | 372 | 0.6557 | 0.3356 | 0.0970 | 0.2521 | 0.5021 | 0.4002 | 0.5483 |
| 0.5204 | 7.0 | 434 | 0.6496 | 0.3205 | 0.0969 | 0.2319 | 0.5187 | 0.3782 | 0.5023 |
| 0.5067 | 8.0 | 496 | 0.6369 | 0.3470 | 0.1098 | 0.2628 | 0.5104 | 0.4092 | 0.5781 |
| 0.5060 | 9.0 | 558 | 0.6256 | 0.3626 | 0.1195 | 0.2731 | 0.5394 | 0.4548 | 0.6115 |
| 0.4426 | 10.0 | 620 | 0.6207 | 0.3212 | 0.1076 | 0.238 | 0.4938 | 0.4304 | 0.5597 |
| 0.4414 | 11.0 | 682 | 0.6174 | 0.3840 | 0.1213 | 0.3049 | 0.5187 | 0.4700 | 0.6059 |
| 0.4453 | 12.0 | 744 | 0.6184 | 0.3163 | 0.1432 | 0.2217 | 0.5519 | 0.4156 | 0.5636 |
| 0.4328 | 13.0 | 806 | 0.6122 | 0.3351 | 0.1403 | 0.2447 | 0.5311 | 0.4441 | 0.5816 |
| 0.4219 | 14.0 | 868 | 0.6133 | 0.3773 | 0.1543 | 0.2866 | 0.5519 | 0.4642 | 0.6327 |
| 0.4109 | 15.0 | 930 | 0.6075 | 0.3722 | 0.1607 | 0.2720 | 0.5892 | 0.4682 | 0.6197 |
| 0.3905 | 16.0 | 992 | 0.6038 | 0.3778 | 0.1665 | 0.2771 | 0.5934 | 0.4642 | 0.6307 |
| 0.3948 | 17.0 | 1054 | 0.6025 | 0.3781 | 0.1448 | 0.2822 | 0.5726 | 0.4645 | 0.5977 |
| 0.3785 | 18.0 | 1116 | 0.6034 | 0.3845 | 0.1521 | 0.2939 | 0.5560 | 0.4529 | 0.6422 |
| 0.3723 | 19.0 | 1178 | 0.6038 | 0.3810 | 0.1467 | 0.2875 | 0.5643 | 0.4914 | 0.6543 |
| 0.3580 | 20.0 | 1240 | 0.6036 | 0.3790 | 0.1504 | 0.2842 | 0.5685 | 0.4524 | 0.6257 |
| 0.3343 | 21.0 | 1302 | 0.5993 | 0.3878 | 0.1522 | 0.2978 | 0.5560 | 0.5228 | 0.6688 |
| 0.3386 | 22.0 | 1364 | 0.6000 | 0.3994 | 0.1501 | 0.3103 | 0.5602 | 0.4819 | 0.6740 |
| 0.3444 | 23.0 | 1426 | 0.5977 | 0.3977 | 0.1586 | 0.3013 | 0.5851 | 0.4945 | 0.6787 |
| 0.3303 | 24.0 | 1488 | 0.6003 | 0.3988 | 0.1571 | 0.3072 | 0.5685 | 0.4862 | 0.6594 |
| 0.3206 | 25.0 | 1550 | 0.6044 | 0.4 | 0.1574 | 0.3124 | 0.5560 | 0.4792 | 0.6825 |
| 0.3180 | 26.0 | 1612 | 0.6080 | 0.4031 | 0.1515 | 0.3188 | 0.5477 | 0.5008 | 0.6744 |
| 0.3100 | 27.0 | 1674 | 0.6085 | 0.4037 | 0.1500 | 0.3211 | 0.5436 | 0.5197 | 0.6289 |
| 0.3075 | 28.0 | 1736 | 0.6061 | 0.4071 | 0.1622 | 0.3158 | 0.5726 | 0.4953 | 0.6656 |
| 0.3042 | 29.0 | 1798 | 0.6135 | 0.3982 | 0.1550 | 0.3155 | 0.5394 | 0.4961 | 0.6722 |
| 0.3044 | 30.0 | 1860 | 0.6097 | 0.4 | 0.1579 | 0.3178 | 0.5394 | 0.4874 | 0.6751 |
| 0.3032 | 31.0 | 1922 | 0.6028 | 0.3875 | 0.1588 | 0.2950 | 0.5643 | 0.4796 | 0.6509 |
| 0.2868 | 32.0 | 1984 | 0.6083 | 0.3994 | 0.1522 | 0.3157 | 0.5436 | 0.5063 | 0.6869 |
| 0.2821 | 33.0 | 2046 | 0.6093 | 0.4018 | 0.1535 | 0.3187 | 0.5436 | 0.4800 | 0.6727 |
| 0.2915 | 34.0 | 2108 | 0.6044 | 0.3982 | 0.1528 | 0.3115 | 0.5519 | 0.4972 | 0.6609 |
| 0.2829 | 35.0 | 2170 | 0.6112 | 0.3988 | 0.1545 | 0.3122 | 0.5519 | 0.4952 | 0.6869 |
| 0.2915 | 36.0 | 2232 | 0.6111 | 0.4062 | 0.1510 | 0.3227 | 0.5477 | 0.5079 | 0.6853 |
| 0.2781 | 37.0 | 2294 | 0.6153 | 0.4 | 0.1507 | 0.3208 | 0.5311 | 0.5020 | 0.6778 |
| 0.2724 | 38.0 | 2356 | 0.6115 | 0.4031 | 0.1509 | 0.3203 | 0.5436 | 0.4972 | 0.6778 |
| 0.2794 | 39.0 | 2418 | 0.6142 | 0.3975 | 0.1468 | 0.3176 | 0.5311 | 0.4984 | 0.6801 |
| 0.2661 | 40.0 | 2480 | 0.6123 | 0.4025 | 0.1516 | 0.3195 | 0.5436 | 0.4972 | 0.6825 |
### Framework versions
- Transformers 5.13.0
- Pytorch 2.12.1+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
## Related artifacts
| Artifact | Location | DOI |
|----------|----------|-----|
| **Released model (use this one)** | [CIRCL/vulnerability-attack-technique-classification-roberta-base](https://huggingface.co/CIRCL/vulnerability-attack-technique-classification-roberta-base) | [10.57967/hf/9623](https://doi.org/10.57967/hf/9623) |
| Gold dataset (1,207 CVEs, CTID-curated labels) | [CIRCL/vulnerability-attack-techniques](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques) | [10.57967/hf/9621](https://doi.org/10.57967/hf/9621) |
| LLM expansion dataset (984 LLM-labeled CVEs) | [CIRCL/vulnerability-attack-techniques-llm-scaling](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques-llm-scaling) | [10.57967/hf/9622](https://doi.org/10.57967/hf/9622) |
| Code | [vulnerability-lookup/VulnTrain](https://github.com/vulnerability-lookup/VulnTrain) | — |
| Paper | [arXiv:2607.25572](https://arxiv.org/abs/2607.25572) | — |
| Paper LaTeX source + trainer logs | [vulnerability-lookup/cve-attack-mapping-paper](https://github.com/vulnerability-lookup/cve-attack-mapping-paper) | — |
## Citation
```bibtex
@misc{bonhomme2026mappingcvesmitreattck,
title={Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion},
author={Cédric Bonhomme and Alexandre Dulaunoy},
year={2026},
eprint={2607.25572},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2607.25572},
}
```
## Acknowledgements
Developed at [CIRCL](https://www.circl.lu) in the context of the
[AIPITCH](https://www.science.nask.pl/en/research-areas/projects/12456)
project, co-funded by the European Union.
|