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CIRCL/
vulnerability-attack-technique-classification-roberta-base

Name: vulnerability-attack-technique-classification-roberta-base
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vulnerability-attack-technique-classification-roberta-base

Suggests MITRE ATT&CK (Enterprise) techniques from a free-text vulnerability description. This is a multi-label classifier (roberta-base with a sigmoid head, one output per technique) fine-tuned on CIRCL/vulnerability-attack-techniques, a gold dataset of 1,207 CVEs whose labels come from expert MITRE CTID mappings. Given a CVE description it scores 53 parent techniques (e.g. T1190 Exploit Public-Facing Application, T1505 Server Software Component), for use as a ranked list of candidate techniques for analyst review.

The model is trained with VulnTrain and runs in production on the public Vulnerability-Lookup instance operated by CIRCL, served locally by ML-Gateway: every vulnerability page has an ATT&CK tab with the model's suggestions (example: CVE-2021-44077).

The methodology, evaluation protocol, and the negative result on LLM-assisted label expansion are described in the paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion (arXiv:2607.25572).

DOI: 10.57967/hf/9623

How to use

With VulnTrain, against a live CVE:

bash

vulntrain-infer-attack-classification --cve CVE-2021-44077 \
    --model CIRCL/vulnerability-attack-technique-classification-roberta-base

With plain Transformers:

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "CIRCL/vulnerability-attack-technique-classification-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

description = (
    "Zoho ManageEngine ServiceDesk Plus before 11306, ServiceDesk Plus MSP "
    "before 10530, and SupportCenter Plus before 11014 are vulnerable to "
    "unauthenticated remote code execution."
)
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}")

Technique IDs map to names via the ATT&CK Enterprise matrix; sigmoid >= 0.5 is the prediction threshold used in evaluation, but the model is most useful as a ranker (see the recall@k numbers below).

Intended uses & limitations

Intended: triage assistance — given a vulnerability description, surface a short ranked list of candidate ATT&CK techniques for a human analyst to confirm or reject. This is how it is deployed in Vulnerability-Lookup, where the UI explicitly flags the output as unverified AI-generated suggestions.

Limitations:

The label space is the 53 parent techniques with at least 5 training examples; sub-techniques are collapsed to their parent, and techniques outside this vocabulary can never be suggested.
The gold set skews toward exploited-in-the-wild CVEs (CTID's corpus and the KEV catalog), so coverage is best for the techniques common there.
Trained on 972 CVEs — deliberately label-quality-bound rather than data-bound (see the paper's gold-size scaling curve: every metric still improves monotonically with more curated rows).
English descriptions only; input is truncated at 512 tokens.
Scores are not calibrated probabilities.
Suggestions are not verified mappings; treat them as guidance, never as authoritative CTI.

Training and evaluation data

Labels come from the two public expert sources of CVE→ATT&CK mappings, both following the CTID Mapping ATT&CK to CVE for Impact methodology: the CTID attack_to_cve mappings and the CTID Mappings Explorer KEV mappings (ATT&CK 16.1). Technique IDs revoked since publication are remapped to their successors via the ATT&CK STIX revoked-by relationships. Descriptions are joined from CIRCL/vulnerability-scores (Vulnerability-Lookup). Full details in the dataset card and the VulnTrain documentation.

Splits: 972 train / 106 validation / 118 test examples. Checkpoint selection uses the validation split; the test split is touched once, for the numbers reported here. The paper documents why this matters: selecting the best checkpoint on the test split inflates and destabilizes small-test-split metrics enough to produce spurious conclusions.

Notably, this model was not trained on LLM-generated labels: the paper's controlled experiments show that folding in LLM-labeled CVEs at ≈0.39 agreement with the experts yields no reliable ranking improvement and measurably degrades rare-technique coverage at scale (the comparison checkpoint is published as ...-llm-expanded).

Evaluation

Held-out test split, this checkpoint (seed 42):

Metric	Value
Recall@5	0.6440
Recall@3	0.5181
F1 micro	0.3899
F1 macro	0.1910
Precision micro	0.2740
Recall micro	0.6756

Recall@5 = 0.64 means that on average 64% of an unseen CVE's expert-assigned techniques appear in the model's top five suggestions — roughly double the zero-shot embedding-similarity baseline reported in the paper. Across five seeds under the identical protocol, the numbers of record are recall@5 0.673 ± 0.019, recall@3 0.536 ± 0.032, micro-F1 0.410 ± 0.006, macro-F1 0.177 ± 0.014. The complete trainer logs are published in the paper repository.

Training procedure

Binary cross-entropy over 53 sigmoid outputs, with per-label pos_weight balancing (capped at 20) to keep rare techniques trainable. Trained with vulntrain-train-attack-classification (VulnTrain).

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 (min 2.447, max 20.0)
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
0.9311	1.0	31	0.8413	0.1833	0.0401	0.1258	0.3376	0.1798	0.2611
0.8366	2.0	62	0.7876	0.1452	0.0234	0.1065	0.2278	0.1047	0.2086
0.8116	3.0	93	0.7717	0.2054	0.0518	0.1641	0.2743	0.1938	0.3234
0.7941	4.0	124	0.7576	0.3204	0.0804	0.2382	0.4895	0.3387	0.5009
0.7743	5.0	155	0.7435	0.3044	0.0841	0.2119	0.5401	0.3313	0.4696
0.7645	6.0	186	0.7290	0.3208	0.0906	0.2174	0.6118	0.3969	0.5391
0.7472	7.0	217	0.7163	0.3551	0.1130	0.2571	0.5738	0.4068	0.5741
0.7219	8.0	248	0.7056	0.3224	0.1079	0.2173	0.6245	0.4079	0.5521
0.7195	9.0	279	0.6933	0.3576	0.1495	0.2449	0.6624	0.4252	0.5663
0.6835	10.0	310	0.6845	0.3705	0.1665	0.2579	0.6582	0.4708	0.6090
0.6539	11.0	341	0.6768	0.4063	0.1810	0.2947	0.6540	0.5227	0.6318
0.6484	12.0	372	0.6725	0.3632	0.1734	0.2520	0.6498	0.4449	0.6200
0.6249	13.0	403	0.6664	0.3974	0.1782	0.2862	0.6498	0.5034	0.6396
0.6109	14.0	434	0.6585	0.3801	0.1721	0.2724	0.6287	0.4834	0.6491
0.6004	15.0	465	0.6539	0.3872	0.1678	0.2781	0.6371	0.4752	0.6347
0.5896	16.0	496	0.6502	0.4049	0.1777	0.2996	0.6245	0.4768	0.6397
0.5667	17.0	527	0.6478	0.3866	0.1682	0.2737	0.6582	0.4941	0.6472
0.5661	18.0	558	0.6425	0.4108	0.1910	0.3022	0.6414	0.5128	0.6667
0.5501	19.0	589	0.6394	0.3880	0.1861	0.2758	0.6540	0.4822	0.6561
0.5461	20.0	620	0.6377	0.4097	0.1804	0.3010	0.6414	0.5069	0.6687
0.5351	21.0	651	0.6338	0.4028	0.1715	0.3002	0.6118	0.4987	0.6624
0.5215	22.0	682	0.6351	0.4146	0.1964	0.3054	0.6456	0.4943	0.6875
0.5155	23.0	713	0.6315	0.4056	0.1737	0.3023	0.6160	0.4994	0.6553
0.5063	24.0	744	0.6269	0.4286	0.1879	0.3208	0.6456	0.5195	0.6923
0.5061	25.0	775	0.6264	0.4178	0.1869	0.3069	0.6540	0.5246	0.6656
0.4996	26.0	806	0.6301	0.4073	0.1792	0.3053	0.6118	0.5274	0.6958
0.4950	27.0	837	0.6225	0.4133	0.1771	0.3079	0.6287	0.5376	0.6593
0.4928	28.0	868	0.6228	0.4173	0.1768	0.3166	0.6118	0.5187	0.7048
0.4819	29.0	899	0.6242	0.4263	0.1871	0.3225	0.6287	0.5494	0.6970
0.4766	30.0	930	0.6194	0.4166	0.1862	0.3094	0.6371	0.5226	0.6871
0.4694	31.0	961	0.6213	0.42	0.1914	0.3175	0.6203	0.5399	0.6918
0.4781	32.0	992	0.6209	0.4292	0.1893	0.3281	0.6203	0.5439	0.7060
0.4636	33.0	1023	0.6218	0.4347	0.1962	0.3276	0.6456	0.525	0.6797
0.4641	34.0	1054	0.6216	0.4314	0.1887	0.3261	0.6371	0.5415	0.6797
0.4592	35.0	1085	0.6206	0.4313	0.1916	0.3282	0.6287	0.5466	0.6858
0.4526	36.0	1116	0.6208	0.4357	0.1926	0.3333	0.6287	0.5447	0.6863
0.4643	37.0	1147	0.6191	0.4218	0.1899	0.3196	0.6203	0.5392	0.6863
0.4501	38.0	1178	0.6191	0.4242	0.1885	0.3224	0.6203	0.5368	0.6929
0.4570	39.0	1209	0.6206	0.4350	0.1932	0.3326	0.6287	0.5281	0.6910
0.4436	40.0	1240	0.6199	0.4325	0.1916	0.3296	0.6287	0.5329	0.6882

Framework versions

Transformers 5.13.0
Pytorch 2.12.1+cu130
Datasets 4.8.5
Tokenizers 0.22.2

Related artifacts

Artifact	Location	DOI
Gold dataset (1,207 CVEs, CTID-curated labels)	CIRCL/vulnerability-attack-techniques	10.57967/hf/9621
LLM expansion dataset (negative result)	CIRCL/vulnerability-attack-techniques-llm-scaling	10.57967/hf/9622
LLM-expanded comparison model	CIRCL/vulnerability-attack-technique-classification-roberta-base-llm-expanded	10.57967/hf/9624
Code	vulnerability-lookup/VulnTrain	—
Paper	arXiv:2607.25572	—
Paper LaTeX source + trainer logs	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 in the context of the AIPITCH project, co-funded by the European Union.