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README.md +58 -159
config.json +100 -108
emissions.csv +1 -1
metrics.json +10 -10
model.safetensors +2 -2
tokenizer.json +1 -6
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -2,131 +2,39 @@
 library_name: transformers
 license: mit
 base_model: roberta-base
-pipeline_tag: text-classification
-language:
-- en
-datasets:
-- CIRCL/vulnerability-attack-techniques
 tags:
-- vulnerability
-- cybersecurity
-- security
-- cve
-- mitre-attack
-- attack-techniques
 - generated_from_trainer
 model-index:
 - name: vulnerability-attack-technique-classification-roberta-base
   results: []
 ---
 # vulnerability-attack-technique-classification-roberta-base
-This model suggests **MITRE ATT&CK (Enterprise) techniques** from a vulnerability
-description. It is a fine-tuned version of
-[roberta-base](https://huggingface.co/roberta-base) trained on
-[CIRCL/vulnerability-attack-techniques](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques),
-a dataset of ~1,200 CVEs with analyst-curated technique mappings from the
-[MITRE Center for Threat-Informed Defense (CTID)](https://ctid.mitre.org/) projects,
-following the "Mapping ATT&CK to CVE for Impact" methodology.
-CVSS tells you *how bad* a vulnerability is, CWE *what kind of flaw* it is —
-ATT&CK tells defenders *what adversary behavior to expect and detect*.
-This is a **multi-label** classifier (sigmoid head, binary cross-entropy with
-per-label positive weights): a CVE legitimately maps to several techniques —
-an exploitation technique (e.g. T1190 *Exploit Public-Facing Application*)
-plus one or more impacts (e.g. T1059 *Command and Scripting Interpreter*).
 ## Intended uses & limitations
-The model **suggests candidate techniques for analyst review**. It must not be
-treated as an authoritative mapping: a CVE description describes a flaw, while
-ATT&CK describes attacker behavior around it, and even human annotators
-disagree on such mappings. Use the top-k suggestions as a triage aid.
-- **Label space**: 57 parent techniques (sub-techniques collapsed, only
-  techniques with ≥ 5 training examples), enterprise ATT&CK v19.1. Techniques
-  outside this vocabulary can never be suggested.
-- **Selection bias**: the training data over-represents exploited-in-the-wild
-  vulnerabilities (a large part comes from CISA KEV mappings).
-- **Small training set** (~1,100 examples): rare-technique performance is
-  weak, as the macro-F1 shows.
-## Evaluation
-Evaluated with `vulntrain-validate-attack-classification` on the dataset's
-test split, against the zero-shot SMET-style baseline (rank techniques by
-cosine similarity between the description embedding —
-`all-MiniLM-L6-v2` — and the official ATT&CK technique descriptions), using
-the same label vocabulary and metrics:
-| Metric | Zero-shot similarity baseline | This model |
-|--------|------------------------------|------------|
-| recall@1 | 0.118 | **0.220** |
-| recall@3 | 0.257 | **0.482** |
-| recall@5 | 0.322 | **0.686** |
-| recall@10 | 0.491 | **0.842** |
-| MRR | 0.397 | **0.620** |
-At the 0.5 threshold: F1 micro 0.417, F1 macro 0.203, precision micro 0.301,
-recall micro 0.682. The balanced positive weights deliberately favor recall;
-for the suggestion use case, ranking metrics (recall@k) are the ones that
-matter — in ~69% of cases the correct techniques appear among the top 5
-suggestions, out of 57 candidates.
-## Usage
-```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)
-description = (
-    "A Missing Authentication for Critical Function vulnerability in J-Web "
-    "allows an unauthenticated, network-based attacker to upload and "
-    "download arbitrary files and execute commands."
-)
-inputs = tokenizer(description, return_tensors="pt", truncation=True)
-with torch.no_grad():
-    probabilities = torch.sigmoid(model(**inputs).logits)[0]
-top = torch.topk(probabilities, k=5)
-for probability, index in zip(top.values, top.indices):
-    print(f"{model.config.id2label[index.item()]}: {probability:.2f}")
-```
 ## Training and evaluation data
-Trained with [VulnTrain](https://github.com/vulnerability-lookup/VulnTrain):
-```bash
-vulntrain-train-attack-classification \
-  --base-model roberta-base \
-  --repo-id CIRCL/vulnerability-attack-technique-classification-roberta-base
-```
-The dataset provenance (why the labels come from the hand-curated CTID
-mappings rather than automatically derived CVE→CWE→CAPEC→ATT&CK chains) is
-documented in the
-[methodology page](https://github.com/vulnerability-lookup/VulnTrain/blob/main/docs/attack-techniques-dataset.md)
-and on the [dataset card](https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques).
-## Next steps
-- **LLM-assisted label expansion**: grow the training set beyond ~1,200
-  examples by labeling a CWE-stratified CVE sample with the CTID methodology,
-  validated against the analyst gold set before use. This targets the main
-  weakness (rare-technique recall / macro-F1).
-- Stronger encoders (e.g. security-domain sentence models) as both baseline
-  and base model.
-- Sub-technique-level labels once the per-label support allows it
-  (`--keep-subtechniques`).
-- Integration into [Vulnerability-Lookup](https://vulnerability.circl.lu) via
-  [ML-Gateway](https://github.com/vulnerability-lookup/ML-Gateway).
 ## Training procedure
@@ -134,59 +42,57 @@ and on the [dataset card](https://huggingface.co/datasets/CIRCL/vulnerability-at
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
-- train_batch_size: 64
-- eval_batch_size: 64
 - 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
-The best checkpoint was selected on validation macro-F1 (epoch 33).
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Recall Micro | Recall At 3 | Recall At 5 |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-----------:|:-----------:|
-| No log        | 1.0   | 17   | 0.9141          | 0.1161   | 0.0311   | 0.0672          | 0.4280       | 0.2637      | 0.2959      |
-| 0.9279        | 2.0   | 34   | 0.8081          | 0.2057   | 0.0305   | 0.1473          | 0.3409       | 0.3201      | 0.3587      |
-| 0.8472        | 3.0   | 51   | 0.7795          | 0.2369   | 0.0429   | 0.1649          | 0.4205       | 0.3212      | 0.3859      |
-| 0.7976        | 4.0   | 68   | 0.7677          | 0.2418   | 0.0375   | 0.1752          | 0.3902       | 0.3415      | 0.3950      |
-| 0.7776        | 5.0   | 85   | 0.7555          | 0.2945   | 0.0616   | 0.2137          | 0.4735       | 0.3449      | 0.4593      |
-| 0.7742        | 6.0   | 102  | 0.7483          | 0.2943   | 0.0702   | 0.2085          | 0.5          | 0.3530      | 0.4845      |
-| 0.7742        | 7.0   | 119  | 0.7393          | 0.3159   | 0.0800   | 0.2199          | 0.5606       | 0.3599      | 0.5048      |
-| 0.7523        | 8.0   | 136  | 0.7281          | 0.3246   | 0.0992   | 0.2212          | 0.6098       | 0.3553      | 0.5116      |
-| 0.7418        | 9.0   | 153  | 0.7172          | 0.336    | 0.1068   | 0.2283          | 0.6364       | 0.4342      | 0.5668      |
-| 0.7328        | 10.0  | 170  | 0.7078          | 0.3431   | 0.1230   | 0.2338          | 0.6439       | 0.4063      | 0.5864      |
-| 0.7166        | 11.0  | 187  | 0.6942          | 0.3546   | 0.1306   | 0.2420          | 0.6629       | 0.4405      | 0.6021      |
-| 0.6951        | 12.0  | 204  | 0.6858          | 0.3746   | 0.1355   | 0.2635          | 0.6477       | 0.4650      | 0.6256      |
-| 0.6778        | 13.0  | 221  | 0.6750          | 0.3603   | 0.1339   | 0.2468          | 0.6667       | 0.4252      | 0.6116      |
-| 0.6778        | 14.0  | 238  | 0.6698          | 0.3661   | 0.1390   | 0.2529          | 0.6629       | 0.4311      | 0.6200      |
-| 0.6692        | 15.0  | 255  | 0.6623          | 0.3845   | 0.1412   | 0.2684          | 0.6780       | 0.4805      | 0.6796      |
-| 0.6465        | 16.0  | 272  | 0.6591          | 0.3700   | 0.1510   | 0.2539          | 0.6818       | 0.4287      | 0.6547      |
-| 0.6499        | 17.0  | 289  | 0.6528          | 0.4036   | 0.1567   | 0.2859          | 0.6856       | 0.4577      | 0.6653      |
-| 0.6229        | 18.0  | 306  | 0.6505          | 0.4088   | 0.1634   | 0.2886          | 0.7008       | 0.4962      | 0.6785      |
-| 0.6214        | 19.0  | 323  | 0.6489          | 0.3913   | 0.1485   | 0.2803          | 0.6477       | 0.4661      | 0.6453      |
-| 0.6092        | 20.0  | 340  | 0.6484          | 0.3825   | 0.1475   | 0.2688          | 0.6629       | 0.4577      | 0.6627      |
-| 0.6092        | 21.0  | 357  | 0.6409          | 0.4118   | 0.1652   | 0.2935          | 0.6894       | 0.5011      | 0.6852      |
-| 0.5973        | 22.0  | 374  | 0.6419          | 0.3982   | 0.1585   | 0.2812          | 0.6818       | 0.4647      | 0.6502      |
-| 0.6046        | 23.0  | 391  | 0.6368          | 0.4095   | 0.1664   | 0.2919          | 0.6856       | 0.4864      | 0.6670      |
-| 0.5811        | 24.0  | 408  | 0.6395          | 0.3895   | 0.1560   | 0.2785          | 0.6477       | 0.4556      | 0.6572      |
-| 0.5741        | 25.0  | 425  | 0.6310          | 0.4241   | 0.1740   | 0.3110          | 0.6667       | 0.5179      | 0.6775      |
-| 0.5697        | 26.0  | 442  | 0.6309          | 0.4014   | 0.1657   | 0.2864          | 0.6705       | 0.4815      | 0.6642      |
-| 0.5697        | 27.0  | 459  | 0.6292          | 0.4151   | 0.1680   | 0.3014          | 0.6667       | 0.4934      | 0.6880      |
-| 0.5656        | 28.0  | 476  | 0.6289          | 0.4      | 0.1616   | 0.2901          | 0.6439       | 0.4579      | 0.6607      |
-| 0.5540        | 29.0  | 493  | 0.6257          | 0.4009   | 0.1798   | 0.2874          | 0.6629       | 0.4710      | 0.6565      |
-| 0.5506        | 30.0  | 510  | 0.6227          | 0.4110   | 0.1809   | 0.2941          | 0.6818       | 0.4815      | 0.6873      |
-| 0.5547        | 31.0  | 527  | 0.6242          | 0.4164   | 0.1850   | 0.3012          | 0.6742       | 0.4773      | 0.6747      |
-| 0.5395        | 32.0  | 544  | 0.6228          | 0.4182   | 0.1844   | 0.3024          | 0.6780       | 0.4661      | 0.6754      |
-| 0.5407        | 33.0  | 561  | 0.6205          | 0.4171   | 0.2027   | 0.3005          | 0.6818       | 0.4822      | 0.6859      |
-| 0.5407        | 34.0  | 578  | 0.6187          | 0.4218   | 0.2007   | 0.3038          | 0.6894       | 0.5274      | 0.6971      |
-| 0.5368        | 35.0  | 595  | 0.6192          | 0.4162   | 0.2018   | 0.2995          | 0.6818       | 0.5025      | 0.6831      |
-| 0.5342        | 36.0  | 612  | 0.6192          | 0.4176   | 0.1833   | 0.3010          | 0.6818       | 0.5134      | 0.6936      |
-| 0.5339        | 37.0  | 629  | 0.6205          | 0.4126   | 0.1832   | 0.2980          | 0.6705       | 0.4775      | 0.6859      |
-| 0.5254        | 38.0  | 646  | 0.6198          | 0.4175   | 0.1851   | 0.3031          | 0.6705       | 0.4817      | 0.6901      |
-| 0.5318        | 39.0  | 663  | 0.6193          | 0.4159   | 0.1846   | 0.3007          | 0.6742       | 0.4859      | 0.6901      |
-| 0.5279        | 40.0  | 680  | 0.6191          | 0.4159   | 0.1843   | 0.3007          | 0.6742       | 0.4831      | 0.6859      |
 ### Framework versions
@@ -195,10 +101,3 @@ The best checkpoint was selected on validation macro-F1 (epoch 33).
 - Pytorch 2.12.1+cu130
 - Datasets 4.8.5
 - Tokenizers 0.22.2
-## References
-- [Vulnerability-Lookup](https://vulnerability.circl.lu) — the vulnerability data source
-- [VulnTrain](https://github.com/vulnerability-lookup/VulnTrain) — training pipeline
-- [MITRE CTID attack_to_cve](https://github.com/center-for-threat-informed-defense/attack_to_cve) and [Mappings Explorer](https://center-for-threat-informed-defense.github.io/mappings-explorer/) — label sources
-- MITRE ATT&CK® is a registered trademark of The MITRE Corporation; content used per the [terms of use](https://attack.mitre.org/resources/legal-and-branding/terms-of-use/)

 library_name: transformers
 license: mit
 base_model: roberta-base
 tags:
 - generated_from_trainer
 model-index:
 - name: vulnerability-attack-technique-classification-roberta-base
   results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 # vulnerability-attack-technique-classification-roberta-base
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6410
+- F1 Micro: 0.3899
+- F1 Macro: 0.1910
+- Precision Micro: 0.2740
+- Recall Micro: 0.6756
+- Recall At 3: 0.5181
+- Recall At 5: 0.6440
+## Model description
+More information needed
 ## Intended uses & limitations
+More information needed
 ## Training and evaluation data
+More information needed
 ## Training procedure
 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
 ### 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
 - Pytorch 2.12.1+cu130
 - Datasets 4.8.5
 - Tokenizers 0.22.2

config.json CHANGED Viewed

@@ -15,60 +15,56 @@
     "0": "T1003",
     "1": "T1005",
     "2": "T1021",
-    "3": "T1027",
-    "4": "T1036",
-    "5": "T1040",
-    "6": "T1041",
-    "7": "T1046",
-    "8": "T1055",
-    "9": "T1059",
-    "10": "T1068",
-    "11": "T1070",
-    "12": "T1071",
-    "13": "T1078",
-    "14": "T1082",
-    "15": "T1083",
-    "16": "T1087",
-    "17": "T1091",
-    "18": "T1098",
-    "19": "T1105",
-    "20": "T1106",
-    "21": "T1110",
-    "22": "T1114",
-    "23": "T1133",
-    "24": "T1136",
-    "25": "T1185",
-    "26": "T1189",
-    "27": "T1190",
-    "28": "T1202",
-    "29": "T1203",
-    "30": "T1204",
-    "31": "T1210",
-    "32": "T1211",
-    "33": "T1212",
-    "34": "T1485",
-    "35": "T1486",
-    "36": "T1489",
-    "37": "T1496",
-    "38": "T1497",
-    "39": "T1498",
-    "40": "T1499",
-    "41": "T1505",
-    "42": "T1528",
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